Podcasts about courant institute

Grand événement - À la recherche d'un Avenir Commun DurableL'IA et les mathématiques pour la météorologie et la climatologieAI and math for meteorology and climatologyCollège de FranceAnnée 2024-20255 mai 2025Grand événement - AI and math for meteorology and climatology - Laure Zanna: Reshaping climate modelling with AILaure ZannaKeller Professor of Applied Mathematics, NYU CourantRésuméWhile AI has been disrupting conventional weather forecasting, we are only beginning to witness the impact of AI on long-term climate simulations. The fidelity and reliability of climate models has been limited by computing capabilities. These limitations lead to inaccurate representations of key processes such as convection, cloud, or mixing or restrict the ensemble size of climate predictions. Therefore, these issues are a significant hurdle in enhancing climate simulations and their predictions.Here, I will discuss a new generation of climate models with AI representations of unresolved ocean physics, learned from high-fidelity simulations, and their impact on reducing biases in climate simulations. The simulations are performed with operational ocean model components. I will further demonstrate the potential of AI to accelerate climate predictions and increase their reliability through the generation of fully AI-driven emulators, which can reproduce decades of climate model output in seconds with high accuracy.Laure ZannaProfessor Zanna is a climate physicist in the Department of Mathematics at the Courant Institute, and the Center for Data Science, NYU. She holds the Joseph B. Keller and Herbert B. Keller Professorship in Applied Mathematics. Her research focuses on understanding, simulating and predicting the role of the ocean in climate on local and global scales. She combines theory, numerical simulations, statistics, and machine learning to tackle a wide range of problems in fluid dynamics and climate, including turbulence, multiscale modeling, ocean heat and carbon uptake, and sea level rise. Since 2020, she is leading M²LInES, an international collaboration sponsored by Schmidt Sciences dedicated to improving climate models using scientific machine learning. In 2020, Prof Zanna received the Nicholas P. Fofonoff Award from the American Meteorological Society "for exceptional creativity in the development and application of new concepts in ocean and climate dynamics", and was the 2022 WHOI Geophysical Fluid Dynamics principal lecturer.

Na série de conversas descontraídas com cientistas, chegou a vez do Estatístico, Mestre e Doutor em Administração, com pós doutorado na respeitadíssima Courant Institute of Mathematical Sciences de Nova York, José Siqueira.Só vem!>> OUÇA (96min 02s)*Naruhodo! é o podcast pra quem tem fome de aprender. Ciência, senso comum, curiosidades, desafios e muito mais. Com o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza.Edição: Reginaldo Cursino.http://naruhodo.b9.com.br*José de Oliveira Siqueira, bacharel em Estatística pelo IME-USP e Mestre e Doutor em Administração pela FEA-USP, é Docente do Departamento de Patologia da Faculdade de Medicina da Universidade de São Paulo (USP). É também docente e orientador do mestrado e doutorado acadêmico interdisciplinar em Bioestatística da Universidade Estadual de Maringá (UEM).Fez Pós-doutorados pelo Departamento de Matemática do Courant Institute of Mathematical Sciences (CIMS) da New York University (NYU), tendo como Supervisor o professor Marco Avellaneda, ambos os pós docs com bolsa de pesquisa no exterior da FAPESP.É avaliador ad hoc FAPESP e CAPES na área de Psicometria e Econometria Financeira. Foi também coordenador e membro do Comitê de Ética de Pesquisa com Seres Humanos do Instituto de Psicologia da USP. Atualmente é representante do Departamento de Patologia na CAPPesq.Foi docente do Departamento de Administração da USP e do Departamento de Psicologia Experimental do Instituto de Psicologia da USP.Lattes: http://lattes.cnpq.br/6545534512730877*APOIE O NARUHODO!O Altay e eu temos duas mensagens pra você.A primeira é: muito, muito obrigado pela sua audiência. Sem ela, o Naruhodo sequer teria sentido de existir. Você nos ajuda demais não só quando ouve, mas também quando espalha episódios para familiares, amigos - e, por que não?, inimigos.A segunda mensagem é: existe uma outra forma de apoiar o Naruhodo, a ciência e o pensamento científico - apoiando financeiramente o nosso projeto de podcast semanal independente, que só descansa no recesso do fim de ano.Manter o Naruhodo tem custos e despesas: servidores, domínio, pesquisa, produção, edição, atendimento, tempo... Enfim, muitas coisas para cobrir - e, algumas delas, em dólar.A gente sabe que nem todo mundo pode apoiar financeiramente. E tá tudo bem. Tente mandar um episódio para alguém que você conhece e acha que vai gostar.A gente sabe que alguns podem, mas não mensalmente. E tá tudo bem também. Você pode apoiar quando puder e cancelar quando quiser. O apoio mínimo é de 15 reais e pode ser feito pela plataforma ORELO ou pela plataforma APOIA-SE. Para quem está fora do Brasil, temos até a plataforma PATREON.É isso, gente. Estamos enfrentando um momento importante e você pode ajudar a combater o negacionismo e manter a chama da ciência acesa. Então, fica aqui o nosso convite: apóie o Naruhodo como puder.bit.ly/naruhodo-no-orelo

From early inspirations to groundbreaking AI achievements, Yann's journey chronicles the rise of deep learning, the struggles for recognition, and the revolution that changed computing forever.00:09- About Yann LeCunYann is the Chief AI Scientist for Facebook AI Research (FAIR).He is also a Silver Professor at New York University on a part-time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Sciences.

On New York University Week:  How does a hula hoop work? Olivia Pomerenk, Ph. D candidate in mathematics, looks at the science. Olivia Pomerenk is a fifth-year graduate student at the Courant Institute of Mathematical Sciences at NYU, working towards a Ph.D. in mathematics after receiving a bachelor's degree in applied mathematics from Caltech. Olivia […]

On New York University Week: Does a nation stand to benefit as a whole when their national sports team succeeds on a global stage? Anasse Bari, professor at the Courant Institute of Mathematical Sciences, digs into the data. Prof. Anasse Bari is an award-winning professor of Computer Science at New York University's Courant Institute of […]

