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2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
2 episodes with fernando perez
Fresh off the recap series on the MLB Blue Jays, Justin and Patrick return to analyze the minor league affiliates. Starting with the Dunedin Blue Jays the guys discuss Eliander Alcalde, Arjun Nimmala, and Victor Arias. Just makes a bold prediction about Sean Keyes and Patrick wonders if Fernando Perez deserves a taste of High-A ball in Vancouver.
Send us a Text Message.DRAGG has been releasing music for around 12/13 years now; a brand of sparse, West Coast-y hip hop, tinged with RnB and soul, all the time showing a progressive approach and high degree of emotional intelligence in the subject matter and how to connect with his audience. His story is remarkable, not least because he lost his sight early in his life…but he has navigated the many obstacles he has faced with persistence and resilience, wearing his heart on his sleeve as the saying goes. He's made some really telling statements on some of the real challenges of being an artist in 2024. And he's done it in front of camera as well for the hellscape that is social media, which always gives me a feeling of awe! His latest 8 track album, Mixed Feelings, came out in April 2024 and it is another sonic progression and features a rich bunch of collaborators – for no particular reason, I'm just gonna mention Fernando Perez's delicious -I think Spanish - guitar on the album's wonderful closer Blue Dreams which ends with “If you love something, let it go….but what if it doesn't come back?”https://www.iwannajumplikedeedee.comI Wanna Jump Like Dee Dee is the music podcast that does music interviews differently. Giles Sibbald talks to musicians, DJ's and producers about how they use an experimental mindset in every part of their lives.- brought to you from the mothership of the experimental mindset™- swirl logo and art by Giles Sibbald - doodle logo and art by Tide Adesanya, Coppie and Paste
Also available on Spotify, Apple Podcasts and other major podcast platforms linktr.ee/guitararrangers Hosted by (ig and youtube) @guitar arrangers @themusicthief7 @donitalia Guest @tolgahancogulu (instagram) @microtonalguitar (youtube) The first prize winner at Georgia Tech Margaret Guthman Musical Instrument Competition in 2014, Tolgahan Çoğulu, designed his "Adjustable Microtonal Guitar" in 2008. His first CD with microtonal guitar, Atlas, was published by Kalan Music in 2012. His microtonal guitar has taken him to many festivals and universities in 34 countries. Tolgahan is building a repertoire for microtonal guitar with more than 40 composers involved at this point. He became an Associate Professor in 2013 and Professor in 2019 at Istanbul Technical University Turkish Music State Conservatory, where he had founded the classical guitar department in 2010 and the world's first microtonal guitar department in 2014. His second CD ‘Microtonal Guitar Duo' has been published by Kalan Music in June 2015. His recording of William Allaudin Mathieu's three-movement ‘Lattice-İşi' in just intonation was published in the US by Cold Mountain Music in 2016. Tolgahan completed a research project entitled ‘Historical Tunings on the Microtonal Guitar' at the University of Bristol in 2016 for 12 months. Between 2015-2017 he collaborated with the University of Music Würzburg for the project ‘Creating and Presenting a Repertoire for the Microtonal Guitar' supported by DAAD in which 12 composers wrote pieces for microtonal guitar and ensemble. He has been organising the world's first “Microtonal Guitar Competition” since 2016 His book ‘Microtonal Guitar' was published in 2018 by Mojo Roots Music, co-authored by Fernando Perez. His latest album ‘Microtonal' was published in 2018 by Ahenk Music. Currently, he is teaching at Istanbul Technical University Turkish Music State Conservatory and Centre for Advanced Studies in Music.
Watch the Screenplay Reading: https://www.youtube.com/watch?v=EBx4Pzw5KvM The world's greatest romance novelist has given up on love due to long-standing grief, but will his blossoming relationship with a younger man lead to a new chapter or the same tragic ending. Get to know the writers: Our screenplay is hilarious and emotionally charged. It combines the classic romantic comedy elements with more serious themes related to AIDS and grief. And certainly more diverse stories from more diverse voices are needed. You can sign up for the 7 day free trial at www.wildsound.ca (available on your streaming services and APPS). There is a DAILY film festival to watch, plus a selection of award winning films on the platform. Then it's only $3.99 per month. Subscribe to the podcast: https://twitter.com/wildsoundpod https://www.instagram.com/wildsoundpod/ https://www.facebook.com/wildsoundpod
La Taberna es el podcast de la música con raíz desde Aragón y para todo el planeta. Llegamos al episodio 969, un repaso por las novedades musicales en la música de raíz. Escuchamos y hablamos del Festival Folk Arrabal que abrirá las fiestas del barrio del arrabal en Zaragoza y donde se realizara un homenaje a Fernando Perez, por el escenario pasaran Gaiteros del Arrabal, Nuei, Vinoman y Cachirulos XL. Si quieres que hablemos de tu grupo, escríbenos a infopodcastaragon@gmail.com Suscribete a nuestros episodios y no te pierdas ninguno Envíanos tus notas de voz a 654 93 42 41 Autor del programa: Francho Martinez Visita nuestros portales https://podcastaragon.es/ y https://musicaypalabras.es/
Host Chaz Wolfe interviews Fernando Perez, co-owner of F&J Services LLC and owner of Flip It KC. Fernando talks about his journey of immigrating to the US from Venezuela and how he saw an opportunity in the Kansas City landscaping industry to provide better quality service. He shares his experience of being wise with his profits and reinvesting back into his business, and Chaz also emphasizes the importance of getting an asset that pays for the things you want.Fernando started his business in 2017 after working for a landscaper and studying construction management in school. He initially focused on landscape and small renovation projects, but as he acquired more clients, he started taking on interior and exterior remodeling projects. He began specializing in patios and hardscape with residential and commercial clients. In April 2022, Fernando started his second company, which renovates houses and sells them. Throughout the episode, Fernando shares his strengths and hot topics, including finding people who can teach and lead you when starting a business, establishing a plan, and putting yourself out there. Listeners can learn more about F&J Services LLC and Flip It KC by visiting their website. Tune in now to learn from Fernando's story! During this episode, you will learn about;[01:50] Introduction to Fernando and his businesses[02:42] Fernando's Why [05:53] How Fernando became a business owner[09:37] A good decision Fernando made in his business[11:23] Living below your means as a business owner[15:43] A bad decision Fernando made in his business[19:10] Fernando's decision making process[21:58] Fernando's #1 KPI[26:57] Fernando's approach and thoughts around networking[31:34] What advice Fernando would give his past self?[32:48] Abundance mindset as a business owner[34:27] How to connect with Fernando[35:25] Information on Gathering The Kings Mastermind Roundtable Notable Quotes"Business is business, and friendship or family should be kept separate. We need to handle it that way." - Fernando Perez"When you have a good understanding of your profits, you can track your business goals and measure your percentage of success. This way, you can set targets for your business and achieve them." - Fernando Perez"Instead of trying to do everything, you should focus on your actual role in the company. This will help you take your business to the next level." - Fernando Perez"In this new era of instant digital communication, you can't afford to be the guy who doesn't do anything or doesn't know what people use. You have to go out, show yourself, let people know what you're doing and why you're the best at it." - Fernando Perez"To be successful in your business, you have to be well personally. You have to be well in your business." - Fernando Perez"We want people to know that we are good people who don't cut corners and offer the best quality." - Fernando Perez"Instead of just paying for the thing that you want, it's better to get an asset that can pay for it. That way, you can invest wisely." - Chaz WolfeBooks and Resources Recommended:The E-Myth Contractor: Why Most Contractors' Businesses Don't Work and What to Do About It by Michael E. Gerber:https://www.amazon.com/Myth-Contractor-Contractors-Businesses-About-ebook/dp/B000RO9VHWLet's Connect!Fernando...
Fernando Perez, content creator and streamer, joins to talk about the adventure video game franchise, Uncharted. We talk about how the game helped him through loss, helped build his excitement and love for video games, and how he ultimately came to the space of streaming games himself. Tangents include the hit Denzel Washington film “Man On Fire,” enjoying things for the sake of enjoying them, and the joy of building a community that supports you, rather than giving into the echo chamber of hater culture. Sign up for my newsletter at our website, tandonproductions.com, and let me know what you thought of the episode by finding me on Twitter, Instagram, and Tik Tok @marissakumari. Join our Discord server to connect with other listeners, chat with Marissa, and find out about watch parties! https://discord.gg/VNtVCMDxEK To learn more about Heard.FM and sign up for early access to the app, visit their website, Heard.FM
Episode 1003 After a big week in baseball, we bring you two face-to-face conversations from the San Diego Winter Meetings before a chat about outfield defense. First up, David Laurila sits down with Fernando Perez, former major league outfielder and analyst and current coach for the San Francisco Giants. We hear what Fernando was doing […]
#Estreno #Cine #Entrevista #ElDesarmadero #PabloPinto #FernandoPerez #CineConMcFly SINOPSIS Bruno es un artista plástico que después de un hecho traumático se refugia en un desarmadero de autos chocados donde deberá vigilar el predio. Una noche tendrá una visión reveladora y hará lo imposible por ingresar al universo de los muertos. REPARTO Luciano Cáceres, Pablo Pinto, Clara Kovacic, Malena Sánchez, Diego Cremonesi, Fernando Pérez, Amelia Cáceres Currá, Gerónimo Pérez, Joaquín Cáceres, Pablo Ríos. FICHA TÉCNICA Escrita y dirigida: Eduardo Pinto. Director de fotografía: Fernando Lugones. Cámara: Daniel Ponce, Martin Kaspersky, Fernando Lugones , Eduardo Pinto. Dirección de arte: Cintia Español Vestuario: Paula García. Maquillaje: Gabriela Castillo. Artista de filmación: Mónica Rojas. Director de Sonido: Pablo Irrazábal. Canción: Morella, Ciro y Los Persas. Música: Manuel Pinto. Montaje: Joaquín Mustafá Torres. Producción de campo: Pablo Pinto, Fernando Pérez. Asistente dirección: Choice Noise. Productores ejecutivos: Pablo Pinto, Luciano Cáceres, Eduardo Pinto, Fernando Pérez. Producida: Eusebia en la Higuera, Rodríguez filma, HD Argentina Si quieren invitarme un cafecito: https://cafecito.app/cineconmcfly ☕ Seguí todas las novedades del mundo del cine y los últimos estrenos videocomentados en: En Twitter: http://twitter.com/pablomcfly En Facebook: https://www.facebook.com/cineconmcfly En Instagram: http://www.instagram.com/pablomcfly
We got an in-house Hoopagoogoo for you today with producers Madeline Wilson & Fernando Perez Leon! Play along and find some rapid-fire Brighter Sides with some of the best humans Amber and Ed know!
There are a number of interventions that can help people living with dementia improve their memory and thinking skills and to enable them to cope better, or even slightly delay the loss of memory. In this podcast we discuss Cognitive Stimulation Therapy (CST) – an intervention which has been significantly researched and supported by a large amount of evidence. In this ISTAART Research Perspectives Special, Global Brain Health Institute (GBHI) Fellows Fernando Peres and Dr Clara Domínguez Vivero talk with CST expert and researcher Dr Elisa França Resende and Alzheimer's Association Volunteer and person living with dementia Pam Montana. Our two guests give two perspectives from each side of the treatment, researcher and provider and recipient and user. Exploring the research and how CST has helped to support Pam to live with the progressive symptoms of dementia. -- Fernando Perez is a Journalist, Writer and GHBI Fellow based in Brazil. -- Dr Clara Dominguez Vivero is a Neurologist, PhD holder, Neuroinflammation Researcher and GHBI Fellow based at Hospital Clínico Universitario de Santiago de Compostela. -- Dr Elisa de Paula França Resende is a Neurologist and GBHI Fellow, researching Cognitive Reserve and dementia in people with low education at Federal University of Minas Gerais (UFMG), Brazil. -- Pam Montana was diagnosed with young-onset Alzheimer's disease in 2016 at the age of 61. A former Intel executive, Pam managed and led sales teams until her early retirement in 2017. Pam is a champion for dementia research and a former member of the Alzheimer's Association National Early-Stage Advisory Group. Find out more about our hosts and guests and review a full transcript of this podcast on our website at https://www.dementiaresearcher.nihr.ac.uk/podcast Further Reading: CST to Maintain Memory - https://bit.ly/3quKXC5 CST Toolkit - https://bit.ly/33xjBSD GBHI - https://www.gbhi.org/ For information on ISTAART Visit – https://www.alz.org/istaart -- This podcast is brought to you in association with Alzheimer's Association, Alzheimer's Research UK and Alzheimer's Society, who we thank for their ongoing support.
How about some Holiday Hoopagoogoo for that hiney? We got some LPN favorites joining The Brighter Side this week. We got Fernando Perez from Abe Lincoln's Top Hat and Too Real Henry Zebrowski from LPOTL finding Brighter Sides to shitting out x-mas cookies, Santa raging on Twitter, & getting terrifying presents from lovers. Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 3.0 License creativecommons.org/licenses/by/3.0
Regresan los Jueves de Tútu ! Fernando Perez trae tres casos muy particulares que sucedieron en carreteras mexicanas.
Director de Cultura Popular de Gral Rodriguez
Tout l'été, Geneviève Bridel nous emmène à la rencontre de femmes inspirantes et tenaces. Des "défricheuses" qui ont pris leur destin en main, mues par le désir dʹapprendre et dʹêtre les égales des hommes. Pour ce premier épisode, on rencontre Henriette Favez (1786-1856). Vaudoise mariée à un soldat de Napoléon puis veuve à 18 ans, elle devient médecin sous des habits dʹhomme et part pour Cuba. Elle y soigne les plus démunis sous le nom dʹEnrique Faber et épouse une Cubaine. Condamnée pour offense à lʹordre et la morale, elle finit ses jours dans les ordres en Louisiane. Avec la cinéaste Laura Cazador, co-réalisatrice avec Fernando Perez du film qui lui est consacré "Insoumises". Film à voir sur la plateforme Filmingo.ch A lire aussi un portrait dʹHenriette Favez dans le livre "100 femmes qui ont fait Lausanne" (Ed. Antipodes). Réalisation: José Moreno Archives: Josette Suillot
A short taster of up-coming episodes. What can you expect? Discussions on navigating culture, finding opportunities, forming connections, sparking your imagination (and occasionally moving and immigration logistics.)Guests featured in the trailer:Robert Belle, James Whittaker, Nadedja, Romina Muhametaj, Nick Smith of The Steam Machine Brew Co., Bene Katabua, Fernando Perez and Mark Hartman.If you want to support this show...Follow us on your podcast app.Get in touch on Instagram.Download our app in the Google Play store. You can get the podcast episodes, updates and valuable links straight to your phone.We also offer multimedia production services and content creation consultancy: www.creatorsabroad.com.Join us on our journey...InstagramFacebookYouTube
En este episodio, Alejandro Girón y Rodrigo Zubiría se unen a Fernando Perez-Maldonado, experto en inversiones en bolsa para discutir cuál es la mejor forma de ganar tu primer millón.
Los cambios en el ciclo menstrual pueden ser tan útiles para predecir posibles alteraciones hormonales y endocrinas... el mas común es el SINDROME DE OVARIO POLIQUISTICO. Donde aparte de tener periodos menstruales irregulares o nulos tenemos una elevación de andrógenos (hormonas masculinas), nuestra insulina se dispara y se presenta una inflación crónica ademas de exceso de vello corporal, cada del cabello, dificultad para perder peso, mucho estrés y acné Se que esto suena de pronto abrumador pero Fer y yo hablaremos acerca de esas estrategias de apoyo para mejorar esta alteración... Quédate a escuchar lo que tenemos para ti !
En el episodio de hoy charlamos con un “clásico” de la veterinaria de Madrid. Desde 1984, el Hospital veterinaria Retiro, ha aportado a nuestra profesión una visión valiente y adelantada a su tiempo y ahora su director vuelve a dar una vuelta de tuerca y apuesta por un proyecto que va a dar mucho que hablar, el Vet Nutrición Center Madrid. Un centro dedicado a la alimentación clínica aplicada al animal de compañía. ¡¡¡Vamos a escuchar a Fernando, un veterinario convencido!!!