Did you like the episode? Text us a message! This month we enjoyed talking to Arjun Raj about his interdisciplinary approaches to studying biological systems Arjun shares how he almost became a truck driver and experimented with a career in an (unnamed) rock bandHe tells us about his journey from mathematics to quantitative biology and he shares thoughts about comparing biology with physics and what they can learn from each otherHe says we all struggle with how to teach creativity and how to evaluate the most promising ideas.Arjun discusses how to improve mentoring and practical auto-mentoring. He gives specific examples about learning how to peer-review papers.He recommends a narrative-based approach to science communication so that you can leave the audience with a feeling of wonderHe is inspired by Star Wars storytelling as a science communicatorHe says it's important to go through a period of really paying attention to the small detailsArjun argues that scientists have become overburdened and that increasing scope creep creates a lot of pressureHe mentioned these labs, scientists and institutionsUniversity of Pennsylvania https://www.upenn.edu/UC Berkeley https://www.berkeley.edu/Courant Institute of Mathematical Sciences, New York University https://cims.nyu.edu/dynamic/Massachusetts Institute of Technology https://www.mit.edu/Fred Kramer https://phri.njms.rutgers.edu/faculty-and-research/faculty/fred-russell-kramer/Sunjay Tyagi https://phri.njms.rutgers.edu/faculty-and-research/faculty/sanjay-tyagi/To find out more about Arjun visit these links  Raj Lab https://rajlab.seas.upenn.edu/on Twitter/X https://x.com/arjunrajlaRandom musings Blog https://rajlaboratory.blogspot.com/Tools for Science resource https://docs.google.com/document/d/1jBGk-u5auVCvI5EMaQnptMWbxHHl3ncIhKgIgEzjylw/editYou want to support our work ? Buy us a coffee ! ==> https://www.buymeacoffee.com/lonelypipetteTo find out more about Renaud and Jonathan : Twitter : https://twitter.com/LePourpre LinkedIn : https://www.linkedin.com/in/renaudpourpre/ Twitter : https://twitter.com/Epigenetique LinkedIn : https://www.linkedin.com/in/jonathanweitzman/%20 More about the soundtrack :Music by Amaria - Lovely Swindler https://soundcloud.com/amariamusique/

Follow me @samirkaji for my thoughts on the venture market, with a focus on the continued evolution of the VC landscape.This week we welcome the three co-founders of Saga Ventures: Ben Braverman, Thomson Nguyen, and Max Altman. Saga Ventures is a seed-stage investment firm that recently closed its first fund of $125M.The conversation dives into their experiences in raising their first fund, building a team, and navigating a competitive seed-stage market. The co-founders bring unique skill sets from their previous roles in operating and investing, and this episode sheds light on how they strategically combine those skills to differentiate themselves from other firms.About Ben BravermanBen Braverman is a Co-Founder and Managing Partner at Saga Ventures, a $125M venture capital fund he co-launched in March 2024 alongside Max Altman and Thomson Nguyen. At Saga, Ben focuses on early-stage investments, working with pre-seed and seed-stage companies across various sectors. His background in scaling companies' go-to-market strategies provides valuable insight into helping startups grow efficiently and sustainably.Before founding Saga Ventures, Ben spent nearly nine years at Flexport, a major player in the logistics space. Starting as Chief Revenue Officer in 2014, he was instrumental in building and scaling Flexport's global sales and go-to-market teams. Later, as Chief Customer Officer, Ben oversaw customer relationships and corporate development, ensuring the company's growth aligned with customer needs. His final role at Flexport saw him leading Flexport Ventures and Corporate Development, where he focused on the company's strategic investments.Earlier in his career, Ben held growth and sales leadership positions at startups like URX, which was acquired by Pinterest, and Heyzap, acquired by RNTS Media. He holds a degree from Vassar College and has spent his career helping innovative companies grow through a hands-on approach to business development and customer engagement.About Thomson NguyenThomson Nguyen is a Co-Founder and Managing Partner at Saga Ventures, where he has been since March 2024. At Saga, he focuses on early-stage investments in technology-driven companies, drawing on his extensive experience in data science, machine learning, and entrepreneurship. Thomson's deep technical expertise helps him identify promising startups, especially those at the intersection of technology and business.Prior to Saga, Thomson founded Nearside, a financial services platform for small businesses, which he led from 2019 until its acquisition by Plastiq in 2022. Before that, he was an Entrepreneur in Residence at Kleiner Perkins and the Head of Capital Data Science at Square, where he managed the data science team responsible for critical business areas like default risk, marketing optimization, and product innovation. His career in fintech is rooted in his work at Framed Data, a startup he founded and later sold to Square.Thomson started his career as a data scientist at tech companies like Lookout and Causes, where he applied his expertise to user segmentation and predictive analytics. He also has a longstanding academic affiliation with New York University's Courant Institute, where he continues to contribute to research in machine learning and cybersecurity. Thomson holds degrees in Applied Mathematics from the University of Cambridge and Mathematics from the University of California, Berkeley.About Max AltmanMax Altman is a Co-Founder and Managing Partner at Saga Ventures, a venture capital fund he helped establish in March 2024. Max focuses on investing in pre-seed and seed-stage companies, working closely with his co-founders to identify and support high-potential startups. His experience as both an investor and operator allows him to bridge the gap between capital and company-building.Before co-founding Saga Ventures, Max was a Partner at Alt Capital from 2021 to 2024, where he invested in early-stage companies. Prior to that, he held a similar role at Apollo Projects, another investment firm focused on startups. His career as an investor began at Hydrazine Capital, where he worked from 2016 to 2019. During his time there, Max honed his skills in evaluating high-growth tech companies and building meaningful relationships with founders.Earlier in his career, Max gained operating experience at Zenefits, where he worked in product management, and at Allston Trading as a trader. He also spent time at Microsoft as a program manager. Max holds a degree in Computer Science from Duke University and has built his career by combining his technical background with a passion for early-stage investments.In this episode, we discuss:* (01:42) The origin story of Saga Ventures, and how the co-founders decided to join forces. Max Altman shares how the idea of starting a fund came about and why he didn't want to follow a solo GP model* (03:31) The unique, complementary skill sets the team brings to the table—Ben's expertise in go-to-market strategy, Thomson's technical knowledge, and Max's investor relationships—and how this combination is designed to support early-stage founders* (04:58) Their hands-on, founder-first approach, focusing on critical areas like product development and initial hires, differentiates Saga from other early-stage firms.* (06:11) The "reality meter" and the importance of being able to take hard hits as an entrepreneur or venture firm, emphasizing how all three co-founders share this mentality* (07:50) The team reflects on the challenges of raising their first fund, including dealing with partnership risk, self-awareness, and the difficulties of convincing LPs early on without firm commitments* (10:02) The careful consideration that went into deciding the fund size of $125M, balancing capital deployment with staying competitive in seed-stage deals.* (12:00) Their fundraising process, the strategic decisions involved, and the importance of securing anchor investors before taking meetings with LPs.* (15:19) What LPs are looking for in early-stage venture firms and the role of partnership risk in their decision-making process* (17:33) Why their shared vision and complementary skill sets have aligned them for long-term success as a team, along with their commitment to focusing on specific sectors like fintech and infrastructure* (19:22) The importance of having a clear value-add for founders beyond capital, and the importance of storytelling and salesmanship in early-stage companies* (23:25) The internal decision-making process at Saga, how the partners determine which deals to pursue, and the dynamics of reaching consensus when choosing investments* (26:45) Patience and long-term thinking are critical when evaluating deals, and how they ensure they don't rush into investments just for fear of missing out* (28:19) The importance of founder resilience and self-awareness, noting that the best founders are those who can attract talent and navigate through difficult times* (30:00) Why salesmanship and charisma are critical qualities in founders, as startup leadership often requires convincing others to join and invest in challenging ventures* (32:00) The team discusses their approach to sourcing and winning deals in a highly competitive market, focusing on the importance of building trust and delivering consistent value to founders.* (34:05) Max talks about the significance of being the first firm to back companies and how they collaborate with other VCs to co-lead investments.* (36:45) Being transparent and responsive to founders creates lasting relationships, even when they pass on deals* (38:04) How they measure success internally at Saga Ventures, focusing on inputs such as responsiveness and the strength of founder relationships, while understanding that long-term results will take years to evaluate* (41:00) Key lessons from the fundraising process, stressing the importance of clarity when positioning their fund to LPs and being patient in closing commitments* (43:25) How the venture landscape has evolved over the past 18 years, highlighting the increasing competition and the need for VCs to be highly self-aware and strategic when entering deals* (45:40) Building a venture firm requires a long-term mindset, much like running a successful companyI'd love to know what you took away from this conversation with Glenn. Follow me @SamirKaji and give me your insights and questions with the hashtag #ventureunlocked. If you'd like to be considered as a guest or have someone you'd like to hear from (GP or LP), drop me a direct message on Twitter.Podcast Production support provided by Agent Bee This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ventureunlocked.substack.com