Orain dela bi urte hartu zuen Fernando Perezek Bilboko Azkuna Zentruko zuzendari kargua. Martxan dauden proiektuak errepasatu ditugu berarekin eta zentruaren hurrengo asmoak ere azaldu dizkigu. ...
Eric Anderson (@ericmander) and Travis Oliphant (@teoliphant) take a far-reaching tour through the history of the Python data community. Travis has had a hand in the creation of many open-source projects, most notably the influential libraries, NumPy and SciPy, which helped cement Python as the standard for scientific computing. Join us for the story of a fledgling community from a time “before open-source was cool,” and their lessons for today’s open-source landscape. In this episode we discuss: How biomedical engineering, MRIs, and an unhappy tenure committee led to NumPy and SciPy Overcoming early challenges of distribution with Python What Travis would have done differently when he wrote NumPy Successfully solving the “two-option split” by adding a third option Community-driven open-source interacting with company-backed open-source Links: NumPy SciPy Anaconda Quansight Conda Matplotlib Enthought TensorFlow PyTorch MXNet PyPi Jupyter pandas People mentioned: Guido van Rossum (@gvanrossum) Robert Kern (Github: @rkern) Pearu Peterson (Github: @pearu) Wes McKinney (@wesmckinn) Charles Harris (Github: @charris) Francesc Alted (@francescalted) Fernando Perez (@fperez_org) Brian Granger (@ellisonbg) Other episodes: TensorFlow with Rajat Monga
Today on The Wet Slap, Brant Lincoln and Fernando Perez give an inside look on the dark truths of car sales, how artificial intelligence will take your job, and some terrible club stories.
Brian and Don welcome a much anticipated guest for this episode, Professor Fernando Perez joins us for an episode of 26.1 AI Podcast. Dr. Perez speaks about his journey, the community, and all the challenges along the way. Fernando shares in his inimitable style, how he journeyed from straight laced physicist in pursuit of an academic career to doggedly ignoring naysayers and creating one of the most important components of the modern PyData stack. One personal challenge during this journey was losing a friend Dr. John Hunter. John also influenced your host Brian Ray. Though John missed collaborating when Fernando set out with IPython because of conflicts from prior commitments, the two joined in later to collaborate on advancing tools data scientists use every day now. Sit back and enjoy Fernando's dexterity on multiple topics as hosts Brian Ray and Don Sheu hold on for the ride for your benefit listener.
Baseball is coming back! Fritz and Kris are joined by SF Giants beat reporter Kerry Crowley and former MLB player Fernando Perez to discuss the return of baseball, from the ugly negotiations between the players and owners, the who might benefit from a shortened season, brawls, and how politics and capitalism have tied into sports returning.
Link to hub of various Black Lives Matter causes/petitions/organizations: https://blacklivesmatters.carrd.co/ Fritz and Kris are back for the second part of the final breakdown of ESPN's "The Last Dance." Like the first part of this final breakdown, this episode was recorded about a week ago and more heavily produced, with clips from various friends and family members reacting to the documentary edited in, including a ten minute appearance from Fritz and Kris's mentor, Fernando Perez (15:30). Fritz and Kris discuss whether or not the doc lived up to its potential, winners and losers, and revisit MJ/LeBron one last time.
Neste episódio entrevistamos o Dr. Fernando Perez, atual Diretor Presidente da Recepta Bio. Durante a conversa, falamos sobre como a Recepta Bio atua no combate ao Câncer de Ovário e também as dificuldades e boas práticas do cenário Científico, no que diz respeito a empreender. Aperte o Play e aproveite! Dr. Fernando Perez: É engenheiro eletrônico pela Escola Politécnica da Universidade de São Paulo (1967), bacharel (1967) e mestre (1969) em Física pela Universidade de São Paulo (1969) e doutor pela Escola Politécnica de Zurique (1973). Foi professor titular do Departamento de Física Matemática do Instituto de Física da USP e Diretor Científico da Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) de 1993 a 2005. Membro da Academia Brasileira de Ciências e da Academia de Ciências do Mundo em Desenvolvimento (TWAS). Comendador e Grã-Cruz da Ordem do Mérito Científico e Tecnológico. Atualmente é Diretor Presidente da Recepta Biophama, empresa de biotecnologia na área de saúde humana.
A freezer opening-up, the hum fills the air while MARK shifts things around looking for something to eat. MARK Hmmm...Frozen spinach, frozen spinach, frozen spinach…(scoff) I just want a snack, is that too much to ask! A voice suddenly is heard. It is sensual and ominous. VOICE What about me Mark? MARK What?! Who said that? VOICE Over here, behind the chopped spinach. Shifts the bag MARK The regular Ice Cream I bought on accident? ICE CREAM Yes, Mark. it’s me. I’ve been waiting for you. MARK But...but I’m lactose intolerant. ICE CREAM It’s okay, I won’t tell. MARK No! ICE CREAM (whispers) Spoon me MARK NEVER! ICE CREAM SPOON ME! Mark and the Ice Cream both scream at each other. Cut to the hall closet door opening. ALEX Mark?! What happened to all the toilet paper? MARK Uhhhhh…. This episode was produced by David Magadan of Directive, with additional voices from Mia Passarella and Fernando Perez.
David Magadan is an MFA Actor, Writer, and Sound Designer as well as the creator of the Scifi Audio Drama Directive. Directive: Part 2 is now available for streaming on your favorite Podcatcher. https://podcasts.apple.com/us/podcast/directive/id1441355969 TRANSCRIPT Video call set up sounds, the mic on the other side connects and we hear the room tone kick in. JONATHAN Pat! What’s up man, how’s quarantine? PAT Good man! I finished all those 20,000 piece puzzles I impulse bought and alphabetized my movie collection by the likelihood of being watched again. What about you? You still doing your English teaching for Chinese kids? JONATHAN Yup! Still teaching the bao bao’s. PAT How’s that going? JONATHAN Uhhhhh Cuts to a class with Jonathan and Leo JONATHAN Good job Leo! Dui ya! (clicks) Okay Leo, What do you see on the mountain? LEO I see Teacher Jonathan. You are on the mountain. You jump off the mountain and you DIE. Big monsters eat you and you are DIE. YOU ARE SO WEAK. YOU ARE SO WEAK. Leo proceeds to go into a 6-year-old maniacal giggle, cuts back to the video call. JONATHAN great. Ya. Super rewarding. This episode was produced by David Magadan of Directive, with additional voices from Clayton Caufman and Fernando Perez.
Fernando Pérez es un hombre de radio, de ventas, de publicidad, de negocios y un locutor de élite. Un apasionado de la locución que conoce perfectamente como funcionan el talento y el negocio en Panamá.
In their twentieth episode, Jessie and Allison talk about all things Blood, Blush, and how their friends are making it BIG with THEIR SPECIAL GUEST!!! That's right! They've got Fernando joining them for this month's episode ALL THE WAY FROM LA! With Allison tackling Blood with America's first vampire: The case of Mercy Brown, Jessie rolls on into blush with the amazing new jade rollers, and Fernando tells us all about his AMAZING new web series called 'generation z'!!!They also discuss how skin care SHOULD BE and IS universal, how directors and actors wont always see eye to eye, how each theater kid is different,and much more! Please rate, review, subscribe...Oh! and share :)
Hey, everyone. Welcome to the Launch and Scale podcast. This is episode four, and I'm Khierstyn Ross. In today's episode, we are going to be taking a look at what is my recommended software stack to use when building up an engaged audience for your product online. And this question was inspired by really Fernando Perez. He emailed in asking how we can incorporate click funnels into our overall plan? So I'm going to spend the next few minutes of this episode really diving in to help you simplify that process because I know that when you're getting started online there could be some confusion as to which software solution do you actually need and what is it for. So, when you are building up a targeted audience of people to reach out to when building up that audience in anticipation for your product launch or even a new business that you have, you can have contacts in different areas. You may have friends and colleagues on LinkedIn that have messaged you to say, "Yes, I really want to help support you." You may have people at work, you may have people from an existing customer base or email list that all say yes, "I'd love to be kept in contact and let me know when you're launching." And the problem with that is if you look at managing communication between multiple platforms, you may get overwhelmed because some people are your friends and you may think that they need different information on the product then a new email subscriber, and how really do you compartmentalize that data? So the answer is that you want to have one central spot online where people go to sign up for information to get updates on your product launch. So that's the very first thing is any communication you have, don't complicate it. You want to have one universal spot where everyone goes to not only learn more about your product but be notified when you're live. So you want to have one spot online. This is a landing page. So a landing page is going to be a one-page website with a little bit of product information on your product, of course, and information on when you're launching, and you want to have this website on a domain that is relevant to the product you're launching. So it could either be yourproductname.com it could be yourpersonaldomain.com if you don't currently own something that's having to do with your product, but have one central spot online where people go to subscribe and join your mailing list, so that's one thing. One kind of software we need to look at is what to do to build a landing page? That's phase one is landing page software. And the second one is when people actually go to this landing page online and they like your product information and they want to join your mailing list to stay up to date with your launch updates and get notified when you're live then you need to have a spot to actually organize and manage those email addresses. The thing is that when you are managing multiple hundreds of people that want to stay in contact with you, think of how crazy that's going to be to try to manage all that communication in Gmail. It's going to get lost and it's just going to be a bit of a nightmare. So you want to have, the second kind of software you want is an email CRM system where when someone inputs their email address on the landing page, that that landing page software will push that email onto a list in a software that will keep that nice and organized. And so that's where we get into an email CRM system as well. And then when you actually go to email your full mailing list, you can do that from one spot where you can automate emails and do a bunch of cool stuff. So those are the two kinds of software I want to look at is what is the recommended landing page software we need so that you can go in, don't worry, you don't need technical coding skills to do this. There're a lot of really simple, straightforward solutions that give you landing page templates you can use. So the first one is what is my recommended landing page software? I have a couple, depending on your budget. Personally, I am a massive fan of ClickFunnels. ClickFunnels is a software that is $97 per month and I am an affiliate of ClickFunnels, but I'm a mega fan of Russell Brunson and the work that he does. I love ClickFunnels because they have really awesome templates and their builder is so easy so I can customize anything that I want to. And I found that when I was trying other landing page software’s a couple of years back that I got really frustrated by how clunky the system was and how not easy other software’s were to use. And so when I moved over to ClickFunnels, it's just, it's stupidly easy, which is awesome. So I love ClickFunnels because it's super customizable and you get really good analytics to tell you really how your landing page is performing in terms of how many people come to the page actually subscribe. And so that was excellent for me. That's more of the higher priced option. If you are on a budget or you don't want to spend $97 a month, other popular solutions, you could look at our Wix or Squarespace. They also have great builders, not necessarily as customizable as ClickFunnels. However, those are two lower priced software’s that end up being around the range of 20 to $40 per month. That's my first recommended software stack. And so Fernando, if you're, of course, you're listening to this, but you're wondering how to incorporate ClickFunnels into your overall plan, it's going to be to create that one spot online where people can go to learn more information about what you are working on. That's one thing. The second kind of software that we talked about is once people actually subscribe to your landing page, where does that information go? So the recommended software that I use is ActiveCampaign. Another popular solution is MailChimp and they start at, if you're just getting started with email marketing, they start around $9 per month and then they will increase in price based on the number of contacts you have. So that's more of a solution that you grow into. So those are the recommended software’s I use and that's really how the funnel works online. Where I find people get really stuck is when you do some research, you may look online and say, "Okay, well I need a website and a landing page and they're all these things happening and I don't really know. Maybe I need multiple landing pages for the different kinds of people I talk to. Maybe all these things." So just when you're setting yourself online with your landing page, just keep it simple. You don't need multiple URLs, you don't need multiple landing pages, you don't need all this fancy software. Just keep it really simple with what you need. Send everyone to one spot and that's going to help make your life a lot easier and make sure that you are keeping the same conversation with everyone so that you don't have all these different communications. So the other thing, I guess one common question I get, is what if you are coming into this episode and you say, "Okay, well I already have a website," what do you do in that case? So again, you have two options. If you've already built out a full website, then I recommend if you are planning on doing paid ads or sending people to a page, then I would still build a separate landing page specific to that product. Because if you already have a website where you're selling three to four different products, if you send traffic to the website where it's not specifically talking about your product, you're actually going to lose a lot of people because they may not know where on your website to go to find out more product information about the new launch. Right? So you want to have again, one central spot and if you have a landing page, what we did with Foundr is we did something, I don't remember the exact URL, but it was something like foundrmag.com/kickstarter so even though foundrmag.com was a full digital publication and it was their main business, Nathan didn't want to muddle the message on that by advertising the whole Kickstarter project all over his main page. So we had a designated spot, a designated landing page that again, you can use Squarespace, Wix, or ClickFunnels to set up, and then you would just set up a subdomain or a separate page on your website where you direct all traffic to. Again, just keep the conversation simple. So that's another easy way that you can really just navigate and set yourself up online so that you don't have literally a hundred different things going on. Keep it simple. One landing page. I love ClickFunnels, but again, if you don't want to use that Squarespace or Wix is good. If you do want to test out the different software’s, see which one works for you. I highly recommend that. I have links to all three software’s that you can access by going to Khierstyn.com/lso4. So in the show notes, so you can go down to recommended resources and there will be links to the software’s that we talked about in here. And apart from that, my name is really impossible to spell, so it's K-H-I-E-R-S-T-Y-N. So again the link for that is Khierstyn.com/lso4 and apart from that, if you are working on building up your E-commerce brand or are looking to launch a product in the near future, let's talk, you can schedule a strategy consult with myself and my team by going to kiersten.com/schedule again, that's K-H-I-E-R-S-T-Y-N.com you're listening today, guys. We will see you next time.
Mitch sits down with returning guests Suzy Stein and Fernando Perez of Entity Eye Entertainment on the convention floor of YumaCon 2018. Facebook: Entity Eye Entertainment, The Mark Of Kings Twitter: @EntityEyeEnt, @TheMarkofKings Website: Entity Eye Entertainment
Ben Lindbergh and Jeff Sullivan banter about Ben’s excursion to a Helena Brewers game, Matt Davidson‘s relief excellence, Juan Soto‘s historic performance, Kole Calhoun‘s resurgence, the A’s acquiring Mike Fiers, Mike Trout’s 27th birthday, and more, then (25:14) talk to former big leaguer (and former live guest) Fernando Perez about the 10th anniversary of the […]
Fernando Perez is best-known as the creator of IPython and co-founder of Project Jupyter: a set of open-source data science tools that some may consider to be the equivalent of the bat & ball to the sport of baseball. Today, you really can’t play the game of data science without Jupyter Notebooks and our guest today is one of Jupyter's leads and originators (see here for the rest of the amazing team). Fernando is also an Assistant Professor in Statistics at UC Berekely, Researcher at the Berekely Institute for Data Science, and Founding Board Member of the NumFOCUS foundation — the community that creates the SciPy stack, along with virtually every other notable open source data science tool out there. This conversation was recorded in-person with Fernando in his office on UC Berekely’s campus, and it turned out to be the most humanizing, energizing, and down-to-earth interview I’ve had so far. Some of the many topics we covered include: what Fernando wanted to be while growing up in Medellin (Me-de-jean), Colombia the function that formal education played in his learning of data science the story behind IPython and Project Jupyter and it’s evolution over the past 10 years lessons learned about technical competence and human character from his mentors over the years what a “computational narrative” means to him and why it’s principles are key to data storytelling Fernando’s experience teaching a 650-student course (part of a pair of courses that are the largest of it's kind) as part of the Berekely Institute of Data Science Enjoy the show! Show Notes: https://ajgoldstein.com/podcast/ep7/ Fernando’s Twitter: https://twitter.com/fperez_org AJ’s Twitter: www.twitter.com/ajgoldstein393/
Escalera de Versos, episodio 23. Poetas invitados, Fernando Perez de Plasencia (Extremadura) y Mari Carmen Gomez de Alcañiz (Aragón). Recitan poemas de sus respectivos libros los poetas invitados. La música es de Pablo Alboran (quien), Alejandro Ibazar (pequeños detalles) y Juan Manuel Serral (la saeta) Suscribete a nuestros episodios y no te pierdas ninguno Envíanos tus notas de voz a Whasapt 654 93 42 41 Nuestro contacto: info@podcastaragon.es
Escalera de Versos, episodio 23. Poetas invitados, Fernando Perez de Plasencia (Extremadura) y Mari Carmen Gomez de Alcañiz (Aragón). Recitan poemas de sus respectivos libros los poetas invitados. La música es de Pablo Alboran (quien), Alejandro Ibazar (pequeños detalles) y Juan Manuel Serral (la saeta) Suscribete a nuestros episodios y no te pierdas ninguno Envíanos tus notas de voz a Whasapt 654 93 42 41 Nuestro contacto: info@podcastaragon.es
Dane Martinez discusses the pace of play rules, tanking, and unsigned players in the MLB. He is joined in studio by former Rays pitcher Fernando Perez. Lastly, Danny comes up from the pit of misery so he and an "old man" can take the Wonderlic Test! #FNTSYRadio #FNTSY #FantasyBaseball #FantasyFootball #WonderlicTest #Rays Learn more about your ad-choices at https://news.iheart.com/podcast-advertisers
Ep. 22 — The rest of the interview has arrived! In part two of their chat with the former Tampa Bay Rays outfielder, Alex and Bobby shift away from strictly on-the-field questions and ask Fernando about poetry and his writing career post-baseball. Then they get his thoughts on why every sports network needs a podcast like Tipping Pitches, and chat about the weirdest traditions he’s seen around the game. Finally, they grill him on the age-old question: what do guys talk about when they get down to first base? Follow Fernando on Twitter here. Some of Fernando’s work: The world’s greatest wiffle ball tournament The evolution of sign stealing in baseball On the art of the walk up song Songs featured in the episode: Edd Kalehoff — “Theme to the Price is Right” • The Cure — “Boys Don’t Cry” • Booker T. & the M.G.’s — “Green Onions” ~~ Follow Tipping Pitches on Twitter and like us on Facebook. Find more great content over at tippingpitches.atavist.com. Questions, comments, or concerns? Shoot us an email at tippingpitchespod@gmail.com.