How do you balance rigorous research with open-mindedness in investing? How do you communicate effectively with clients during volatile times?This week, Ryan Detrick, Chief Market Strategist at Carson Group & Sonu Varghese, VP, Global Macro Strategist at Carson Group, chat with Cliff Asness, Managing and Founding Principal at AQR Capital Management, for an insightful discussion on market strategies and the nuances of value investing. Cliff shares his thoughts on the current state of value investing, explores the concept of 'value spread,' and even dips into some fun side topics.They discuss: The current state of value investing and why it has seen challenging periodsInsights into how AQR navigates market anomaliesThe importance of communication and transparency with clientsFun personal insights into Cliff's interests outside of finance, from hot sauce to superhero moviesAnd more!Resources:Any questions about the show? Send it to us! We'd love to hear from you! factsvsfeelings@carsongroup.com Connect with Cliff Asness: LinkedIn: Cliff AsnessX: Cliff AsnessWebsite: AQR Capital ManagementConnect with Ryan Detrick: LinkedIn: Ryan DetrickX: Ryan DetrickConnect with Sonu Varghese: LinkedIn: Sonu VargheseX: Sonu VargheseAbout Our Guest: Cliff Asness is a Founder, Managing Principal, and Chief Investment Officer at AQR Capital Management. He is an active researcher and has authored articles on a variety of financial topics for many publications, including The Journal of Portfolio Management, Financial Analysts Journal, The Journal of Finance, and The Journal of Financial Economics. He has received five Bernstein Fabozzi/Jacobs Levy Awards from The Journal of Portfolio Management in 2002, 2004, 2005, 2014, and 2015. Financial Analysts Journal has twice awarded him the Graham and Dodd Award for the year's best paper, as well as a Graham and Dodd Excellence Award, the award for the best perspectives piece, and the Graham and Dodd Readers' Choice Award. He has won the second prize of the Fama/DFA Prize for Capital Markets and Asset Pricing in the 2020 Journal of Financial Economics. In 2006, the CFA Institute presented Cliff with the James R. Vertin Award, which is periodically given to individuals who have produced a body of research notable for its relevance and enduring value to investment professionals. Prior to co-founding AQR Capital Management, he was a Managing Director and Director of Quantitative Research for the Asset Management Division of Goldman Sachs & Co. He is on the editorial board of The Journal of Portfolio Management, the governing board of the Courant Institute of Mathematical Finance at NYU, the board of directors of the Q-Group, the board of the International Rescue Committee and the board of trustees of The National WWII Museum. Cliff received a B.S. in economics from the Wharton School and a B.S. in engineering from the Moore School of Electrical Engineering at the University of Pennsylvania, graduating summa cum laude in both. He received an M.B.A. with high honors and a Ph.D. in finance from the University of Chicago, where he was Eugene Fama's student and teaching assistant for two years.

My guest is Yann LeCun, a pioneering French-American computer scientist, known for his groundbreaking work in machine learning, computer vision, and neural networks. Yann is the Silver Professor at the Courant Institute of Mathematical Sciences at New York University and serves as the Vice President and Chief AI Scientist at Meta. Yann is one of the world's most influential computer scientists. He has accumulated over 350,000 citations on Google Scholar, he is one of the founding figures in the field of deep learning thanks to its contribution to convolutional neural networks and backpropagation algorithms, and he is a vocal proponent of open source. In recognition of his significant contributions to artificial intelligence, he was awarded the Turing Award in 2018, often referred to as the “Nobel Prize of Computing.” Our conversation is structured into three distinct parts. We begin by discussing the overarching dynamics in the AI space, then narrow our focus to the firm level, and finally, we conclude with an exploration of the challenges that lie ahead. By the end of this discussion, you will learn whether open source has a chance to make it in AI, the key factors for scaling an AI foundation model, the role ecosystems play in market dynamics, Meta long term strategy in the space, how concentration among chip manufacturers impacts AI companies, the current effect of the European AI Act on AI companies, what Yann would like to see regulators doing, and more. I hope you enjoy the conversation.

Des Traynor is a Co-Founder of Intercom, and has built and led many teams within the company, including Product, Marketing, and Customer Support. Yann LeCun is VP & Chief AI Scientist at Meta and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science. He was the founding Director of FAIR and of the NYU Center for Data Science.  Emad Mostaque is the Co-Founder and CEO @ StabilityAI, the parent company of Stable Diffusion. Stability are building the foundation to activate humanity's potential. Jeff Seibert is the Founder & CEO @ Digits, building the future of AI-powered accounting. Digits have raised funding from the likes of Peter Fenton @ Benchmark and 20VC. Tomasz Tunguz is the Founder and General Partner @ Theory Ventures, just announced last week, Theory is a $230M fund that invests $1-25m in early-stage companies that leverage technology discontinuities into go-to-market advantages. Douwe Kiela is the CEO of Contextual AI, building the contextual language model to power the future of businesses. Cris Valenzuela is the CEO and co-founder of Runway, the company that trains and builds generative AI models for content creation.  Richard Socher is the founder and CEO of You.com. Richard previously served as the Chief Scientist and EVP at Salesforce. Before that, Richard was the CEO/CTO of AI startup MetaMind, acquired by Salesforce in 2016. In Today's Episode We Discuss: Foundational Models: Analysis Will foundational models become commoditized? Who are the major players? What are their different strengths? Who will win? Who will lose? How important is the size of the model vs the quality of the data? 2. Open vs Closed: What are the biggest pros and cons of an open ecosystem for LLMs? Why is it naive to think that open-source LLMs will prevail? What will determine which method wins? 3. An Analysis of the Incumbents: Why is Google the most vulnerable? What can they do to regain ground? Why is Apple the sleeping giant? How could they win the next wave of AI? What should Amazon do today to compete with Microsoft? 4. The Future: Doom and Gloom? Why is it ridiculous to assume AI systems want to dominate? Why will AI create a renaissance of creativity and human freedom? What role should regulation play in the advancement and progression of AI?

Arthur Breitman is a co-founder of Tezos. Previously, Arthur was a research engineer for Google X and Waymo. In his early career, he worked as a quantitative analyst for Goldman Sachs and Morgan Stanley. Arthur graduated from the École Polytechnique and the Courant Institute of NYU where he studied applied mathematics.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Investing is a complex and uncertain activity that requires careful analysis, discipline, and patience. There are many factors that can influence the performance of different investment strategies, such as market conditions and investor preferences.Today, we speak with a leading figure in the field.In this special edition of the Facts Vs. Feelings podcast, recorded live at the Excell conference in Nashville, Ryan Detrick & Sonu Varghese speak with Cliff Asness, Managing and Founding Principal at AQR Capital Management.Together, they chat about investment management, the importance of understanding uncertainty in the market, and the need to learn from past mistakes. They also touch on topics such as market bubbles, momentum strategies, and the parallels between decision-making in sports and investing.They discuss: The challenge of quantifying investment strategies and determining if they align with clients' expectationsA critical mistake made during the launch of his firm, AQRA definition of a bubble and examples of past bubbles in the marketCliff's journey from considering law school to becoming a quantitative finance researcher, highlighting pivotal momentsThe challenges and misconceptions surrounding momentum investingCliff's research on the optimal time to pull a hockey goalie and how it relates to investment strategiesThe parallels between sports and investingAnd more!Connect with Cliff Asness: LinkedIn: Cliff AsnessX: Cliff AsnessWebsite: AQR Capital ManagementConnect with Ryan Detrick: LinkedIn: Ryan DetrickConnect with Sonu Varghese: LinkedIn: Sonu VargheseAbout our guest: Cliff Asness is a Founder, Managing Principal, and Chief Investment Officer at AQR Capital Management. He is an active researcher and has authored articles on a variety of financial topics for many publications, including The Journal of Portfolio Management, Financial Analysts Journal, The Journal of Finance, and The Journal of Financial Economics. He has received five Bernstein Fabozzi/Jacobs Levy Awards from The Journal of Portfolio Management in 2002, 2004, 2005, 2014, and 2015. Financial Analysts Journal has twice awarded him the Graham and Dodd Award for the year's best paper, as well as a Graham and Dodd Excellence Award, the award for the best perspectives piece, and the Graham and Dodd Readers' Choice Award. He has won the second prize of the Fama/DFA Prize for Capital Markets and Asset Pricing in the 2020 Journal of Financial Economics. In 2006, the CFA Institute presented Cliff with the James R. Vertin Award, which is periodically given to individuals who have produced a body of research notable for its relevance and enduring value to investment professionals. Prior to co-founding AQR Capital Management, he was a Managing Director and Director of Quantitative Research for the Asset Management Division of Goldman Sachs & Co. He is on the editorial board of The Journal of Portfolio Management, the governing board of the Courant Institute of Mathematical Finance at NYU, the board of directors of the Q-Group, the board of the International Rescue Committee and the board of trustees of The National WWII Museum. Cliff received a B.S. in economics from the Wharton School and a B.S. in engineering from the Moore School of Electrical Engineering at the University of Pennsylvania, graduating summa cum laude in

With the debut of ChatGPT, the AI once promised in some distant future seems to have suddenly arrived with the potential to reshape our working lives, culture, politics and society. For proponents of AI, we are entering a period of unprecedented technological change that will boost productivity, unleash human creativity and empower billions in ways we have only begun to fathom. Others think we should be very concerned about the rapid and unregulated development of machine intelligence. For their detractors, AI applications like ChatGPT herald a brave new world of deep fakes and mass propaganda that could dwarf anything our democracies have experienced to date. Immense economic and political power may also concentrate around the corporations who control these technologies and their treasure troves of data. Finally, there is an existential concern that we could, in some not-so-distant future, lose control of powerful AIs who, in turn, pursue goals that are antithetical to humanity's interests and our survival as a species. Arguing for the motion is Yoshua Bengio, one of the leading worldwide experts on AI whose pioneering work in deep learning earned him the 2018 Turing Award, often referred to as “the Nobel Prize of Computing. Yoshua's debate partner is Max Tegmark, an internationally renowned cosmologist, global leader in machine learning research, and a professor at the M.I.T.  Arguing against the motion is Yann Lecun. Yann is an acclaimed computer scientist of mobile robotics and computational neuroscience, the Silver Professor of the Courant Institute of Mathematical Sciences at N.Y.U. and Vice-President, Chief AI Scientist at Meta. His debate partner is Melanie Mitchell, a bestselling author and world-leading expert in the various fields of artificial intelligence and cognitive science at the Santa Fe Institute.   The host of the Munk Debates is Rudyard Griffiths - @rudyardg.   Tweet your comments about this episode to @munkdebate or comment on our Facebook page https://www.facebook.com/munkdebates/ To sign up for a weekly email reminder for this podcast, send an email to podcast@munkdebates.com.   To support civil and substantive debate on the big questions of the day, consider becoming a Munk Member at https://munkdebates.com/membership Members receive access to our 10+ year library of great debates in HD video, a free Munk Debates book, newsletter and ticketing privileges at our live events. This podcast is a project of the Munk Debates, a Canadian charitable organization dedicated to fostering civil and substantive public dialogue - https://munkdebates.com/ Senior Producer: Ricki Gurwitz Editor: Kieran Lynch  

The birth of generative AI application ChatGPT is transforming work and life, and how we perceive ourselves. Following ChatGPT, global tech giants have presented their own generative AI tools, offering more choices for users and all the while, as some say, unleashing an AI race. While some people are awed by how much the technology can facilitate productivity, others are worried about the risks it presents. Policymakers around the world have thus begun working on regulating AI. So can AI be regulated, and how? Host Liu Kun is joined by Professor Pascale Fung, Director of Centre for Artificial Intelligence Research and Chair Professor at Department of Electronic & Computer Engineering and Department of Computer Science & Engineering, Hong Kong University of Science and Technology; Mengye Ren, Assistant Professor at Department of Computer Science and Courant Institute of Mathematical Sciences, New York University; Edward Lehman, Managing Director of LEHMAN, LEE & Xu Law Firm.