Ep. 21 — On part one of this very special episode, Bobby and Alex are coming to you straight from the apartment of former MLB player and current sportswriter Fernando Perez. They talk with Fernando about everything from life in the minor leagues to how player development is changing. They also chat about players really knowing the game versus simply being able to play it, and ask the question, what makes a good GM, really? This was a really fun one, so make sure to check back in a couple days for part two! Follow Fernando on Twitter here. Some of Fernando’s work: The world’s greatest wiffle ball tournament The evolution of sign stealing in baseball On the art of the walk up song Songs featured in the episode: Paul McCartney and Wings — “Band on the Run” • Booker T. & the M.G.’s — “Green Onions” ~~ Follow Tipping Pitches on Twitter and like us on Facebook. Find more great content over at tippingpitches.atavist.com. Questions, comments, or concerns? Shoot us an email at tippingpitchespod@gmail.com.
Support these videos: http://pgbovine.net/support.htmhttp://pgbovine.net/PG-Vlog-94-fernando-perez.htmThis story appeared in [Year 4 of The Ph.D. Grind](http://pgbovine.net/PhD-memoir-year4.htm) (search for "Fernando")[Fernando's academic home page](https://bids.berkeley.edu/people/fernando-perez)Recorded: 2017-12-07
I caught up with Fernando Perez recently whilst in Barcelona. Fernando contacted m earlier in the year with a desire to share his music and his story. John Schneider from Gobal Village in Los Angeles describes Fernando : "Fernando Perez's is a long and amazing story. He learned the global music traditions one on one living in every major culture. Whether is West African, Latin American, Chinese, Hawaiian, Indian, Turkish you name it, Fernando Perez has been there, done that. He shares his artistry with an increasing list of recordings and publications which are just absolutely beyond belief." In my chat with Fernando we talk about his time in LA and his work as a session guitarist, his teachers, and his passion for guitar music from other cultures. You can find out more info about Fernando at www.fernandoperezguitar.com. The Guitar Conversations Podcast is free but you can help support the show in a couple of ways. Firstly go to iTunes and leave a rating or review and secondly visit latinguitarmastery.com which is my guitar learning platform. Receive a 20% discount on a subscription by typing conversations in to the promo code. Thanks for listening to guitar conversations podcast, you can listen to moire episodes at www.sergioercole.com as well as finding out about my other projects or send me a hello and let me know what you think of the show. Till next episode bye for now.
Sonido Clash is a collective from San Jose, CA comprised of artists, DJs, promoters, & activists that have been throwing shows in the Bay Area for nearly a decade. They’ve collaborated with Prayers, Las Cafeteras, Mexican Insitute of Sound, Los Rakas, Ana Tijoux, Toy Selectah & many more.Jesus sat down and chopped it up with one of the founding members, Fernando Perez, AKA "Tlacoyo", about their story, musical influences, the fast changing landscape, & their upcoming festival labor day weekend. Be sure to follow them and check out the 2nd Annual Sonido Clash Music Fest in San Jose and see Prayers, Helado Negro, Cuco, Grupo Maravilla & many more!Buy tickets here >> https://scmusicfest2017.eventbrite.com/
The O’Reilly Programming Podcast: The next technological evolution of cloud systems.In this episode of the O’Reilly Programming Podcast, I talk serverless architecture with Mike Roberts, engineering leader and co-founder of Symphonia, a serverless and cloud architecture consultancy. Roberts will give two presentations—Serverless Architectures: What, Why, Why Not, and Where Next? and Designing Serverless AWS Applications—at the O’Reilly Software Architecture Conference, October 16-19, 2017, in London.Discussion points: Why Roberts calls serverless “the next evolution of cloud systems,” as individual process deployment and the resource allocation of servers are increasingly outsourced to vendors How serverless architectures use backend-as-a-service (BaaS) products and functions-as-a-service (FaaS) platforms The similarities and differences between a serverless architecture and microservices, and how microservices ideas can be applied to serverless Roberts explains that serverless is “not an all-or-nothing approach,” and that often “the best architecture for a company is going to be a hybrid architecture between serverless and non-serverless technologies.” Recent advances in serverless tooling, including progress in distributed system monitoring tools, such as Amazon’s X-Ray We also get a preview of JupyterCon, August 22-25, 2017, in New York, from conference co-chair Fernando Perez. Our discussion highlights the sessions on JupyterLab, and the UC Berkeley Data Science program, an introductory-level course in which the students use Jupyter Notebooks. Other links: Video of Roberts’ presentation An Introduction to Serverless at the April 2017 Software Architecture in New York The free eBook What Is Serverless?, by Mike Roberts and John Chapin The video AWS Lambda, presented by Mike Roberts and John Chapin Video of Roberts and Chapin’s OSCON 2017 presentation Building, Displaying and Running a Scalable and Extensible Serverless Application Using AWS Sam Newman’s book Building Microservices
From The Bell House in Brooklyn The Fangraphs Effectively Wild Podcast with Ben Lindbergh, Jeff Sullivan and Fernando Perez!
The O’Reilly Programming Podcast: The next technological evolution of cloud systems.In this episode of the O’Reilly Programming Podcast, I talk serverless architecture with Mike Roberts, engineering leader and co-founder of Symphonia, a serverless and cloud architecture consultancy. Roberts will give two presentations—Serverless Architectures: What, Why, Why Not, and Where Next? and Designing Serverless AWS Applications—at the O’Reilly Software Architecture Conference, October 16-19, 2017, in London.Discussion points: Why Roberts calls serverless “the next evolution of cloud systems,” as individual process deployment and the resource allocation of servers are increasingly outsourced to vendors How serverless architectures use backend-as-a-service (BaaS) products and functions-as-a-service (FaaS) platforms The similarities and differences between a serverless architecture and microservices, and how microservices ideas can be applied to serverless Roberts explains that serverless is “not an all-or-nothing approach,” and that often “the best architecture for a company is going to be a hybrid architecture between serverless and non-serverless technologies.” Recent advances in serverless tooling, including progress in distributed system monitoring tools, such as Amazon’s X-Ray We also get a preview of JupyterCon, August 22-25, 2017, in New York, from conference co-chair Fernando Perez. Our discussion highlights the sessions on JupyterLab, and the UC Berkeley Data Science program, an introductory-level course in which the students use Jupyter Notebooks. Other links: Video of Roberts’ presentation An Introduction to Serverless at the April 2017 Software Architecture in New York The free eBook What Is Serverless?, by Mike Roberts and John Chapin The video AWS Lambda, presented by Mike Roberts and John Chapin Video of Roberts and Chapin’s OSCON 2017 presentation Building, Displaying and Running a Scalable and Extensible Serverless Application Using AWS Sam Newman’s book Building Microservices
At the Bell House in Brooklyn for a Pitch Talks event, Ben Lindbergh and Jeff Sullivan talk to former major leaguer Fernando Perez about Mike Trout’s birthday pranking, the return of Carter Capps, Perez’s injury history, his late conversion to switch-hitting, what makes a pitcher deceptive, the problems with player development and batting practice, the […]
Fresh off their recent discussion about catch probability, the Statcast crew can't help but discuss the amazing catch made by Adam Jones against the Dominican Republic team in the World Baseball Classic. Statcast host Mike Petriello and national editor Matt Meyers are joined by two former MLB outfielders, Ryan Spilborghs and Fernando Perez, to break down one of the more impressive plays in Classic history.
Happy Halloween! We've got a BONUS episode for you today! Let's just whip up the magic words: Hocus pocus, boil and toil, double the trouble! *poof* IT WORKED! We've got a great conversation with Suzy Stein and Fernando Perez, the creators and writers for their graphic novel, The Mark of Kings. The post Everything is Awesome Episode 39.5 – Challenge Accepted appeared first on That's Entertainment.
Happy Halloween! We've got a BONUS episode for you today! Let's just whip up the magic words: Hocus pocus, boil, and toil, double the trouble! *poof* IT WORKED! We've got a great conversation with Suzy Stein and Fernando Perez, the creators, and writers for their graphic novel, The Mark of Kings. We chat about film, comics, inspiration and more! But that's not all! We've got some behind the scenes audio from our interview with Adam (and a Lil' bit of Travis) from the After 6 Podcast. Don't forget, on November 17th, 2016, we'll be performing LIVE at Bridget Sound on South Street in Philadelphia, Pa at 8:00 pm! It's going to be a good time-- we'll talk to awesome people, play some awesome games, and just have an awesome time altogether. We've launched our Patreon page. Patreon is a service that allows you, the Super Friends, to help support this show. This show will always be free, but with your help, we're looking to grow this show, which allows me to talk to cool and interesting people, into something beyond the specialness it is now. Thanks in advance if you're able to support-- other great ways of supporting this show-- 1) tell a friend! 2) Subscribe and Review on iTunes, which helps bring more eyes to the show, which in turn will allow us to do bigger and cooler things. All this and more on this week's edition of Everything is Awesome! Find Kev on twitter @hhwst Find Entity Eye on twitter @EntityEyeEnt Find The Mark of Kings on twitter @TheMarkofKings Find After 6 on twitter @after6podcast Everything is Awesome on twitter @RealAwesomePod Find The Mark of Kings for purchase here Find After 6 Podcast on the web Support Everything is Awesome on Patreon Support Everything is Awesome by leaving a 5-star review on iTunes, Apple's math gets us in front of more people's eyes and ear :) Support Everything is Awesome by telling a friend
As baseball season kicks off this week hosts Tom and Joe speak to former Tampa Bay Ray and Columbia graduate Fernando Perez, who opens up about his path to the Major League and his fears for the future of the sport. Fernando explains in the second half of the show (from 31:15) why he has no problem with bat-flipping, and why those who do are contributing to a "tired" baseball culture which threatens the sport's long-term relevance. He offers his solution as to how the sport can secure its future, by being more inclusive towards minorities and confronting baseball's language barrier. Fernando also talks in the first half of the show about why the Columbia badminton team proved a tricky obstacle along his path to being drafted, and why cutting up a teammate's chicken and bringing them beer can be a good way to go up on the world of Minor League baseball. Follow Fernando on Twitter @ifernandoperez3 The Facts The CUSP Show is a production by the faculty of Sports Management at Columbia University. You can get in touch with the program on Twitter @CUSportsBiz. Our presenters are Joe Favorito and Tom Richardson.
Episode 10 - Brian Granger and Fernando Perez of the IPython Project
Minutes after the STAR WARS CELEBRATION ended, an assembly of Skywalkers gathered to discuss the best moments of the convention. This episode is packed with THE FORCE AWAKENS Trailer reactions (tears), Star Wars Celebration thoughts (and tears), Star Wars Rebels Season 2 Red Carpet Interviews (more tears), and even a word or two from Mary Franklin, Star Wars Celebration organizer-supreme! Special thanks to our Debriefing Panel: Alan Sanborn, Robert (Bald Solo) Bapst, Tricia Barr, Geek Kay, Dave Skale, Joey Pittman, Matthew Clifton, Kevin Raidernerd Reitzel, David and Luann Manderville, Ryan Stampfli, Fernando Perez, Patty Hammond and Jeff Long. And remember…NeverLand On Alderaan! Skywalking Through Neverland T-Shirts now available on TeePublic! Check them out HERE. Shopping AWESOME new Star Wars on HerUniverse? Click here! Contact us: tweet! tweet! @SkywalkingPod Send emails to share@skywalkingthroughneverland.com and follow us on Facebook. Subscribe on iTunes | Stitcher | YouTube
Skywalking Through Neverland: A Star Wars / Disney Fan Podcast
Minutes after the STAR WARS CELEBRATION ended, an assembly of Skywalkers gathered to discuss the best moments of the convention. This episode is packed with THE FORCE AWAKENS Trailer reactions (tears), Star Wars Celebration thoughts (and tears), Star Wars Rebels Season 2 Red Carpet Interviews (more tears), and even a word or two from Mary Franklin, Star Wars Celebration organizer-supreme! Special thanks to our Debriefing Panel: Alan Sanborn, Robert (Bald Solo) Bapst, Tricia Barr, Geek Kay, Dave Skale, Joey Pittman, Matthew Clifton, Kevin Raidernerd Reitzel, David and Luann Manderville, Ryan Stampfli, Fernando Perez, Patty Hammond and Jeff Long. And remember…NeverLand On Alderaan! Skywalking Through Neverland T-Shirts now available on TeePublic! Check them out HERE. Shopping AWESOME new Star Wars on HerUniverse? Click here! Contact us: tweet! tweet! @SkywalkingPod Send emails to share@skywalkingthroughneverland.com and follow us on Facebook. Subscribe on iTunes | Stitcher | YouTube
From the September 2009 issue of Poetry, Andrew shares a poem by former Tampa Bay Ray outfielder Fernando Perez [...]
Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Mm MM. Speaker 3: Uh Huh [inaudible]. Speaker 4: [00:00:30] We'll come to spectrum the science and technology show on Katie l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events. Speaker 3: [inaudible].Speaker 1: Hello and good afternoon. My name is Renee Rao and I'll be hosting today's show this week [00:01:00] on spectrum present part two of our two part series on big data at cal. The Berkeley Institute for data science bids is only four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez. They are today's guest Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley for Nana Perez is a research scientist at the Henry H. Wheeler [00:01:30] Jr Brain imaging center at UC Berkeley. He created the iPod iPhone project while he was a graduate student in 2001 and continues to lead the project today. In part two they talk about teaching data science. Brad Swift conducts the interview Speaker 5: on the teaching side of things. Does data science just fold into the domains in the fields and some faculty embrace it, others don't. How does the teaching of data science move [00:02:00] forward at an undergraduate level? Yeah, there there've been some really interesting institutional experiments in the last year or two here at Berkeley. Thinking about last semester, fall of 2013 stat one 57 which was reproducible collaborative data science pitched at statistics majors simply because you have to start with the size that can fit in a classroom [00:02:30] and training students in the practices of scientific collaboration around open source production of software tools or to look at what was Josh Bloom's course, so that's astro four 50 it's listed as special topics in astrophysics just because Josh happens to be a professor in the astronomy department and so you have to list it somewhere. The course is actually called Python for science Speaker 6: [00:03:00] and it's a course that Josh has run for the last, I think this is, this was its fourth iteration and that course is a completely interdisciplinary course that it's open to students in any field. The examples really do not privilege and the homework sets do not privilege astronomy in any way and we see students. I liked her a fair bit in that course as a guest lecture and we see students from all departments participating. This last semester it was packed to the gills. We actually had problems because we couldn't find a room large enough to accommodate. So word of mouth is working. In terms of students finding these [00:03:30] courses, Speaker 5: it's happening. I wouldn't say it's working in part because it's very difficult to get visibility across this campus landscape. I am sure there are innovations going on that even the pis and bids aren't aware of and one of the things we want to do is stimulate more innovation in places like the the professional schools. We'll be training students who need to be able to use these tools as well. What do they have in mind or there [00:04:00] are other formats of instruction beyond traditional semester courses. What would intensive training stretched out over a much shorter time look like? What gaps are there in the undergraduate or graduate curriculum that can effectively be filled in that way? The Python bootcamp is another example of this that's been going on for Speaker 6: for about four years. Josh and I teach a a bootcamp on also python for data science that is immediately before the beginning of the fall semester. Literally the weekend before [00:04:30] and it's kind of, it's a prerequisite for the semester long course, but it's three days of intensive hands-on scientific bite on basically programming and data analysis and computing for three days. We typically try to get a large auditorium and we got 150 to 200 people. A combination of undergrads, Grad Students, postdocs, folks from LVL campus faculty and also a few folks from industry. We always leave, leave a few slots available for people from outside the university to come and that one a has been very popular at [00:05:00] tends to, it's intense to have very good attendance be, it serves as an on ramp for the course because we advertise the in the semester course during the bootcamp and that one has been fairly successful so far and I think it has worked well. Speaker 6: We see issues with it too. That would be that we would like to address three days is probably not enough. Um, it means because it's a single environment, it means that we have to have examples that are a little bit above that can accommodate everyone, but it means they're not particularly interesting for any one group. It would be, I think it would be great to have [00:05:30] things of this nature that might be a little bit better focused at the life sciences and the social sciences that the physical sciences, so that the examples are more relevant for a given community that may be better targeted at the undergraduate and the graduate level so that you can kind of select a little bit in tune the requirements or the methodological base a little bit better to the audience. But so far we've had to kind of bootstrapping with what we have. Speaker 6: There's another interesting course on campus offered by the ice school by Raymond Lecture at the high school called working with open data [00:06:00] that is very much aimed at folks who are the constituency of the high school that have an intersection of technical background with a broader interdisciplinary kind of skills that are the hallmark of the high school and they work with openly available data sets that are existing on the Internet to create basically interesting analysis projects out of them and that's of course that that I've seen come up with some very, very successful and compelling projects at the end of the semester Speaker 7: about the teaching and preparation in universities. In [00:06:30] the course of doing interviews on spectrum, a number of people have said that really the only way to tackle sciences interdisciplinary, the big issues of science is with an interdisciplinary approach, but that that's not being taught in universities as the way to do science. Sarah way to break that down using data science as a vehicle. Speaker 5: I can speak about that as a science and technology studies scholar. The practice of interdisciplinarity, what makes it actually work is one of the [00:07:00] the most challenging social questions that can be asked of contemporary science and adding into that the fact that scientists get trained inside this existing institution that we've inherited from let's roughly say the Middle Ages with a set of disciplines that have been in their current form since roughly the late 19th century. That is the interface where I expect in the next oh two to five decades major transformations in research universities. [00:07:30] We don't yet know what an institution or research institution will look like that does not take disciplines as it sort of zero order ground level approximation to the way to encapsulate truth. But we do see, and I think bids is like data science in general and an example of this. We do see continual pressure to open up the existing disciplines and figure out how to do connections across them. It's [00:08:00] not been particularly easy for Berkeley to do that in part because of the structure of academic planning at our institution and in part because we have such disciplinary strengths here, but I think the invitation for the future that that word keeps coming back invitation. The invitation for the future for us is to understand what we mean by practicing interdisciplinarity and then figure out how to hack the institution so that it learns how to do it better. [inaudible] Speaker 8: [inaudible] [00:08:30] you're listening to structure fun. K A, l ex Berkeley Fasten Kirsten and Fernando Perez are our guests. They're part of the Berkeley Institute for Data Science for Bids [inaudible] Oh, Speaker 6: it seems that data science has an almost unlimited [00:09:00] application. Are there, are you feeling limits? I don't know about limits specifically because I think in principle almost any discipline can have some of its information and whatever the concepts and constructs of that discipline can probably be represented in a way that is amicable to quantitative analysis of some sort. In that regard, probably almost any discipline can have a data science aspect to it. I think it's important not to sort of [00:09:30] over fetishize it so that we don't lose sight of the fact that there's other aspects of intellectual work in all disciplines that are still important. That theory still has a role. That model building still has a role that, uh, knowing what questions to ask, it's still important that hypotheses still matter. I'm not so sure that it's so much an issue of drawing arbitrary limits around it, but rather of being knowledgeable and critical users of the tools and the approaches that are offered. Speaker 6: Because in terms of domain [00:10:00] applications, I actually recently saw at the strata conference, which is one of these more industry oriented big data conferences that took place a few weeks ago in Silicon Valley. It's in Santa Clara. One of the best talks that I saw at the conference was an analysis half the poem, if I told him that Gertrude Stein wrote about Picasso After Picasso painted this very famous portrait of her. And that poem has a very, very repetitive rhythmic structure. It has very few words and it's a long poem with a very peculiar linguistic structure. And [00:10:30] this hardest, I, I'm blanking on his name right now, but he's an artist who works kind of at the intersection of digital arts in, in linguistics wrote basically a custom one off visual analysis and visualization tool to work on the structure of this poem to visualize it, to turn it into music. Speaker 6: And it was a beautiful talk. It was a beautiful and very interesting talk and this was kind of the exact opposite of this was tiny data. This was one poem and in fact during the Q and a they asked him and he said, well I've tried to use the tool [00:11:00] on a few other things and there's a few songs in hip hop that it works well with, but it's almost, it's almost custom made for this one poem, right? So this was sort of tiny data, completely non generalizable and yet I thought it was fascinating and beautiful talk. So that's kind of an example that I would have never have thought of as as data science. Any examples of misapplication? Speaker 5: I think we can admit that data science is a buzzword that is [00:11:30] exactly through, it's almost indefinable nature creates space for people to do methodologically problematic and in many cases also uninteresting work. Just throwing data into an analysis without asking is this the right analysis will get you stupid or misleading answers. It's the garbage in out principle. So yeah, like any intellectual tool in the toolkit, [00:12:00] there are misleading conclusions that can be drawn and one of the powers that Berkeley brings to this effort in data science is a focus on the methodology, the intelligent development of methodology along with just building things that look like tools on their own. I think that's going to be the place with the sweet spot for universities because of the emphasis on rigor and stringency and reasoning [00:12:30] along with just getting out results that look good and are attractive Speaker 7: with data science. Are there infrastructure challenges that are worth talking about either in industry or at an academic institution? Because I know that computing power now through Amazon, Google organizations like that are enormous and so industry is sort of giving up the idea of having their own [00:13:00] computational capacity and they're using cloud virtual universities I would think are following suit. Speaker 6: Yes, there is work being done already on campus in that regard. We've had some intersection with those teams. The university right now, uh, we've had since last year a new CIO on campus, Larry Conrad, who's been spearheading an effort to sort of reimagine what the research computing infrastructure for campus should look like. [00:13:30] Considering these questions precisely of what is happening in industry, what are the models that are successfully being used at other institutions to provide larger scales off competitional resources across all disciplines and beyond the disciplines that have been traditionally the ones that have super computers. Well, there's a long history of departments, again, like physics, like competition, fluid dynamics, teams like quantum chemistry teams that have had either their own clusters or that have large budgets who have access to the supercomputing centers at [00:14:00] the doe labs and things of that nature. But as we've been saying today, all of a sudden those needs are exploding across all disciplines and the usage patterns are changing and that often what is the bottleneck is maybe not the amount of raw compute power, but the ability to operate over a very large data sets, so maybe storage is the issue or maybe throughput biologists often end up buying computers that look really weird. Speaker 6: Too many supercomputing centers because they, the actual things that they need are skewed in a different way and so there are certainly [00:14:30] challenges in that regard when we do know that Berkeley is right now at least in the midst of making a very concerted and serious attempt at at least taking a step forward on this problem. Speaker 7: A lot of data is derived from personal information. Are there privacy concerns that you have [inaudible] Speaker 5: they're all quite definitely in so many different ways that the input of experts who have thought about questions of consent, of privacy, [00:15:00] of the challenges around keeping de identified data d identified when it is possible through analytics to understand what patterns are emerging from them that is going to be so key. Especially to working with social data. And so one of the still open questions for all of us working with data that is about people is how to develop the practices that will do the protections necessary [00:15:30] in order to avoid the kinds of catastrophic misuses and violations of privacy that many of us do. Fear will be coming our way as so much data becomes available so fast with so many invitations to just make use of it and worry about the consequences later. That's not the responsible way forward. And I would like to see bids and Berkeley take on that challenge as part of its very deliberate agenda. Speaker 8: [00:16:00] Okay. Spectrum is a public affairs show on k a l ex Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about institutional reactions to bids. Oh, Speaker 7: are there any impediments that you've run into within the bids process [00:16:30] of getting up and running? Cause it's been going since, uh, Speaker 5: it's not been going on that long as it, it's only December of 2013. Pretty recent, but I'm sure there's gotta be some institutional pushback or no, it's, it's been incredible actually how much support the institution has given. What bids is though, is a laboratory for the kind of collaboration that we're trying to instantiate. And so you have 13 brilliant Co-pi eyes each with their own vision and figuring out where [00:17:00] the intersection is and how to get the different sets of expertise and investments where they, where those intersections lie and how to get them aligned. I mean, that's, that's one of the fascinating challenges in front of beds as a laboratory in the small, for the process at large that we're trying to do Speaker 7: on the tools and programming side. How would you break up what languages are providing, what kind of capability, [00:17:30] and are there new languages that are ascendent and other languages that are languages that are losing their grip? I'm sort of curious. It's a, it's another trivia questions that I think might have some interest for people. No, I think there's, there's clearly an ascendance. I think naturally the expansion of the surface of people interested in these problems Speaker 6: is naturally driving the growth and importance of high level languages that are immediately usable by domain scientists. We're not full time programmers [00:18:00] and professional programmers. Traditionally a lot of the high end computing had been done in languages like c, c plus plus for trend and some Java that are languages that tend to be more the purview of, of people who do lots of software development. And a lot of that did happen in departments like physics and chemistry and computer science, but not so much in other disciplines. And so we're seeing the rise of open source languages like Python and r that are immediately applicable and easy to use for data analysis where a few commands [00:18:30] can load a file, compute some statistics on it, produce a few visualizations, and you can do that in five lines of code, not having to write a hundred or 500 lines of c plus plus. Speaker 6: Right. And so the languages like that are, they're not new. Both I think are came out in the late eighties early nineties python came out in 1991 but they're seeing a huge amount of growth in recent years for this reason. There's also a growth of either new tools to extend these languages [00:19:00] or new languages as well. Tools for example, that connect these languages to databases or extensions to these languages to couple them to databases in better ways so that people don't have to only write raw sequel, which SQL is not the classic language for interacting with databases, so extensions to couple existing languages to database back ends. A lot of work is being done in that direction and there are some novel languages. For example, there's a team at MIT that about two years ago started [00:19:30] a project for a new language called Julia that is aimed at numerical computing, but it's sort of re-imagining. Speaker 6: What would you do if you wanted to create a language like python with the strengths of language like python or Ruby or r, but if you were doing that today with the lessons of the last 20 years, that would be good for numerical computing, but it would be easy to use for domain scientists. That would be high level, that would be interactive, that would feel like a scripting tool, but that would also give you very high performance. [00:20:00] If you had the the last 20 years of lessons and the advances in some of the underlying technology and improved compiler machinery that we have today, how would you go about that problem? And I think the Giulia team at MIT is making rapid progress and it has caught the intention of people in the statistics community of people in the numerical analysis and algorithms community. Some prominent people have become very interested in how to become active participants in its development. Speaker 6: So we're seeing both mature tools like python and are growing in their strength and and their importance. At the latest Strada Conference, [00:20:30] for example, there was a an analysis of kind of the the abstracts submitted that had r and python in their names versus things like excel or sequel or Java and Python and are clearly dominating that space, but also these, these kinds of more novels, sort of research level languages that whose futures still not clear because they're very, very young, but at least they're exploring sort of the frontier of what will we do in the next five or 10 years. And is this an area that's ripe for a commercial software creators who develop [00:21:00] a tool that would be specific to data science and sort of the same way that Mat lab is kind of specific now it's kind of a generic tool for mathematics. Obviously my answer here is extremely biased, but I'm, I sort of think that the space for a, the window to create a proprietary data science language is closed already. Speaker 6: I think the community simply would not adopt a new one. There are some existing successful ones such as mat lab, IDL, which is smaller than Madlib. It is widely used in the astronomy and astrophysics. [00:21:30] And Physics Communities Mathematica, which is a project that came out of the mathematics and physics world and that is very, very sophisticated and interesting. Maple, which is also a mathematics language. Those are successful existing proprietary languages. I think the mood has changed to these are products that came out in the eighties and the nineties. I think the, the window for that, uh, as a purely proprietary offer has closed. I think what we're going to see is the continued growth and the rise potential. You have new entrants that are fundamentally [00:22:00] open source, but yet that maintain, as I said earlier, a healthy dialogue with industry because it doesn't mean, for example, in the art world there are companies that build very successful commercial products around are there is a product called r studio that is a development environment for analysis in our, and that's a company, there's a company called I think revolution analytics. Speaker 6: I think they built some sort of sort of large scale backend high-performance version of our, I don't know the details, I don't use it, but I've seen their website. I think they're a large company that builds kind of our for the enterprise. So I think [00:22:30] that's what we're going to see moving forward at the base. People want the base technology, the base language to be open source. And I think for us as universities and for me as a scientist, I think that's a Tenet I'm not willing to compromise on because I do not want a result that I obtain or result that I published or a tool that I educate my students with to have a black box that I'm legally prevented from opening and to tell my student, well, this is a result about nature, but you can't understand how it was achieved because you are legally prevented from opening the box. [00:23:00] I think that is fundamentally unacceptable. But what is, I think a perfectly sensible way forward, is to have these base layers that are open on top of which domain specific tools can be created by industry that add value for specific problems, for specific domains that may be add performance, whatever. Catherine Carson and Fernando Perez. Thanks very much for coming on spectrum. Thanks for having us here. Thanks much. Speaker 8: [inaudible]Speaker 9: [00:23:30] all spectrums. Past shows are archived on iTunes university. We've created a simple link for you. The link is tiny url.com/k Speaker 1: a l x Speaker 8: spectrum Speaker 1: Rick Curtis Skin. I will present a few of the science and technology events [00:24:00] happening locally over the next two weeks. Speaker 10: Counter culture, labs and pseudo room present gravitational waves, results and implications with Bicep to collaborator Jamie Tolan at the pseudo room, hackerspace to one 41 Broadway in Oakland on Sunday, April 27th at 7:00 PM recently, scientists from the Bicep to experiment recorded their data findings demonstrating [00:24:30] evidence of gravitational waves that may imply cosmic inflation. The bicep to experiment is an international collaboration of research and technology from many institutions including a team at Stanford University work. Jamie Tolan works. Jamie will discuss the results of the bicep two experiment and its scientific contribution to current theories that attempt to explain the why, what and how of our universe. The event will be free. Speaker 1: On April 30th UCLA professor [00:25:00] of geography, Jared diamond will give this year's Horace m Albright Lecture in conversation. Diamond is best known for his Pulitzer Prize winning book, guns, germs and steel and this lecture he will discuss his newest book, the world until yesterday, what we can learn from traditional societies. The book is about how traditional peoples differ from members of modern industrial societies and their reactions to danger. He will then produce B in a question answer session with the audience doors open at 6:00 PM [00:25:30] the event is free and open to the public on a first come first served basis will be held Wednesday, April 30th from seven to 8:30 PM in the International House Auditorium at two two nine nine Piedmont Avenue Berkeley. Speaker 10: The theme of Mays science at the theater is science remix. Joined Berkeley lab scientists at the East Bay Center for the Performing Arts in Richmond, California on May 1st at 7:00 PM they'll discuss how discovery [00:26:00] happens. Help you show what science means to you and reveal why science can be as personal as you want it to be. Light refreshments will be served, but bring your imagination and participate at this free event. Speaker 1: A feature spectrum is to present new stories about science that we find particularly interesting. Rick Carnesi joins me in presenting the news. Speaker 10: Nature News reported on April 13th that a team of scientists from [00:26:30] Caltech have estimated that Mars's atmosphere was probably never thick enough to keep temperatures on the planet surface above freezing for very long. Edwin kite now at Princeton used from the Mars reconnaissance orbiter to catalog more than 300 craters and an 84,000 square kilometer area near the planets equator. The sizes of the creators were compared to computer models with varying atmospheres. Dance [00:27:00] or atmospheres would have broken up small objects as they do on earth, but the high frequency of smaller craters on Mars suggest the upper limit of atmospheric pressure on Mars was only one or two bar. This most likely means a temperatures on Mars have typically been below freezing. Did the team notes that their findings do allow the possibility of scenarios of Mars having a slightly thicker atmosphere at times. Do you perhaps to volcanic activity or gas is released by the large impact events and these could have [00:27:30] made Mars warmer for decades or centuries at a time, allowing water to flow. Then Speaker 1: science daily reports one of the first social science experiments to rest on. Big Data has been published in plus one. A chair of investigators from Simon Fraser University analyzed when humans start to experience and age-related decline in cognitive motor skills. The researchers analyze the digital performances of over 3000 starcraft two players, age 16 to 44 starcraft two is a ruthless intergalactic computer [00:28:00] game that players often undertake to win serious money. Their performance records, which can be easily accessed, represent thousands of hours worth of strategic real time. Cognitive based moves performed at various skill levels using complex statistical modeling. Researchers distilled meaning from this colossal compilation of information about how players responded to their opponents and more importantly, how long they took to react after around 24 years of age, players show slowing and a measure of cognitive speed that is known to be important for performance. [00:28:30] Explains Joe Thompson lead author of the study. This cognitive performance decline is present even at higher levels of skill, but there's a silver lining in this earlier than expected slippery slope into old age. Thompson says older players, those slower seem to compensate by employing simpler strategies and using the games interface more efficiently. The younger players enabling them to retain their skill despite cognitive motor speed losses. These findings says Thompson suggests that our cognitive motor capabilities are not stable across our adulthood, but are constantly [00:29:00] in flux and that our day to day performance is a result of the constant interplay between change and adaptation. Speaker 2: [inaudible]Speaker 11: and music heard during this show was written and produced by Alex Simon. Today's interview was edited by Rene Rau. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email or email [00:29:30] address is spectrum dot kalx@yahoo.com join us in two weeks at this same tone. [inaudible]. See acast.com/privacy for privacy and opt-out information.
Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Mm MM. Speaker 3: Uh Huh [inaudible]. Speaker 4: [00:00:30] We'll come to spectrum the science and technology show on Katie l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events. Speaker 3: [inaudible].Speaker 1: Hello and good afternoon. My name is Renee Rao and I'll be hosting today's show this week [00:01:00] on spectrum present part two of our two part series on big data at cal. The Berkeley Institute for data science bids is only four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez. They are today's guest Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley for Nana Perez is a research scientist at the Henry H. Wheeler [00:01:30] Jr Brain imaging center at UC Berkeley. He created the iPod iPhone project while he was a graduate student in 2001 and continues to lead the project today. In part two they talk about teaching data science. Brad Swift conducts the interview Speaker 5: on the teaching side of things. Does data science just fold into the domains in the fields and some faculty embrace it, others don't. How does the teaching of data science move [00:02:00] forward at an undergraduate level? Yeah, there there've been some really interesting institutional experiments in the last year or two here at Berkeley. Thinking about last semester, fall of 2013 stat one 57 which was reproducible collaborative data science pitched at statistics majors simply because you have to start with the size that can fit in a classroom [00:02:30] and training students in the practices of scientific collaboration around open source production of software tools or to look at what was Josh Bloom's course, so that's astro four 50 it's listed as special topics in astrophysics just because Josh happens to be a professor in the astronomy department and so you have to list it somewhere. The course is actually called Python for science Speaker 6: [00:03:00] and it's a course that Josh has run for the last, I think this is, this was its fourth iteration and that course is a completely interdisciplinary course that it's open to students in any field. The examples really do not privilege and the homework sets do not privilege astronomy in any way and we see students. I liked her a fair bit in that course as a guest lecture and we see students from all departments participating. This last semester it was packed to the gills. We actually had problems because we couldn't find a room large enough to accommodate. So word of mouth is working. In terms of students finding these [00:03:30] courses, Speaker 5: it's happening. I wouldn't say it's working in part because it's very difficult to get visibility across this campus landscape. I am sure there are innovations going on that even the pis and bids aren't aware of and one of the things we want to do is stimulate more innovation in places like the the professional schools. We'll be training students who need to be able to use these tools as well. What do they have in mind or there [00:04:00] are other formats of instruction beyond traditional semester courses. What would intensive training stretched out over a much shorter time look like? What gaps are there in the undergraduate or graduate curriculum that can effectively be filled in that way? The Python bootcamp is another example of this that's been going on for Speaker 6: for about four years. Josh and I teach a a bootcamp on also python for data science that is immediately before the beginning of the fall semester. Literally the weekend before [00:04:30] and it's kind of, it's a prerequisite for the semester long course, but it's three days of intensive hands-on scientific bite on basically programming and data analysis and computing for three days. We typically try to get a large auditorium and we got 150 to 200 people. A combination of undergrads, Grad Students, postdocs, folks from LVL campus faculty and also a few folks from industry. We always leave, leave a few slots available for people from outside the university to come and that one a has been very popular at [00:05:00] tends to, it's intense to have very good attendance be, it serves as an on ramp for the course because we advertise the in the semester course during the bootcamp and that one has been fairly successful so far and I think it has worked well. Speaker 6: We see issues with it too. That would be that we would like to address three days is probably not enough. Um, it means because it's a single environment, it means that we have to have examples that are a little bit above that can accommodate everyone, but it means they're not particularly interesting for any one group. It would be, I think it would be great to have [00:05:30] things of this nature that might be a little bit better focused at the life sciences and the social sciences that the physical sciences, so that the examples are more relevant for a given community that may be better targeted at the undergraduate and the graduate level so that you can kind of select a little bit in tune the requirements or the methodological base a little bit better to the audience. But so far we've had to kind of bootstrapping with what we have. Speaker 6: There's another interesting course on campus offered by the ice school by Raymond Lecture at the high school called working with open data [00:06:00] that is very much aimed at folks who are the constituency of the high school that have an intersection of technical background with a broader interdisciplinary kind of skills that are the hallmark of the high school and they work with openly available data sets that are existing on the Internet to create basically interesting analysis projects out of them and that's of course that that I've seen come up with some very, very successful and compelling projects at the end of the semester Speaker 7: about the teaching and preparation in universities. In [00:06:30] the course of doing interviews on spectrum, a number of people have said that really the only way to tackle sciences interdisciplinary, the big issues of science is with an interdisciplinary approach, but that that's not being taught in universities as the way to do science. Sarah way to break that down using data science as a vehicle. Speaker 5: I can speak about that as a science and technology studies scholar. The practice of interdisciplinarity, what makes it actually work is one of the [00:07:00] the most challenging social questions that can be asked of contemporary science and adding into that the fact that scientists get trained inside this existing institution that we've inherited from let's roughly say the Middle Ages with a set of disciplines that have been in their current form since roughly the late 19th century. That is the interface where I expect in the next oh two to five decades major transformations in research universities. [00:07:30] We don't yet know what an institution or research institution will look like that does not take disciplines as it sort of zero order ground level approximation to the way to encapsulate truth. But we do see, and I think bids is like data science in general and an example of this. We do see continual pressure to open up the existing disciplines and figure out how to do connections across them. It's [00:08:00] not been particularly easy for Berkeley to do that in part because of the structure of academic planning at our institution and in part because we have such disciplinary strengths here, but I think the invitation for the future that that word keeps coming back invitation. The invitation for the future for us is to understand what we mean by practicing interdisciplinarity and then figure out how to hack the institution so that it learns how to do it better. [inaudible] Speaker 8: [inaudible] [00:08:30] you're listening to structure fun. K A, l ex Berkeley Fasten Kirsten and Fernando Perez are our guests. They're part of the Berkeley Institute for Data Science for Bids [inaudible] Oh, Speaker 6: it seems that data science has an almost unlimited [00:09:00] application. Are there, are you feeling limits? I don't know about limits specifically because I think in principle almost any discipline can have some of its information and whatever the concepts and constructs of that discipline can probably be represented in a way that is amicable to quantitative analysis of some sort. In that regard, probably almost any discipline can have a data science aspect to it. I think it's important not to sort of [00:09:30] over fetishize it so that we don't lose sight of the fact that there's other aspects of intellectual work in all disciplines that are still important. That theory still has a role. That model building still has a role that, uh, knowing what questions to ask, it's still important that hypotheses still matter. I'm not so sure that it's so much an issue of drawing arbitrary limits around it, but rather of being knowledgeable and critical users of the tools and the approaches that are offered. Speaker 6: Because in terms of domain [00:10:00] applications, I actually recently saw at the strata conference, which is one of these more industry oriented big data conferences that took place a few weeks ago in Silicon Valley. It's in Santa Clara. One of the best talks that I saw at the conference was an analysis half the poem, if I told him that Gertrude Stein wrote about Picasso After Picasso painted this very famous portrait of her. And that poem has a very, very repetitive rhythmic structure. It has very few words and it's a long poem with a very peculiar linguistic structure. And [00:10:30] this hardest, I, I'm blanking on his name right now, but he's an artist who works kind of at the intersection of digital arts in, in linguistics wrote basically a custom one off visual analysis and visualization tool to work on the structure of this poem to visualize it, to turn it into music. Speaker 6: And it was a beautiful talk. It was a beautiful and very interesting talk and this was kind of the exact opposite of this was tiny data. This was one poem and in fact during the Q and a they asked him and he said, well I've tried to use the tool [00:11:00] on a few other things and there's a few songs in hip hop that it works well with, but it's almost, it's almost custom made for this one poem, right? So this was sort of tiny data, completely non generalizable and yet I thought it was fascinating and beautiful talk. So that's kind of an example that I would have never have thought of as as data science. Any examples of misapplication? Speaker 5: I think we can admit that data science is a buzzword that is [00:11:30] exactly through, it's almost indefinable nature creates space for people to do methodologically problematic and in many cases also uninteresting work. Just throwing data into an analysis without asking is this the right analysis will get you stupid or misleading answers. It's the garbage in out principle. So yeah, like any intellectual tool in the toolkit, [00:12:00] there are misleading conclusions that can be drawn and one of the powers that Berkeley brings to this effort in data science is a focus on the methodology, the intelligent development of methodology along with just building things that look like tools on their own. I think that's going to be the place with the sweet spot for universities because of the emphasis on rigor and stringency and reasoning [00:12:30] along with just getting out results that look good and are attractive Speaker 7: with data science. Are there infrastructure challenges that are worth talking about either in industry or at an academic institution? Because I know that computing power now through Amazon, Google organizations like that are enormous and so industry is sort of giving up the idea of having their own [00:13:00] computational capacity and they're using cloud virtual universities I would think are following suit. Speaker 6: Yes, there is work being done already on campus in that regard. We've had some intersection with those teams. The university right now, uh, we've had since last year a new CIO on campus, Larry Conrad, who's been spearheading an effort to sort of reimagine what the research computing infrastructure for campus should look like. [00:13:30] Considering these questions precisely of what is happening in industry, what are the models that are successfully being used at other institutions to provide larger scales off competitional resources across all disciplines and beyond the disciplines that have been traditionally the ones that have super computers. Well, there's a long history of departments, again, like physics, like competition, fluid dynamics, teams like quantum chemistry teams that have had either their own clusters or that have large budgets who have access to the supercomputing centers at [00:14:00] the doe labs and things of that nature. But as we've been saying today, all of a sudden those needs are exploding across all disciplines and the usage patterns are changing and that often what is the bottleneck is maybe not the amount of raw compute power, but the ability to operate over a very large data sets, so maybe storage is the issue or maybe throughput biologists often end up buying computers that look really weird. Speaker 6: Too many supercomputing centers because they, the actual things that they need are skewed in a different way and so there are certainly [00:14:30] challenges in that regard when we do know that Berkeley is right now at least in the midst of making a very concerted and serious attempt at at least taking a step forward on this problem. Speaker 7: A lot of data is derived from personal information. Are there privacy concerns that you have [inaudible] Speaker 5: they're all quite definitely in so many different ways that the input of experts who have thought about questions of consent, of privacy, [00:15:00] of the challenges around keeping de identified data d identified when it is possible through analytics to understand what patterns are emerging from them that is going to be so key. Especially to working with social data. And so one of the still open questions for all of us working with data that is about people is how to develop the practices that will do the protections necessary [00:15:30] in order to avoid the kinds of catastrophic misuses and violations of privacy that many of us do. Fear will be coming our way as so much data becomes available so fast with so many invitations to just make use of it and worry about the consequences later. That's not the responsible way forward. And I would like to see bids and Berkeley take on that challenge as part of its very deliberate agenda. Speaker 8: [00:16:00] Okay. Spectrum is a public affairs show on k a l ex Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about institutional reactions to bids. Oh, Speaker 7: are there any impediments that you've run into within the bids process [00:16:30] of getting up and running? Cause it's been going since, uh, Speaker 5: it's not been going on that long as it, it's only December of 2013. Pretty recent, but I'm sure there's gotta be some institutional pushback or no, it's, it's been incredible actually how much support the institution has given. What bids is though, is a laboratory for the kind of collaboration that we're trying to instantiate. And so you have 13 brilliant Co-pi eyes each with their own vision and figuring out where [00:17:00] the intersection is and how to get the different sets of expertise and investments where they, where those intersections lie and how to get them aligned. I mean, that's, that's one of the fascinating challenges in front of beds as a laboratory in the small, for the process at large that we're trying to do Speaker 7: on the tools and programming side. How would you break up what languages are providing, what kind of capability, [00:17:30] and are there new languages that are ascendent and other languages that are languages that are losing their grip? I'm sort of curious. It's a, it's another trivia questions that I think might have some interest for people. No, I think there's, there's clearly an ascendance. I think naturally the expansion of the surface of people interested in these problems Speaker 6: is naturally driving the growth and importance of high level languages that are immediately usable by domain scientists. We're not full time programmers [00:18:00] and professional programmers. Traditionally a lot of the high end computing had been done in languages like c, c plus plus for trend and some Java that are languages that tend to be more the purview of, of people who do lots of software development. And a lot of that did happen in departments like physics and chemistry and computer science, but not so much in other disciplines. And so we're seeing the rise of open source languages like Python and r that are immediately applicable and easy to use for data analysis where a few commands [00:18:30] can load a file, compute some statistics on it, produce a few visualizations, and you can do that in five lines of code, not having to write a hundred or 500 lines of c plus plus. Speaker 6: Right. And so the languages like that are, they're not new. Both I think are came out in the late eighties early nineties python came out in 1991 but they're seeing a huge amount of growth in recent years for this reason. There's also a growth of either new tools to extend these languages [00:19:00] or new languages as well. Tools for example, that connect these languages to databases or extensions to these languages to couple them to databases in better ways so that people don't have to only write raw sequel, which SQL is not the classic language for interacting with databases, so extensions to couple existing languages to database back ends. A lot of work is being done in that direction and there are some novel languages. For example, there's a team at MIT that about two years ago started [00:19:30] a project for a new language called Julia that is aimed at numerical computing, but it's sort of re-imagining. Speaker 6: What would you do if you wanted to create a language like python with the strengths of language like python or Ruby or r, but if you were doing that today with the lessons of the last 20 years, that would be good for numerical computing, but it would be easy to use for domain scientists. That would be high level, that would be interactive, that would feel like a scripting tool, but that would also give you very high performance. [00:20:00] If you had the the last 20 years of lessons and the advances in some of the underlying technology and improved compiler machinery that we have today, how would you go about that problem? And I think the Giulia team at MIT is making rapid progress and it has caught the intention of people in the statistics community of people in the numerical analysis and algorithms community. Some prominent people have become very interested in how to become active participants in its development. Speaker 6: So we're seeing both mature tools like python and are growing in their strength and and their importance. At the latest Strada Conference, [00:20:30] for example, there was a an analysis of kind of the the abstracts submitted that had r and python in their names versus things like excel or sequel or Java and Python and are clearly dominating that space, but also these, these kinds of more novels, sort of research level languages that whose futures still not clear because they're very, very young, but at least they're exploring sort of the frontier of what will we do in the next five or 10 years. And is this an area that's ripe for a commercial software creators who develop [00:21:00] a tool that would be specific to data science and sort of the same way that Mat lab is kind of specific now it's kind of a generic tool for mathematics. Obviously my answer here is extremely biased, but I'm, I sort of think that the space for a, the window to create a proprietary data science language is closed already. Speaker 6: I think the community simply would not adopt a new one. There are some existing successful ones such as mat lab, IDL, which is smaller than Madlib. It is widely used in the astronomy and astrophysics. [00:21:30] And Physics Communities Mathematica, which is a project that came out of the mathematics and physics world and that is very, very sophisticated and interesting. Maple, which is also a mathematics language. Those are successful existing proprietary languages. I think the mood has changed to these are products that came out in the eighties and the nineties. I think the, the window for that, uh, as a purely proprietary offer has closed. I think what we're going to see is the continued growth and the rise potential. You have new entrants that are fundamentally [00:22:00] open source, but yet that maintain, as I said earlier, a healthy dialogue with industry because it doesn't mean, for example, in the art world there are companies that build very successful commercial products around are there is a product called r studio that is a development environment for analysis in our, and that's a company, there's a company called I think revolution analytics. Speaker 6: I think they built some sort of sort of large scale backend high-performance version of our, I don't