Yann LeCun is VP & Chief AI Scientist at Meta and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science. He was the founding Director of FAIR and of the NYU Center for Data Science. After a postdoc in Toronto he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as Head of Image Processing Research. He joined NYU as a professor in 2003 and Meta/Facebook in 2013. He is the recipient of the 2018 ACM Turing Award for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing". Huge thanks to David Marcus for helping to make this happen. In Today's Episode with Yann LeCun: 1.) The Road to AI OG: How did Yann first hear about machine learning and make his foray into the world of AI? For 10 years plus, machine learning was in the shadows, how did Yan not get discouraged when the world did not appreciate the power of AI and ML? What does Yann know now that he wishes he had known when he started his career in machine learning? 2.) The Next Five Years of AI: Hope or Horror: Why does Yann believe it is nonsense that AI is dangerous? Why does Yann think it is crazy to assume that AI will even want to dominate humans? Why does Yann believe digital assistants will rule the world? If digital assistants do rule the world, what interface wins? Search? Chat? What happens to Google when digital assistants rule the world? 3.) Will Anyone Have Jobs in a World of AI: From speaking to many economists, why does Yann state "no economist thinks AI will replace jobs"? What jobs does Yann expect to be created in the next generation of the AI economy? What jobs does Yann believe are under more immediate threat/impact? Why does Yann expect the speed of transition to be much slower than people anticipate? Why does Yann believe Elon Musk is wrong to ask for the pausing of AI developments? 4.) Open or Closed: Who Wins: Why does Yann know that the open model will beat the closed model? Why is it superior for knowledge gathering and idea generation? What are some core historical precedents that have proved this to be true? What did Yann make of the leaked Google Memo last week? 5.) Startup vs Incumbent: Who Wins: Who does Yann believe will win the next 5 years of AI; startups or incumbents? How important are large models to winning in the next 12 months? In what ways does regulation and legal stop incumbents? How has he seen this at Meta? Has his role at Meta ever stopped him from being impartial? How does Yan deal with that?

Nalini AnantharamanGéométrie spectraleCollège de FranceAnnée 2022-2023Séminaire - Ergodicité et thermalisation des fonctions propres : Quantum Unique Ergodicity and Random Matrices, an IntroductionIntervenant(s) : Paul Bourgade, Courant Institute, New York UniversityRésuméI will review the role of the quantum unique ergodicity (QUE) notion of delocalization, in the context of random matrices. QUE can be proved by dynamic or combinatorial methods, and implies that the local eigenvalues statistics exhibit the universal repulsion of Gaussian, invariant ensembles.We will focus in particular on the techniques involving the Dyson Brownian Motion.Applications include Wigner matrices, Erdös Rényi and d-regular random graphs, and random band matrices.

David Holland is a renowned climate scientist who recently returned from the Thwaites Glacier in Antarctica. He works in close partnership with his wife, Denise Holland, who is his manager of field and logistics. Fascinated by the Arctic since his childhood in Newfoundland and Labrador, he discusses what we know—and don't yet know—about the warming of the oceans and its threat to humankind; his shuttling between teaching and research in urban centers and intensive fieldwork in some of the most beautiful and dangerous regions of the world; and the politicization of climate science as vast changes become more of a reality. Holland is an esteemed global scientist—recently made a fellow of the American Geophysical Union—who has published over 100 peer-reviewed papers in the field of polar environmental science. At NYU, he is professor of mathematics and atmosphere/ocean science at the Courant Institute of Mathematical Sciences; director of the Environmental Fluid Dynamics Laboratory in New York City; and director of the Center for Sea Level Change at NYU Abu Dhabi.

Alexander Gorban is a Director of the Centre for Artificial Intelligence, Data Analysis and Modelling (AIDAM) and Professor of Applied Mathematics at the University of Leicester. He worked for the Russian Academy of Sciences, Siberian Branch (Krasnoyarsk, Russia), and ETH Zürich (Switzerland), was a visiting professor and research scholar at Clay Mathematics Institute (Cambridge, MA), IHES (Bures-sur-Yvette, Île de France), Courant Institute of Mathematical Sciences (New York), and Isaac Newton Institute for Mathematical Sciences (Cambridge, UK). His main research interests are dynamics of systems of physical, chemical and biological kinetics; biomathematics; machine learning, data mining and model reduction problems. A.N. Gorban has developed a family of methods for model reduction and coarse-graining. He has solved problems in gas kinetics, polymer dynamics, chemical reaction kinetics and biological kinetics. In recognition of this series of work, he received the I. Prigogine Award and medal, has been a Clay Scholar (Cambridge, MA, USA, 2000), received Fellowship of I. Newton Institute (Cambridge, UK) and Lifetime Achievement Award "in recognition of outstanding contributions to the research field of (bio)chemical kinetics", MaCKIE-2015, Ghent, Belgium, 2015. Lead Academic in three Knowledge Transfer Partnership (KTP) grants in Machine Learning and Data Mining funded by Innovate UK. A general neural networks based technology of extraction of explicit knowledge from data was developed. A system of methods is developed to reveal the hidden intelligible models in complex systems: complex datasets and complex reaction networks. FIND ALEXANDER ON SOCIAL MEDIA LinkedIn | Facebook ================================ SUPPORT & CONNECT: Support on Patreon: https://www.patreon.com/denofrich Twitter: https://twitter.com/denofrich Facebook: https://www.facebook.com/denofrich YouTube: https://www.youtube.com/denofrich Instagram: https://www.instagram.com/den_of_rich/ Hashtag: #denofrich © Copyright 2022 Den of Rich. All rights reserved.

Alexander Gorban is a Director of the Centre for Artificial Intelligence, Data Analysis and Modelling (AIDAM) and Professor of Applied Mathematics at the University of Leicester. He worked for the Russian Academy of Sciences, Siberian Branch (Krasnoyarsk, Russia), and ETH Zürich (Switzerland), was a visiting professor and research scholar at Clay Mathematics Institute (Cambridge, MA), IHES (Bures-sur-Yvette, Île de France), Courant Institute of Mathematical Sciences (New York), and Isaac Newton Institute for Mathematical Sciences (Cambridge, UK).His main research interests are dynamics of systems of physical, chemical and biological kinetics; biomathematics; machine learning, data mining and model reduction problems. A.N. Gorban has developed a family of methods for model reduction and coarse-graining. He has solved problems in gas kinetics, polymer dynamics, chemical reaction kinetics and biological kinetics. In recognition of this series of work, he received the I. Prigogine Award and medal, has been a Clay Scholar (Cambridge, MA, USA, 2000), received Fellowship of I. Newton Institute (Cambridge, UK) and Lifetime Achievement Award "in recognition of outstanding contributions to the research field of (bio)chemical kinetics", MaCKIE-2015, Ghent, Belgium, 2015.Lead Academic in three Knowledge Transfer Partnership (KTP) grants in Machine Learning and Data Mining funded by Innovate UK. A general neural networks based technology of extraction of explicit knowledge from data was developed. A system of methods is developed to reveal the hidden intelligible models in complex systems: complex datasets and complex reaction networks.FIND ALEXANDER ON SOCIAL MEDIALinkedIn | Facebook================================PODCAST INFO:Podcast website: https://www.uhnwidata.com/podcastApple podcast: https://apple.co/3kqOA7QSpotify: https://spoti.fi/2UOtE1AGoogle podcast: https://bit.ly/3jmA7ulSUPPORT & CONNECT:Support on Patreon: https://www.patreon.com/denofrichTwitter: https://www.instagram.com/denofrich/Instagram: https://www.instagram.com/denofrich/Facebook: https://www.facebook.com/denofrich

TCA methodologies that ignore partial fills “might be off by 20% to 30%”, says Petter Kolm, professor of finance and director of the Mathematics in Finance master's program at NYU's Courant Institute of Mathematical Sciences

Welcome to the Today in Manufacturing Podcast, a new podcast brought to you by the editors from Manufacturing.net and Industrial Equipment News (IEN).In each episode, we discuss the five biggest stories in manufacturing, and the implications they have on the industry moving forward. This week, we talk about:Pandemic Claims Another ManufacturerEcolab manufactures infection prevention solutions to more than 40 industries — everything from food processing plants to hospitals. This week, the St. Paul, Minnesota-based company announced plans to shutter its plant in Columbus, Mississippi.Autonomous Van Rescued After Traffic Cone-Induced MeltdownA Waymo One driverless van recently panicked when it encountered some traffic cones. The technology still has some gaps. Mexico City is SinkingGeologists think areas of the world's second largest city could sink as much as 100 feet over the next 150 years.Employee Dies in Accident at Mattress FactoryAnita Irene Coester was a 51-year-old maintenance worker at Purple Mattress in Grantsville, Utah. On May 13th, she was injured while performing a repair and died as a result of her injuries.1,600 Layoffs Coming to Jeep PlantThe fallout from the global computer chip shortage continues. Jeep is one of Stellantis's top-selling brands. Despite their popularity, 1,641 workers making Jeep Cherokees in Belvidere, IL could lose their jobs. In Case You Missed It:Shaking 1,000-Foot Skyscraper EvacuatedA 70-story skyscraper in the southern Chinese tech center of Shenzhen was evacuated after it began swaying.Nikola Tesla's Valve Could Have Applications TodayNikola Tesla invented the valvular conduit about 100 years ago, but researchers from New York University's Courant Institute of Mathematical Sciences found that the valve is not only more functional than previously realized, but has potential applications today. First Long-Haul Flight with Eco-FuelThe plane is using petroleum mixed with a synthetic jet fuel made from waste cooking oils.Please make sure to like, subscribe and share the podcast. You could also help us out a lot by giving the podcast a positive review on Apple podcast or whatever platform you use. Finally, to email the podcast, you can reach any of us at Jeff, Anna or David @ien.com, with “Email the Podcast” in the subject line.

Dr. Deborah Berebichez is a Chief Data Scientist, who made history as the first Mexican woman to graduate from Stanford University with a Ph.D. in physics. Deborah has been the co-host of Discovery Channel's "Outrageous Acts of Science" for the past eleven seasons, where she uses her physics background to explain the science behind extraordinary engineering feats. Deborah has been featured by many media outlets worldwide, including in Forbes, Wired, The New York Times, US & World News Report, and on The Travel Chanel, CNN, FOX, MSNBC, Univision, NatGeo, and many more. Deborah's work in science education and outreach has been widely recognized by The Wall Street Journal, Oprah, TED, and by organizations like the American Association for the Advancement of Science (AAAS) where she was named the IF/THEN Ambassador for inspiring and empowering young women to learn science and to improve the state of STEM education in the world.Deborah completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences. Love the show? Subscribe, rate, review, and share! https://www.calentertainment.com/virtually-speaking/

Professor Dr. Nasir Memon is Vice Dean for Academics and Student Affairs and a Professor of Computer Science and Engineering at the New York University (NYU) Tandon School of Engineering. He is a co-founder of NYU's Center for Cyber Security (CCS) at New York as well as NYU Abu Dhabi. He is an affiliate faculty at the Computer Science department in NYU's Courant Institute of Mathematical Sciences, and department head of NYU Tandon Online. He introduced cyber security studies to NYU Tandon in 1999, making it one of the first schools to implement the program at the undergraduate level. He is the founder of the OSIRIS Lab, CSAW, The Bridge to Tandon Program as well as the Cyber Fellows program at NYU. He has received several best paper awards and awards for excellence in teaching. He has been on the editorial boards of several journals, and was the Editor-In-Chief of the IEEE Transactions on Information Security and Forensics. In this podcast, Professor Memon traces the highlights in the evolution of AI technologies, and how breakthrough work in “deep” neural network powered by the rapid development in processing power led to the explosion of today’s “deepfakes”.  Giving examples of convincing “deepfakes”, he also notes the emergence of “cheapfakes” and “shallowfakes”. He points out the changing landscape as the prevalence of “deepfakes” grows, including the development of “deepfake-as-a-service” and other monetization opportunities for threat actors, and the potential of misinformation being weaponised by nation state actors as “deepfakes” are added to their cyber attacks arsenal. As the world of “deepfakes” creation and detection becomes a cat-and-mouse game, he stresses the need for going beyond passive detection to a proactive approach of embedding integrity measures for image provenance. With truth under attack as “deepfakes” technologies grow more sophisticated, he also sees the need for a societal shift towards becoming more sceptical in general and advises us: “do not jump into conclusions, look for corroborative evidence”. Recorded Singapore 17th March 2021 7.15am / New York 16th March 2021 7.15pm.  