know the details, I don't use it, but I've seen their website. I think they're a large company that builds kind of our for the enterprise. So I think [00:22:30] that's what we're going to see moving forward at the base. People want the base technology, the base language to be open source. And I think for us as universities and for me as a scientist, I think that's a Tenet I'm not willing to compromise on because I do not want a result that I obtain or result that I published or a tool that I educate my students with to have a black box that I'm legally prevented from opening and to tell my student, well, this is a result about nature, but you can't understand how it was achieved because you are legally prevented from opening the box. [00:23:00] I think that is fundamentally unacceptable. But what is, I think a perfectly sensible way forward, is to have these base layers that are open on top of which domain specific tools can be created by industry that add value for specific problems, for specific domains that may be add performance, whatever. Catherine Carson and Fernando Perez. Thanks very much for coming on spectrum. Thanks for having us here. Thanks much. Speaker 8: [inaudible]Speaker 9: [00:23:30] all spectrums. Past shows are archived on iTunes university. We've created a simple link for you. The link is tiny url.com/k Speaker 1: a l x Speaker 8: spectrum Speaker 1: Rick Curtis Skin. I will present a few of the science and technology events [00:24:00] happening locally over the next two weeks. Speaker 10: Counter culture, labs and pseudo room present gravitational waves, results and implications with Bicep to collaborator Jamie Tolan at the pseudo room, hackerspace to one 41 Broadway in Oakland on Sunday, April 27th at 7:00 PM recently, scientists from the Bicep to experiment recorded their data findings demonstrating [00:24:30] evidence of gravitational waves that may imply cosmic inflation. The bicep to experiment is an international collaboration of research and technology from many institutions including a team at Stanford University work. Jamie Tolan works. Jamie will discuss the results of the bicep two experiment and its scientific contribution to current theories that attempt to explain the why, what and how of our universe. The event will be free. Speaker 1: On April 30th UCLA professor [00:25:00] of geography, Jared diamond will give this year's Horace m Albright Lecture in conversation. Diamond is best known for his Pulitzer Prize winning book, guns, germs and steel and this lecture he will discuss his newest book, the world until yesterday, what we can learn from traditional societies. The book is about how traditional peoples differ from members of modern industrial societies and their reactions to danger. He will then produce B in a question answer session with the audience doors open at 6:00 PM [00:25:30] the event is free and open to the public on a first come first served basis will be held Wednesday, April 30th from seven to 8:30 PM in the International House Auditorium at two two nine nine Piedmont Avenue Berkeley. Speaker 10: The theme of Mays science at the theater is science remix. Joined Berkeley lab scientists at the East Bay Center for the Performing Arts in Richmond, California on May 1st at 7:00 PM they'll discuss how discovery [00:26:00] happens. Help you show what science means to you and reveal why science can be as personal as you want it to be. Light refreshments will be served, but bring your imagination and participate at this free event. Speaker 1: A feature spectrum is to present new stories about science that we find particularly interesting. Rick Carnesi joins me in presenting the news. Speaker 10: Nature News reported on April 13th that a team of scientists from [00:26:30] Caltech have estimated that Mars's atmosphere was probably never thick enough to keep temperatures on the planet surface above freezing for very long. Edwin kite now at Princeton used from the Mars reconnaissance orbiter to catalog more than 300 craters and an 84,000 square kilometer area near the planets equator. The sizes of the creators were compared to computer models with varying atmospheres. Dance [00:27:00] or atmospheres would have broken up small objects as they do on earth, but the high frequency of smaller craters on Mars suggest the upper limit of atmospheric pressure on Mars was only one or two bar. This most likely means a temperatures on Mars have typically been below freezing. Did the team notes that their findings do allow the possibility of scenarios of Mars having a slightly thicker atmosphere at times. Do you perhaps to volcanic activity or gas is released by the large impact events and these could have [00:27:30] made Mars warmer for decades or centuries at a time, allowing water to flow. Then Speaker 1: science daily reports one of the first social science experiments to rest on. Big Data has been published in plus one. A chair of investigators from Simon Fraser University analyzed when humans start to experience and age-related decline in cognitive motor skills. The researchers analyze the digital performances of over 3000 starcraft two players, age 16 to 44 starcraft two is a ruthless intergalactic computer [00:28:00] game that players often undertake to win serious money. Their performance records, which can be easily accessed, represent thousands of hours worth of strategic real time. Cognitive based moves performed at various skill levels using complex statistical modeling. Researchers distilled meaning from this colossal compilation of information about how players responded to their opponents and more importantly, how long they took to react after around 24 years of age, players show slowing and a measure of cognitive speed that is known to be important for performance. [00:28:30] Explains Joe Thompson lead author of the study. This cognitive performance decline is present even at higher levels of skill, but there's a silver lining in this earlier than expected slippery slope into old age. Thompson says older players, those slower seem to compensate by employing simpler strategies and using the games interface more efficiently. The younger players enabling them to retain their skill despite cognitive motor speed losses. These findings says Thompson suggests that our cognitive motor capabilities are not stable across our adulthood, but are constantly [00:29:00] in flux and that our day to day performance is a result of the constant interplay between change and adaptation. Speaker 2: [inaudible]Speaker 11: and music heard during this show was written and produced by Alex Simon. Today's interview was edited by Rene Rau. Thank you for listening to spectrum. If you have comments about the show, please send them to us via email or email [00:29:30] address is spectrum dot kalx@yahoo.com join us in two weeks at this same tone. [inaudible]. Hosted on Acast. See acast.com/privacy for more information.
Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Okay. [inaudible] [inaudible]. Speaker 1: Welcome to spectrum the science [00:00:30] and technology show on k a l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events and news. Speaker 3: Hi, good afternoon. My name is Brad Swift. I'm the host of today's show this week on spectrum we present part one of our two part series on big data at cal. The Berkeley Institute for Data Science or bids is only [00:01:00] four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez and they are our guests. Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr Brain imaging center at UC Berkeley. He created the ipython project while a graduate student in 2001 [00:01:30] and continues to lead the project here is part one, Catherine Carson and Fernando Perez. Welcome to spectrum. Thanks for having us and I wanted to get from both of you a little bit of a short summary about the work you're doing now that you just sort of your activity that predates your interest in data science. Speaker 4: Data Science is kind of an Ale defined term I think and it's still an open question precisely what it is, but in a certain sense all of my research has been probably under the umbrella [00:02:00] of what we call today data science since the start. I did my phd in particle physics but it was computational in particle physics and I was doing data analysis in that case of models that were competitionally created. So I've sort of been doing this really since I was a graduate student. What has changed over time is the breadth of disciplines that are interested in these kinds of problems in these kinds of tools and that have these kinds of questions. In physics. This has been kind of a common way of working on writing for a long time. Sort of the deep intersection [00:02:30] between computational tools and large data sets, whether they were created by models or collected experimentally is something that has a long history in physics. Speaker 4: How long the first computers were created to solve differential equations, to plot the trajectories of ballistic missiles. I was one of the very first tasks that's computers were created for so almost since the dawn of coats and so it's really only recently though that the size of the data sets has really jumped. Yes, the size has grown very, [00:03:00] very large in the last couple of decades, especially in the last decade, but I think it's important to not get too hung up on the issue of size because I think when we talk about data science, I like to define it rather in the context of data that is large for the traditional framework tools and conceptual kind of structure of a given discipline rather than it's raw absolute size because yes, in physics for example, we have some of the largest data sets in existence, things like what the LHC creates [00:03:30] for the Higgs Boson. Those data sets are just absolute, absurdly large, but in a given discipline, five megabytes of data might be a lot depending on what it is that you're trying to ask. And so I think it's more, it's much, much more important to think of data that has grown larger than a given discipline was used in manipulating and that therefore poses interesting challenges for that given domain rather than being completely focused on the raw size of the data. Speaker 1: I approached this from an angle that's actually complimentary to Fernando in part because [00:04:00] my job as the interim director of the social sciences data laboratory is not to do data science but to provide the infrastructure, the setting for researchers across the social sciences here who are doing that for themselves. And exactly in the social sciences you see a nice exemplification of the challenge of larger sizes of data than were previously used and new kinds of data as well. So the social sciences are starting to pick up say on [00:04:30] sensor data that has been placed in environmental settings in order to monitor human behavior. And social scientists can then use that in order to design tests around it or to develop ways of interpreting it to answer research questions that are not necessarily anticipated by the folks who put the sensors in place or accessing data that comes out of human interactions online, which is created for entirely different purposes [00:05:00] but makes it possible for social scientists to understand things about human social networks. Speaker 1: So the challenges of building capacity for disciplines to move into new scales of data sets and new kinds of data sets. So one of the ones that I've been seeing as I've been building up d lab and that we've jointly been seeing as we tried to help scope out what the task of the Berkeley Institute for data science is going to be. How about the emergence [00:05:30] of data science? Do you have a sense of the timeline when you started to take note of its feasibility for social sciences? Irrespective of physics, which has a longer history. One of the places that's been driving the conversations in social sciences, actually the funding regime in that the existing beautifully curated data sets that we have from the post World War Two period survey data, principally administrative data on top of that, [00:06:00] those are extremely expensive to produce and to curate and maintain. Speaker 1: And as the social sciences in the last only five to 10 years have been weighing the portfolio of data sources that are supported by funding agencies. We've been forced to confront the fact that the maintenance of the post World War Two regime of surveying may not be feasible into the future and that we're going to have to be shifting to other kinds of data that are generated [00:06:30] for other purposes and repurposing and reusing it, finding new ways to, to cut it and slice it in order to answer new kinds of questions that weren't also accessible to the old surveys. So one way to approach it is through the infrastructure that's needed to generate the data that we're looking at. Another way is simply to look at the infrastructure on campus. One of the launching impetuses for the social sciences data laboratory was in fact the budget cuts of 2009 [00:07:00] here on campus. When we acknowledged that if we were going to support cutting edge methodologically innovative social science on this campus, that we were going to need to find ways to repurpose existing assets and redirect them towards whatever this new frontier in social science is going to be. Speaker 5: You were listening to spectrum on k a l x Berkeley, Catherine Carson and Fernando Perez, our guests. [00:07:30] They are part of the Berkeley Institute for data science known as big [inaudible]. Speaker 4: Fernando, you sort of gave us a generalized definition of data science. Do you want to give it another go just in case you evoke something else? Sure. I want to leave that question slightly on answer because I feel that to some extent, one of the challenges we have as an intellectual effort that we're trying to tackle at the Brooklyn [00:08:00] instead for data science is precisely working on what this field is. Right. I don't want to presuppose that we have a final answer on this question, but at least we, we do know that we have some elements to frame the question and I think it's mostly about an intersection. It's about an intersection of things that were being done already on their own, but that were being done often in isolation. So it's the intersection of methodological work whereby that, I mean things like statistical theory, applied mathematics, computer science, [00:08:30] algorithm development, all of the computational and theoretical mathematical machinery that has been done traditionally, the questions arising from domain disciplines that may have models that may have data sets, that may have sensors that may have a telescope or that may have a gene sequencing array and where are they have their own theoretical models of their organisms or galaxies or whatever it is and where that data can be inscribed and the fact that tools need to be built. Speaker 4: Does data doesn't get analyzed by blackboards? Those data gets analyzed by software, but this is software that is deeply woven [00:09:00] into the fabric of these other two spaces, right? It's software that has to be written with the knowledge of the questions and the discipline and the domain and also with the knowledge of the methodology, the theory. It's that intersection of this triad of things of concrete representation in computational machinery, abstract ideas and methodologies and domain questions that in many ways creates something new when the work has to be done simultaneously with enough depth and enough rigor on all [00:09:30] of these three directions and precisely that intersection is where now the bottleneck is proving to be because you can have the ideas, you can have the questions, you can have the data, you can have the the fear m's, but if you can't put it all together into working concrete tools that you can use efficiently and with a reasonably rapid turnaround, you will not be able to move forward. You will not be able to answer the questions you want to answer about your given discipline and so that embodiment of that intersection is I think where the challenge is opposed. Maybe there is something new called [00:10:00] data science. I'd actually like to suggest that Speaker 1: the indefinable character of data science is actually not a negative because it's an intersection in a way that we're all still very much struggling. How to define it. I won't underplay that exactly in that it's an intersection. It points to the fact that it's not an intellectual thing that we're trying to get our heads around. It's a platform for activity for doing kinds of research that are either enabled or hindered by the [00:10:30] existing institutional and social structures that the research is getting done in, and so if you think of it less as a kind of concept or an intellectual construct and more of a space where people come together, either a physical space or a methodological sharing space, you realize that the indefinable ness is a way of inviting people in rather than drawing clear boundaries around it and saying, we know what this is. It is x and not Speaker 4: why [00:11:00] Berkeley Institute for data science is that where it comes in this invitation, this collection of people and the intersection. That's sort of the goal of it. Speaker 1: That's what we've been asked to build it as not as uh, an institute in the traditional sense of there are folks inside and outside, but in the sense of a meeting point and a crossing site for folks across campus. That's [00:11:30] something that's been put in front of us by the two foundations who have invested in a significant sum of money in us. That's the Gordon and Betty Moore Foundation and the Alfred p Sloan Foundation. And it's also become an inspiring vision for those of us who have been engaged in the process over the last year and a half of envisioning what it might be. It's an attempt to address the doing of data science as an intersectional area within a research university that has existing structures [00:12:00] and silos and boundaries within it. Speaker 4: And to some extent you try to deconstruct the silos and leverage the work done by one group, share it with another, you know, the concrete mechanisms are things that we're still very much working on it and we will see how it unfolds. There's even a physical element that reflects this idea of being at a crossroads, which is that the university was willing to commit to [inaudible] the physical space of one room in the main doe library, which is not only physically [00:12:30] at the center of the university and that is very important because it does mean that it is quite literally at the crossroads. It is one central point where many of us walk by frequently, so it's a space that is inviting in that sense too to encounters, to stopping by to having easy collaboration rather than being in some far edge corner of the campus. Speaker 4: But also intellectually the library is traditionally the store of the cultural and scientific memory of an institution. And so building this space in the library is a way of signaling [00:13:00] to our community that it is meant to be a point of encounter and how specifically those encounters will be embodied and what concrete mechanisms of sharing tools, sharing coach, showing data, having lecture series, having joint projects. We're in the process of imagining all of that and we're absolutely certain that we'll make some mistakes along the way, but that is very much the intent is to have something which is by design about as openly and as explicitly collaborative as we can make it and I think [00:13:30] in that sense we are picking up on many of the lessons that Catherine and her team at the d lab have already learned because the d lab has been in operation here in Barrows Hall for about a year and has already done many things in that direction and that at least I personally see them as things in the spirit of what bids is attempting to do at the scale of the entire institution. D Lab has been kind of blazing that trail already for the last year in the context of the social sciences and to the point where their impact has actually spread beyond the social sciences because so many of the things that they were doing or were [00:14:00] found to have very thirsty customers for the particular brand of lemonade that they were selling here at the lab. And their impact has already spread beyond the social sciences. But we hope to take a lot of these lessons and build them with a broader scope. Speaker 1: And in the same way BYD sits at the center of other existing organizations, entities, programs on campus, which are also deeply engaged in data science. And some of them are research centers, others of them are the data science masters program in the School of information where [00:14:30] there is a strong and deliberate attempt to think through how in a intelligent way to train people for outside the university doing data science. So all of these centers of excellence on campus have the potential to get networked in, in a much more synergistic way with the existence of bids with is not encompassing by any means. All of the great work that's