Spencer is founder and CEO of Spark Wave, a startup foundry (a.k.a. company builder / startup studio) that creates new software companies from scratch, designed to help solve big problems in the world. He has a PhD in applied math from the Courant Institute of Mathematical Sciences at NYU, with his specialty being machine learning (sometimes referred to as “artificial intelligence”). He also has a bachelor of science degree from Columbia University, where he studied applied math and computer science. He has published papers on a variety of topics in applied math, machine learning, mental health and social science.Spencer joins me today to discuss his founder's story and the many projects that his company, Spark Wave, are working on. We learn more about Spencer's take on ethical value and how this drives him in business and life. Spencer shares how his academic background in mathematics influences the work that he does. He also tells about some of the challenges he has encountered, and advice to all inspiring founders. “Entrepreneurship is basically the world punching you in the face between 10 and 100 times, and the vast majority of people would give up after a few punches, right?” - Spencer GreenbergToday on Startups for Good we cover:Keeping track of multiple products within a companyProducts and business idea generation and evaluationWhat an effective altruist isThe type of team involved with a general studio modelHow to recruit a CEO or co-founder to pair with an ideaSpencer's new podcast - Clearer Thinking with Spencer GreenbergQuestions to ask yourself prior to becoming an entrepreneur.Net Promoter Score and Dissatisfaction Score and other metrics Connect with Spencer on Twitter and LinkedIn. For more information about sparkwave.tech and clearerthinking.org Subscribe, Rate & Share Your Favorite Episodes!Thanks for tuning into today's episode of Startups For Good with your host, Miles Lasater. If you enjoyed this episode, please subscribe and leave a rating and review on your favorite podcast listening app.Don't forget to visit our website, connect with Miles on Twitter or LinkedIn, and share your favorite episodes across social media. For more information about The Giving Circle

On this episode of Techsetters, co-hosts Samantha Wiener and Jenny Wang interview Dr. Deborah Berebichez, PhD. Debbie is the first Mexican woman to graduate from Stanford University with a PhD in Physics, and she uses her education and background to make science approachable to a wide range of audiences. Debbie co-hosts Discovery Channel’s Outrageous Acts of Science TV show, is the Chief Data Scientist at Metis where she leads the creation and growth of exceptional data science training opportunities, and complete two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences where she carried out research in the area of acoustic waves. She has recently been recognized as an AAAS IF/THEN Ambassador for inspiring and empowering young women to learn science and to improve the state of STEM education in the world. Did we mention she has worked in TWO Nobel Peace Prize Labs? Techsetters is Executive Produced by Kode With Klossy and made possible by If / Then. This episode was recorded in May 2020.

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Debbie Berebichez she is the first Mexican woman to graduate from Stanford University with a PhD in Physics, and she uses her education and background to make science approachable to a wide range of audiences. Debbie co-hosts numerous TV shows, where she uses her knowledge of physics to explain the science behind extraordinary engineering feats. Deborah is the Chief Data Scientist at Metis where she leads the creation and growth of exceptional data science training opportunities. Deborah completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences where she carried out research in the area of acoustic waves. She invented a highly effective technique in the field of wireless communications whereby a cell phone user can communicate with a desired target user in a location far away. Show Notes2:10 – Debbie’s mission statement8:58 – Mentorship11:25 - Debbie talks about Discovery TV shows15:04 – Debbie discusses her Ted Talk19:20 - Data literacy discussion30:35 – Physicist to data science 33:22 – Training and data scientist 38:43 - What makes the perfect data science implementation in a company?41:53 – Debbie’s advice for young girlsDebbie Berebichez - InstagramDebbie Berebichez - TwitterDebbie Berebichez - LinkedInDebbie Berebichez - FacebookStatistic and the art of deception Connect with the TeamProducer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.

Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. AbstractHosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.This week on Making Data Simple, we have Debbie Berebichez she is the first Mexican woman to graduate from Stanford University with a PhD in Physics, and she uses her education and background to make science approachable to a wide range of audiences. Debbie co-hosts numerous TV shows, where she uses her knowledge of physics to explain the science behind extraordinary engineering feats. Deborah is the Chief Data Scientist at Metis where she leads the creation and growth of exceptional data science training opportunities. Deborah completed two postdoctoral fellowships at Columbia University's Applied Math and Physics Department and at NYU's Courant Institute for Mathematical Sciences where she carried out research in the area of acoustic waves. She invented a highly effective technique in the field of wireless communications whereby a cell phone user can communicate with a desired target user in a location far away. Show Notes2:10 – Debbie’s mission statement8:58 – Mentorship11:25 - Debbie talks about Discovery TV shows15:04 – Debbie discusses her Ted Talk19:20 - Data literacy discussion30:35 – Physicist to data science 33:22 – Training and data scientist 38:43 - What makes the perfect data science implementation in a company?41:53 – Debbie’s advice for young girlsDebbie Berebichez - InstagramDebbie Berebichez - TwitterDebbie Berebichez - LinkedInDebbie Berebichez - FacebookStatistic and the art of deception Connect with the TeamProducer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.

Learn more about blockchain at Microsoft: https://azure.microsoft.com/en-us/solutions/blockchain/About Yorke Rhodes IIIYorke E. Rhodes III is Cofounder of Blockchain @Microsoft and Principal Program Manager Azure Blockchain Engineering. He is a passionate technologist with broad interests, always drawn to the next shiny object. He earned a BS in Computer Science from NYU's Courant Institute of Mathematical Sciences. He has worked in industry for over 20 years, in enterprises such as Microsoft & IBM and startups in wireless, mobile, digital marketing & ecommerce. At Goldman Sachs Investment Bank he built their first wireless internet ingress and advised bankers in wireless, telecom and media. As a young developer he saw the beginnings of client server databases, obscure languages like ada, lisp & paradox and the birth of the internet. Blockchain piqued his interest in the summer of 2015 with the launch of Ethereum. An Adjunct Professor at NYU, he has taught Digital Marketing, Ecommerce and Intrapreneurship.

This week, we’re presenting stories about unexpected friendships in science, whether they’re formed in the field or at Burning Man. Part 1: Looking to connect with new people, mathematician Seth Cottrell sets up an ‘Ask a Mathematician’ booth at Burning Man. Part 2:  When herpetologist Joseph Mendelson gets his an opportunity to do fieldwork in Guatemala during his first year of graduate school, he struggles to connect with the locals. Seth Cottrell earned his PhD in mathematics from the Courant Institute at NYU.  His research is in quantum information and he teaches at New York City College of Technology.  For ten years, Seth has talked to complete strangers about math and physics and written about it at askamathematician.com.  His new book is “Do Colors Exist?: And Other Profound Physics Questions.” Joseph R. Mendelson III has been studying amphibians and reptiles for more than 30 years, concentrating mostly on Mexico and Central America. Most of his work has involved evolutionary studies and taxonomy―including the discovery and naming of about 40 new species. Other studies have included ecology, biomechanics, and natural history. Formerly an Associate Professor in Biology at Utah State University, Mendelson transitioned his career to balance his energies between research and conservation, while still teaching at the university level. Currently he is Director of Research at Zoo Atlanta and Adjunct Associate Professor of Biology at Georgia Tech University, where he teaches regularly. He also is Past-President of the Society for the Study of Amphibians and Reptiles, the world’s largest professional herpetological society. Joe has published more than 100 technical papers in peer-reviewed journals such as Science, Biology Letters, Proceedings of the National Academy of Sciences, Journal of Experimental Biology, Journal of Herpetology and Molecular Ecology.  He has also authored a number of articles and essays. His work has been featured in media outlets such as National Public Radio, National Geographic, Nature, New York Times, CNN, and Comedy Central’s Colbert Report. Additionally, Joe is a guitarist in the Atlanta-based science punk-rock band Leucine Zipper and the Zinc Fingers.   Learn more about your ad choices. Visit megaphone.fm/adchoices