getting done in teaching research around data science on this campus Speaker 6: [00:15:00] spectrum is a public affairs show on k a l x Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about challenges in Berkeley Institute for Data Science Phase Speaker 2: [inaudible]Speaker 3: and it seems that that eScience does happen best in teams and multidisciplinary [00:15:30] teams or is that not really the case? Speaker 1: I think we've been working on that assumption in part because it seems too much to ask of any individual to do all the things at once. At the same time, we do have many specimens of individuals who cross the boundaries of the three areas that Fernando was sketching out as domain area expertise, hacking skills and methodological competence. [00:16:00] And it's interesting to think through the intersectional individuals as well. But that said, the default assumption I think is going to have to be that teamwork collaboration and actually all of the social engineering to make that possible is going to be necessary for data science to flourish. And again, that's one of the challenges of working in a research university setting where teamwork is sometimes prized and sometimes deprecated. Speaker 4: That goes back to the incentive people building tools don't necessarily get much attention, [00:16:30] prestige from that. How do you defeat that on an institutional level within the institute or just the community? Ask us in five years if we had any success. That's one of the central challenges that we have and it's not only here at Berkeley, this is actually, there's kind of an ongoing worldwide conversation happening about this every few days. There's another article where this issue keeps being brought up again and again and it's raising in volume. The business of creating tools is becoming actually an increasing [00:17:00] part of the job of people doing science. And so for example, even young faculty who are on the tenure track are finding themselves kind of pushed against the wall because they're finding themselves writing a lot of tools and building a lot of software and having to do it collaboratively and having to engage others and picking up all of these skills and this being an important central part of their work. Speaker 4: But they feel that if their tenure committee is only going to look at their publication record and [00:17:30] 80% of their actual time went into building these things, they are effectively being shortchanged for their effort. And this is a difficult conversation. What are we going to do about it? We have a bunch of ideas. We are going to try many things. I think it's a conversation that has to happen at many levels. Some agencies are beginning, the NSF recently changed the terms of its biosketch requirements for example. And now the section that used to be called relevant publications is called relevant publications and other research outcomes. And in parentheses they explained such as software [00:18:00] projects, et cetera. So this is beginning to change the community that cure rates. For example, large data sets. That's a community that has very similar concerns. It turns out that working on a rich and complex data set may be a Labor that requires years of intensive work and that'd be maybe for a full time endeavor for someone. Speaker 4: And yet those people may end up actually getting little credit for it because maybe they weren't the ones who did use that data set to answer a specific question. But if they're left in the dust, no one will do that job. Right. And so [00:18:30] we need to acknowledge that these tasks are actually becoming a central part of the intellectual effort of research. And maybe one point that is worth mentioning in this context of incentives and careers is that we as the institution of academic science in a broad sense, are facing the challenge today that these career paths and these kinds of intersectional problems and data science are right now extremely highly valued by industry. [00:19:00] What we're seeing today with this problem is genuinely of a different scale and different enough to merit attention and consideration in its own right. Because what's happening is the people who have this intersection of skills and talents and competencies are extraordinarily well regarded by the industry right now, especially here in the bay area. Speaker 4: I know the companies that are trying to hire and I know that people were going there and the good ones can effectively name their price if they can name their price to go into contexts that are not [00:19:30] boring. A lot of the problems that industry has right now with data are actually genuinely interesting problems and they often have datasets that we in academia actually have no access to because it turns out that these days the amount of data that is being generated by web activity, by Apps, by personal devices that create an upload data is actually spectacular. And some of those data sets are really rich and complex and material for interesting work. And Industry also has the resources, the computational resources, the backend, the engineering expertise [00:20:00] to do interesting work on those problems. And so we as an academic institution are facing the challenge that we are making it very difficult for these people to find a space at the university. Yet they are critical to the success of modern data driven research and discovery and yet across the street they are being courted by an industry that isn't just offering them money to do boring work. It's actually offering them respect, yes, compensation, but also respect and intellectual space and a community that values their work and that's something [00:20:30] that is genuinely an issue for us to consider. Speaker 4: Is there a way to cross pollinate between the academic side and industry and work together on building a toolkit? Absolutely. We've had great success in that regard in the last decade with the space that I'm most embedded in, which is the space of open source scientific computing tools in python. We have a licensing model for most of the tools in our space that [00:21:00] is open source but allows for a very easy industry we use and what we find is that that has enabled a very healthy two way dialogue between industry and academia in this context. Yes, industry users, our tools, and they often use them in a proprietary context, but they use them for their own problems and for building their own domain specific products and whatever, but when they want to contribute to the base tool, the base layer if you will, it's much [00:21:30] easier for them. Speaker 4: They simply make the improvements out in the open or they just donate resources. They donate money. Microsoft research last year made $100,000 donation to the python project, which was strictly a donation. This was not a grant to develop any specific feature. This was a blanket, hey, we use your tools and they help what we build and so we would like to support you and we've had a very productive relationship with them in the past, but it's by, not by no means the only one you're at Berkeley. The amp lab was two co-directors are actually part of the team [00:22:00] that is working on bids, a young story and Mike Franklin, the AMPLab has a very large set of tools for data analytics at scale that is now widely used at Twitter and Facebook and many other places. They have industry oriented conferences around their tools. Now they have an annual conference they run twice per year. Large bootcamps, large fractions of their attendees come from industry because industry is using all of these tools and the am Platt has currently more of its funding [00:22:30] comes from industry than it comes from sources like the NSF. And so I think there are, there are actually very, very clear and unambiguous examples of models where the open source work that is coming out of our research universities can have a highly productive and valuable dialogue with the industry. Speaker 3: It seems like long term he would have a real uphill battle to create enough competent people with data trained to [00:23:00] quench both industry and academia so that there would be a, a calming of the flow out of academia. Speaker 4: As we've said a couple of times in our discussions, this is a problem. Uh, it's a very, very challenging set of problems that we've signed up for it, but we feel that it's a problem worth failing on in the sense that we, we know the challenges is, is a steep one. But at the same time, the questions are important enough to be worth making the effort. Speaker 6: [inaudible] [00:23:30] don't miss part two of this interview in two weeks and on the next edition of spectrum spectrum shows are archived on iTunes university. We've created a simple link for the link is tiny url.com/kalx specter. Now, if you're the science and technology events happen, Speaker 3: I mean locally over the next two weeks, [00:24:00] enabling a sustainable energy infrastructure is the title of David Color's presentation. On Wednesday, April 9th David Color is the faculty director of [inaudible] for Energy and the chair of computer science at UC Berkeley. He was selected in scientific American top 50 researchers and technology review 10 technologies that will change the world. His research addresses networks of small embedded wireless devices, planetary scale Internet services, parallel computer architecture, [00:24:30] parallel programming languages, and high-performance communications. This event is free and will be held in Satara Dye Hall Beneteau Auditorium. Wednesday, April 9th at noon. Cal Day is April 12th 8:00 AM to 6:00 PM 357 events for details. Go to the website, cal day.berkeley.edu a lunar eclipse Monday April 14th at 11:00 PM [00:25:00] look through astronomical telescopes at the Lawrence Hall of science to observe the first total lunar eclipse for the bay area since 2011 this is for the night owls among us UC students, staff and faculty are admitted. Speaker 3: Free. General admissions is $10 drought and deluge how applied hydro informatics are becoming standard operating data for all Californians is the title of Joshua Vere's presentation. On Wednesday, [00:25:30] April 16th Joshua veers joined the citrus leadership as the director at UC Merced said in August, 2013 prior to this, Dr Veers has been serving in a research capacity at UC Davis for 10 years since receiving his phd in ecology. This event is free and will be held in Soutar Dye Hall and Beneteau Auditorium Wednesday, April 16th at noon. A feature of spectrum is to present news stories we find interesting here are to. [00:26:00] This story relates to today's interview on big data. On Tuesday, April 1st a workshop titled Big Data Values and governance was held at UC Berkeley. The workshop was hosted by the White House Office of Science and Technology Policy, the UC Berkeley School of Information and the Berkeley Center for law and technology. The day long workshop examined policy and governance questions raised by the use of large and complex data sets and sophisticated analytics to [00:26:30] fuel decision making across all sectors of the economy, academia and government for panels. Speaker 3: Each an hour and a half long framed the issues of values and governance. A webcast. This workshop will be available from the ice school webpage by today or early next week. That's ice school.berkeley.edu vast gene expression map yields neurological and environmental stress insights. Dan Kraits [00:27:00] writing for the Lawrence Berkeley Lab News Center reports a consortium of scientists led by Susan Cell Knicker of Berkeley's labs. Life Sciences Division has conducted the largest survey yet of how information and code it in an animal genome is processed in different organs, stages of development and environmental conditions. Their findings paint a new picture of how genes function in the nervous system and in response to environmental stress. The scientists [00:27:30] studied the fruit fly, an important model organism in genetics research in all organisms. The information encoded in genomes is transcribed into RNA molecules that are either translated into proteins or utilized to perform functions in the cell. The collection of RNA molecules expressed in a cell is known as its transcriptome, which can be thought of as the readout of the genome. Speaker 3: While the genome is essentially [00:28:00] the same in every cell in our bodies, the transcriptome is different in each cell type and consistently changing cells in cardiac tissue are radically different from those in the gut or the brain. For example, Ben Brown of Berkeley Labs said, our study indicates that the total information output of an animal transcriptome is heavily weighted by the needs of the developing nervous system. The scientists also discovered a much broader [00:28:30] response to stress than previously recognized exposure to heavy metals like cadmium resulted in the activation of known stress response pathways that prevent damage to DNA and proteins. It also revealed several new genes of completely unknown function. Speaker 7: You can [inaudible]. Hmm. Speaker 3: The music or during the show [00:29:00] was [inaudible] Speaker 5: produced by Alex Simon. Today's interview with [inaudible] Rao about the show. Please send them to us spectrum [00:29:30] dot kalx@yahoo.com same time. [inaudible]. Hosted on Acast. See acast.com/privacy for more information.
Cathryn Carson is an Assoc Prof of History, and the Ops Lead of the Social Sciences D- Lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr. Brain Imaging Center at U.C. Berkeley. Berkeley Institute for Data Science.TranscriptSpeaker 1: Spectrum's next. Speaker 2: Okay. [inaudible] [inaudible]. Speaker 1: Welcome to spectrum the science [00:00:30] and technology show on k a l x Berkeley, a biweekly 30 minute program bringing you interviews featuring bay area scientists and technologists as well as a calendar of local events and news. Speaker 3: Hi, good afternoon. My name is Brad Swift. I'm the host of today's show this week on spectrum we present part one of our two part series on big data at cal. The Berkeley Institute for Data Science or bids is only [00:01:00] four months old. Two people involved with shaping the institute are Catherine Carson and Fernando Perez and they are our guests. Catherine Carson is an associate professor of history and associate dean of social sciences and the operational lead of the social sciences data lab at UC Berkeley. Fernando Perez is a research scientist at the Henry H. Wheeler Jr Brain imaging center at UC Berkeley. He created the ipython project while a graduate student in 2001 [00:01:30] and continues to lead the project here is part one, Catherine Carson and Fernando Perez. Welcome to spectrum. Thanks for having us and I wanted to get from both of you a little bit of a short summary about the work you're doing now that you just sort of your activity that predates your interest in data science. Speaker 4: Data Science is kind of an Ale defined term I think and it's still an open question precisely what it is, but in a certain sense all of my research has been probably under the umbrella [00:02:00] of what we call today data science since the start. I did my phd in particle physics but it was computational in particle physics and I was doing data analysis in that case of models that were competitionally created. So I've sort of been doing this really since I was a graduate student. What has changed over time is the breadth of disciplines that are interested in these kinds of problems in these kinds of tools and that have these kinds of questions. In physics. This has been kind of a common way of working on writing for a long time. Sort of the deep intersection [00:02:30] between computational tools and large data sets, whether they were created by models or collected experimentally is something that has a long history in physics. Speaker 4: How long the first computers were created to solve differential equations, to plot the trajectories of ballistic missiles. I was one of the very first tasks that's computers were created for so almost since the dawn of coats and so it's really only recently though that the size of the data sets has really jumped. Yes, the size has grown very, [00:03:00] very large in the last couple of decades, especially in the last decade, but I think it's important to not get too hung up on the issue of size because I think when we talk about data science, I like to define it rather in the context of data that is large for the traditional framework tools and conceptual kind of structure of a given discipline rather than it's raw absolute size because yes, in physics for example, we have some of the largest data sets in existence, things like what the LHC creates [00:03:30] for the Higgs Boson. Those data sets are just absolute, absurdly large, but in a given discipline, five megabytes of data might be a lot depending on what it is that you're trying to ask. And so I think it's more, it's much, much more important to think of data that has grown larger than a given discipline was used in manipulating and that therefore poses interesting challenges for that given domain rather than being completely focused on the raw size of the data. Speaker 1: I approached this from an angle that's actually complimentary to Fernando in part because [00:04:00] my job as the interim director of the social sciences data laboratory is not to do data science but to provide the infrastructure, the setting for researchers across the social sciences here who are doing that for themselves. And exactly in the social sciences you see a nice exemplification of the challenge of larger sizes of data than were previously used and new kinds of data as well. So the social sciences are starting to pick up say on [00:04:30] sensor data that has been placed in environmental settings in order to monitor human behavior. And social scientists can then use that in order to design tests around it or to develop ways of interpreting it to answer research questions that are not necessarily anticipated by the folks who put the sensors in place or accessing data that comes out of human interactions online, which is created for entirely different purposes [00:05:00] but makes it possible for social scientists to understand things about human social networks. Speaker 1: So the challenges of building capacity for disciplines to move into new scales of data sets and new kinds of data sets. So one of the ones that I've been seeing as I've been building up d lab and that we've jointly been seeing as we tried to help scope out what the task of the Berkeley Institute for data science is going to be. How about the emergence [00:05:30] of data science? Do you have a sense of the timeline when you started to take note of its feasibility for social sciences? Irrespective of physics, which has a longer history. One of the places that's been driving the conversations in social sciences, actually the funding regime in that the existing beautifully curated data sets that we have from the post World War Two period survey data, principally administrative data on top of that, [00:06:00] those are extremely expensive to produce and to curate and maintain. Speaker 1: And as the social sciences in the last only five to 10 years have been weighing the portfolio of data sources that are supported by funding agencies. We've been forced to confront the fact that the maintenance of the post World War Two regime of surveying may not be feasible into the future and that we're going to have to be shifting to other kinds of data that are generated [00:06:30] for other purposes and repurposing and reusing it, finding new ways to, to cut it and slice it in order to answer new kinds of questions that weren't also accessible to the old surveys. So one way to approach it is through the infrastructure that's needed to generate the data that we're looking at. Another way is simply to look at the infrastructure on campus. One of the launching impetuses for the social sciences data laboratory was in fact the budget cuts of 2009 [00:07:00] here on campus. When we acknowledged that if we were going to support cutting edge methodologically innovative social science on this campus, that we were going to need to find ways to repurpose existing assets and redirect them towards whatever this new frontier in social science is going to be. Speaker 5: You were listening to spectrum on k a l x Berkeley, Catherine Carson and Fernando Perez, our guests. [00:07:30] They are part of the Berkeley Institute for data science known as big [inaudible]. Speaker 4: Fernando, you sort of gave us a generalized definition of data science. Do you want to give it another go just in case you evoke something else? Sure. I want to leave that question slightly on answer because I feel that to some extent, one of the challenges we have as an intellectual effort that we're trying to tackle at the Brooklyn [00:08:00] instead for data science is precisely working on what this field is. Right. I don't want to presuppose that we have a final answer on this question, but at least we, we do know that we have some elements to frame the question and I think it's mostly about an intersection. It's about an intersection of things that were being done already on their own, but that were being done often in isolation. So it's the intersection of methodological work whereby that, I mean things like statistical theory, applied mathematics, computer science, [00:08:30] algorithm development, all of the computational and theoretical mathematical machinery that has been done traditionally, the questions arising from domain disciplines that may have models that may have data sets, that may have sensors that may have a telescope or that may have a gene sequencing array and where are they have their own theoretical models of their organisms or galaxies or whatever it is and where that data can be inscribed and the fact that tools need to be built. Speaker 4: Does data doesn't get analyzed by blackboards? Those data gets analyzed by software, but this is software that is deeply woven [00:09:00] into the fabric of these other two spaces, right? It's software that has to be written with the knowledge of the questions and the discipline and the domain and also with the knowledge of the methodology, the theory. It's that intersection of this triad of things of concrete representation in computational machinery, abstract ideas and methodologies and domain questions that in many ways creates something new when the work has to be done simultaneously with enough depth and enough rigor on all [00:09:30] of these three directions and precisely that intersection is where now the bottleneck is proving to be because you can have the ideas, you can have the questions, you can have the data, you can have the the fear m's, but if you can't put it all together into working concrete tools that you can use efficiently and with a reasonably rapid turnaround, you will not be able to move forward. You will not be able to answer the questions you want to answer about your given discipline and so that embodiment of that intersection is I think where the challenge is opposed. Maybe there is something new called [00:10:00] data science. I'd actually like to suggest that Speaker 1: the indefinable character of data science is actually not a negative because it's an intersection in a way that we're all still very much struggling. How to define it. I won't underplay that exactly in that it's an intersection. It points to the fact that it's not an intellectual thing that we're trying to get our heads around. It's a platform for activity for doing kinds of research that are either enabled or hindered by the [00:10:30] existing institutional and social structures that the research is getting done in, and so if you think of it less as a kind of concept or an intellectual construct and more of a space where people come together, either a physical space or a methodological sharing space, you realize that the indefinable ness is a way of inviting people in rather than drawing clear boundaries around it and saying, we know what this is. It is x and not Speaker 4: why [00:11:00] Berkeley Institute for data science is that where it comes in this invitation, this collection of people and the intersection. That's sort of the goal of it. Speaker 1: That's what we've been asked to build it as not as uh, an institute in the traditional sense of there are folks inside and outside, but in the sense of a meeting point and a crossing site for folks across campus. That's [00:11:30] something that's been put in front of us by the two foundations who have invested in a significant sum of money in us. That's the Gordon and Betty Moore Foundation and the Alfred p Sloan Foundation. And it's also become an inspiring vision for those of us who have been engaged in the process over the last year and a half of envisioning what it might be. It's an attempt to address the doing of data science as an intersectional area within a research university that has existing structures [00:12:00] and silos and boundaries within it. Speaker 4: And to some extent you try to deconstruct the silos and leverage the work done by one group, share it with another, you know, the concrete mechanisms are things that we're still very much working on it and we will see how it unfolds. There's even a physical element that reflects this idea of being at a crossroads, which is that the university was willing to commit to [inaudible] the physical space of one room in the main doe library, which is not only physically [00:12:30] at the center of the university and that is very important because it does mean that it is quite literally at the crossroads. It is one central point where many of us walk by frequently, so it's a space that is inviting in that sense too to encounters, to stopping by to having easy collaboration rather than being in some far edge corner of the campus. Speaker 4: But also intellectually the library is traditionally the store of the cultural and scientific memory of an institution. And so building this space in the library is a way of signaling [00:13:00] to our community that it is meant to be a point of encounter and how specifically those encounters will be embodied and what concrete mechanisms of sharing tools, sharing coach, showing data, having lecture series, having joint projects. We're in the process of imagining all of that and we're absolutely certain that we'll make some mistakes along the way, but that is very much the intent is to have something which is by design about as openly and as explicitly collaborative as we can make it and I think [00:13:30] in that sense we are picking up on many of the lessons that Catherine and her team at the d lab have already learned because the d lab has been in operation here in Barrows Hall for about a year and has already done many things in that direction and that at least I personally see them as things in the spirit of what bids is attempting to do at the scale of the entire institution. D Lab has been kind of blazing that trail already for the last year in the context of the social sciences and to the point where their impact has actually spread beyond the social sciences because so many of the things that they were doing or were [00:14:00] found to have very thirsty customers for the particular brand of lemonade that they were selling here at the lab. And their impact has already spread beyond the social sciences. But we hope to take a lot of these lessons and build them with a broader scope. Speaker 1: And in the same way BYD sits at the center of other existing organizations, entities, programs on campus, which are also deeply engaged in data science. And some of them are research centers, others of them are the data science masters program in the School of information where [00:14:30] there is a strong and deliberate attempt to think through how in a intelligent way to train people for outside the university doing data science. So all of these centers of excellence on campus have the potential to get networked in, in a much more synergistic way with the existence of bids with is not encompassing by any means. All of the great work that's getting done in teaching research around data science on this campus Speaker 6: [00:15:00] spectrum is a public affairs show on k a l x Berkeley. Our guests are Cathryn Carson and Fernando Perez. In the next segment they talk about challenges in Berkeley Institute for Data Science Phase Speaker 2: [inaudible]Speaker 3: and it seems that that eScience does happen best in teams and multidisciplinary [00:15:30] teams or is that not really the case? Speaker 1: I think we've been working on that assumption in part because it seems too much to ask of any individual to do all the things at once. At the same time, we do have many specimens of individuals who cross the boundaries of the three areas that Fernando was sketching out as domain area expertise, hacking skills and methodological competence. [00:16:00] And it's interesting to think through the intersectional individuals as well. But that said, the default assumption I think is going to have to be that teamwork collaboration and actually all of the social engineering to make that possible is going to be necessary for data science to flourish. And again, that's one of the challenges of working in a research university setting where teamwork is sometimes prized and sometimes deprecated. Speaker 4: That goes back to the incentive people building tools don't necessarily get much attention, [00:16:30] prestige from that. How do you defeat that on an institutional level within the institute or just the community? Ask us in five years if we had any success. That's one of the central challenges that we have and it's not only here at Berkeley, this is actually, there's kind of an ongoing worldwide conversation happening about this every few days. There's another article where this issue keeps being brought up again and again and it's raising in volume. The business of creating tools is becoming actually an increasing [00:17:00] part of the job of people doing science. And so for example, even young faculty who are on the tenure track are finding themselves kind of pushed against the wall because they're finding themselves writing a lot of tools and building a lot of software and having to do it collaboratively and having to engage others and picking up all of these skills and this being an important central part of their work. Speaker 4: But they feel that if their tenure committee is only going to look at their publication record and [00:17:30] 80% of their actual time went into building these things, they are effectively being shortchanged for their effort. And this is a difficult conversation. What are we going to do about it? We have a bunch of ideas. We are going to try many things. I think it's a conversation that has to happen at many levels. Some agencies are beginning, the NSF recently changed the terms of its biosketch requirements for example. And now the section that used to be called relevant publications is called relevant publications and other research outcomes. And in parentheses they explained such as software [00:18:00] projects, et cetera. So this is beginning to change the community that cure rates. For example, large data sets. That's a community that has very similar concerns. It turns out that working on a rich and complex data set may be a Labor that requires years of intensive work and that'd be maybe for a full time endeavor for someone. Speaker 4: And yet those people may end up actually getting little credit for it because maybe they weren't the ones who did use that data set to answer a specific question. But if they're left in the dust, no one will do that job. Right. And so [00:18:30] we need to acknowledge that these tasks are actually becoming a central part of the intellectual effort of research. And maybe one point that is worth mentioning in this context of incentives and careers is that we as the institution of academic science in a broad sense, are facing the challenge today that these career paths and these kinds of intersectional problems and data science are right now extremely highly valued by industry. [00:19:00] What we're seeing today with this problem is genuinely of a different scale and different enough to merit attention and consideration in its own right. Because what's happening is the people who have this intersection of skills and talents and competencies are extraordinarily well regarded by the industry right now, especially here in the bay area. Speaker 4: I know the companies that are trying to hire and I know that people were going there and the good ones can effectively name their price if they can name their price to go into contexts that are not [00:19:30] boring. A lot of the problems that industry has right now with data are actually genuinely interesting problems and they often have datasets that we in academia actually have no access to because it turns out that these days the amount of data that is being generated by web activity, by Apps, by personal devices that create an upload data is actually spectacular. And some of those data sets are really rich and complex and material for interesting work. And Industry also has the resources, the computational resources, the backend, the engineering expertise [00:20:00] to do interesting work on those problems. And so we as an academic institution are facing the challenge that we are making it very difficult for these people to find a space at the university. Yet they are critical to the success of modern data driven research and discovery and yet across the street they are being courted by an industry that isn't just offering them money to do boring work. It's actually offering them respect, yes, compensation, but also respect and intellectual space and a community that values their work and that's something [00:20:30] that is genuinely an issue for us to consider. Speaker 4: Is there a way to cross pollinate between the academic side and industry and work together on building a toolkit? Absolutely. We've had great success in that regard in the last decade with the space that I'm most embedded in, which is the space of open source scientific computing tools in python. We have a licensing model for most of the tools in our space that [00:21:00] is open source but allows for a very easy industry we use and what we find is that that has enabled a very healthy two way dialogue between industry and academia in this context. Yes, industry users, our tools, and they often use them in a proprietary context, but they use them for their own problems and for building their own domain specific products and whatever, but when they want to contribute to the base tool, the base layer if you will, it's much [00:21:30] easier for them. Speaker 4: They simply make the improvements out in the open or they just donate resources. They donate money. Microsoft research last year made $100,000 donation to the python project, which was strictly a donation. This was not a grant to develop any specific feature. This was a blanket, hey, we use your tools and they help what we build and so we would like to support you and we've had a very productive relationship with them in the past, but it's by, not by no means the only one you're at Berkeley. The amp lab was two co-directors are actually part of the team [00:22:00] that is working on bids, a young story and Mike Franklin, the AMPLab has a very large set of tools for data analytics at scale that is now widely used at Twitter and Facebook and many other places. They have industry oriented conferences around their tools. Now they have an annual conference they run twice per year. Large bootcamps, large fractions of their attendees come from industry because industry is using all of these tools and the am Platt has currently more of its funding [00:22:30] comes from industry than it comes from sources like the NSF. And so I think there are, there are actually very, very clear and unambiguous examples of models where the open source work that is coming out of our research universities can have a highly productive and valuable dialogue with the industry. Speaker 3: It seems like long term he would have a real uphill battle to create enough competent people with data trained to [00:23:00] quench both industry and academia so that there would be a, a calming of the flow out of academia. Speaker 4: As we've said a couple of times in our discussions, this is a problem. Uh, it's a very, very challenging set of problems that we've signed up for it, but we feel that it's a problem worth failing on in the sense that we, we know the challenges is, is a steep one. But at the same time, the questions are important enough to be worth making the effort. Speaker 6: [inaudible] [00:23:30] don't miss part two of this interview in two weeks and on the next edition of spectrum spectrum shows are archived on iTunes university. We've created a simple link for the link is tiny url.com/kalx specter. Now, if you're the science and technology events happen, Speaker 3: I mean locally over the next two weeks, [00:24:00] enabling a sustainable energy infrastructure is the title of David Color's presentation. On Wednesday, April 9th David Color is the faculty director of [inaudible] for Energy and the chair of computer science at UC Berkeley. He was selected in scientific American top 50 researchers and technology review 10 technologies that will change the world. His research addresses networks of small embedded wireless devices, planetary scale Internet services, parallel computer architecture, [00:24:30] parallel programming languages, and high-performance communications. This event is free and will be held in Satara Dye Hall Beneteau Auditorium. Wednesday, April 9th at noon. Cal Day is April 12th 8:00 AM to 6:00 PM 357 events for details. Go to the website, cal day.berkeley.edu a lunar eclipse Monday April 14th at 11:00 PM [00:25:00] look through astronomical telescopes at the Lawrence Hall of science to observe the first total lunar eclipse for the bay area since 2011 this is for the night owls among us UC students, staff and faculty are admitted. Speaker 3: Free. General admissions is $10 drought and deluge how applied hydro informatics are becoming standard operating data for all Californians is the title of Joshua Vere's presentation. On Wednesday, [00:25:30] April 16th Joshua veers joined the citrus leadership as the director at UC Merced said in August, 2013 prior to this, Dr Veers has been serving in a research capacity at UC Davis for 10 years since receiving his phd in ecology. This event is free and will be held in Soutar Dye Hall and Beneteau Auditorium Wednesday, April 16th at noon. A feature of spectrum is to present news stories we find interesting here are to. [00:26:00] This story relates to today's interview on big data. On Tuesday, April 1st a workshop titled Big Data Values and governance was held at UC Berkeley. The workshop was hosted by the White House Office of Science and Technology Policy, the UC Berkeley School of Information and the Berkeley Center for law and technology. The day long workshop examined policy and governance questions raised by the use of large and complex data sets and sophisticated analytics to [00:26:30] fuel decision making across all sectors of the economy, academia and government for panels. Speaker 3: Each an hour and a half long framed the issues of values and governance. A webcast. This workshop will be available from the ice school webpage by today or early next week. That's ice school.berkeley.edu vast gene expression map yields neurological and environmental stress insights. Dan Kraits [00:27:00] writing for the Lawrence Berkeley Lab News Center reports a consortium of scientists led by Susan Cell Knicker of Berkeley's labs. Life Sciences Division has conducted the largest survey yet of how information and code it in an animal genome is processed in different organs, stages of development and environmental conditions. Their findings paint a new picture of how genes function in the nervous system and in response to environmental stress. The scientists [00:27:30] studied the fruit fly, an important model organism in genetics research in all organisms. The information encoded in genomes is transcribed into RNA molecules that are either translated into proteins or utilized to perform functions in the cell. The collection of RNA molecules expressed in a cell is known as its transcriptome, which can be thought of as the readout of the genome. Speaker 3: While the genome is essentially [00:28:00] the same in every cell in our bodies, the transcriptome is different in each cell type and consistently changing cells in cardiac tissue are radically different from those in the gut or the brain. For example, Ben Brown of Berkeley Labs said, our study indicates that the total information output of an animal transcriptome is heavily weighted by the needs of the developing nervous system. The scientists also discovered a much broader [00:28:30] response to stress than previously recognized exposure to heavy metals like cadmium resulted in the activation of known stress response pathways that prevent damage to DNA and proteins. It also revealed several new genes of completely unknown function. Speaker 7: You can [inaudible]. Hmm. Speaker 3: The music or during the show [00:29:00] was [inaudible] Speaker 5: produced by Alex Simon. Today's interview with [inaudible] Rao about the show. Please send them to us spectrum [00:29:30] dot kalx@yahoo.com same time. [inaudible]. See acast.com/privacy for privacy and opt-out information.
Episode 117 Fernando Perez is (a) a former seventh-round draft pick by the Tampa Bays Rays, (b) a real-live published poet, and (c) a guest on this edition of FanGraphs Audio. Mr. Perez joins us from China, where he’s doing whatever Fernando Perez wants to do. Among the topics discussed: some writing projects on which […]
On this week's live show: Dan Price ("Bigfoot Knows Karate") + Dr. Suzy Stein and Mr. Fernando Perez (Heavy Metal's "Mark of Kings")!SUBSCRIBE ON RSS, APPLE, ANDROID, SPOTIFY, STITCHER OR THE APP OF YOUR CHOICE. FOLLOW US ON TWITTER, INSTAGRAM AND FACEBOOK. SUPPORT OUR SHOWS ON PATREON.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
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