Shmuel Rosner and Moshe Koppel discuss Koppel's theory about the Jewish world and its continuation. Moshe Koppel is an American-Israeli computer scientist, Talmud scholar and political activist. Koppel was born and raised in New-York where he received a traditional Jewish education. He received a B.A. from Yeshiva University and in 1979 completed his doctorate in mathematics under the supervision of Martin Davis at the Courant Institute of New York University. He spent a post-doctoral year at the Institute for Advanced Study in Princeton before moving to Israel in 1980. He has been a member of the Department of Computer Science in Bar-Ilan University since then.   Follow Shmuel Rosner on Twitter.

On the podcast today, we have two more fascinating interviews from Melanie’s time at Deep Learning Indaba! Mark helps host this episode as we speak with Karim Beguir and Muthoni Wanyoike about their company, Instadeep, the wonderful Indaba conference, and the growing AI community in Africa. Instadeep helps large enterprises understand how AI can benefit them. Karim stresses that it is possible to build advanced AI and machine learning programs in Africa because of the growing community of passionate developers and mentors for the new generation. Muthoni tells us about Nairobi Women in Machine Learning and Data Science, a community she is heavily involved with in Nairobi. The group runs workshops and classes for AI developers and encourages volunteers to participate by sharing their knowledge and skills. Karim Beguir Karim Beguir helps companies get a grip on the latest AI advancements and how to implement them. A graduate of France’s Ecole Polytechnique and former Program Fellow at NYU’s Courant Institute, Karim has a passion for teaching and using applied mathematics. This led him to co-found InstaDeep, an AI startup that was nominated at the MWC17 for the Top 20 global startup list made by PCMAG. Karim uses TensorFlow to develop Deep Learning and Reinforcement Learning products. Karim is also the founder of the TensorFlow Tunis Meetup. He regularly organises educational events and workshops to share his experience with the community. Karim is on a mission to democratize AI and make it accessible to a wide audience. Muthoni Wanyoike Muthoni Wanyoike is the team lead at Instadeep in Kenya. She is Passionate about bridging the skills gap in AI in Africa and does this by co-organizing the Nairobi Women in Machine Learning community. The community enables learning, mentorship, networking, and job opportunities for people interested in working in AI. She is experienced in research, data analytics, community and project management, and community growth hacking. Cool things of the week Is there life on other planets? Google Cloud is working with NASA’s Frontier Development Lab to find out blog In this Codelab, you will learn about StarCraft II Learning Environment project and to train your first Deep Reinforcement Learning agent. You will also get familiar some of the concepts and frameworks to get to train a machine learning agent. site A new course to teach people about fairness in ML blog Serverless from the ground up: Building a simple microservice with Cloud Functions (Part 1) blog Superposition Podcast from Deep Learning Indaba with Omoju Miller and Nando de Freitas tweet and video Interview Instadeep site Nairobi Women in Machine Learning and Data Science site Neural Information Processing Systems site Google Launchpad Accelerator site TensorFlow site Google Assistant site Cloud AutoML site Hackathon Lagos site Deep Learning Book book Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization research paper Lessons learned on building a tech community blog Kenya Open Data Initiative site R for Data Science GitHub site and book TWIML Presents Deep Learning Indaba site Question of the week If I want to create a GKE cluster with a specific major kubernetes version (or even just the latest) using the command line tools, how do I do that? GCloud container clusters create site Specifying cluster version site Where can you find us next? Our guests will be at Indaba 2019 in Kenya. Mark will be at KubeCon in December. Melanie will be at SOCML in November.

Twenty years ago, Chetan Dube left the world of academia, at New York University’s Courant Institute of Mathematical Sciences, to pursue a career in business. He tells Jonathan Moules what inspired the move. See acast.com/privacy for privacy and opt-out information.

April 30, 2018 Gender diversity in science, technology, engineering and math (STEM) is a global challenge in this rapidly growing industry. Experts studying the gender ratio in STEM have found that it is far more balanced in the Arab world than in the West. The many examples of highly successful women entrepreneurs in the technology sector emerging from Arab countries is contrasted with some remaining challenges faced by Arab women in their education and employment trajectory in STEM. This panel of experts in gender, STEM and technology highlights the inspiring contributions Arab women have made to the STEM fields and cover the remaining challenges to addressing the global STEM gender gap. Speakers Marina Hamed Kazim, Medical Practitioner, Sheikh Khalifa Medical City (SKMC) Jennifer Olmsted, Professor of Economics and Business, Director of the Social Entrepreneurship Semester, and Director of Middle East Studies and Arabic, Drew University Sana Odeh, Clinical Professor of Computer Science and Faculty Liaison for Global Programs of Computer Science, Courant Institute of Mathematical Sciences, NYU Moderated by Kirsten Sadler, Associate Professor of Biology, NYUAD

January 15, 2018 The arrival of massive amounts of data from imaging, sensors, computation and the internet brought with it significant challenges for data science. New methods for analysis and manipulation of big data have come from many scientific disciplines. The first focus of this talk is the application of ideas from differential equations, such as variational principles and numerical diffusion, to image and data analysis. Examples include denoising, segmentation, inpainting and texture extraction for images. The second focus is the development of new ideas in information science, such as soft-thresholding, sparsity and compressed sensing. The subsequent application of these ideas to differential equations and numerical computation is the third focus of this talk. Examples include soft-thresholding in multiscale computation, solutions with compact support and “compressed modes” for differential equations that come from variational principles, and applications to quantum physics. Speakers Russel E. Caflisch, Director, Courant Institute; Professor of Mathematics, NYU

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests. This time around I'm joined by Joan Bruna, Assistant Professor at the Courant Institute of Mathematical Sciences and the Center for Data Science at NYU, and Michael Bronstein, associate professor at Università della Svizzera italiana (Switzerland) and Tel Aviv University. Joan and Michael join me after their tutorial on Geometric Deep Learning on Graphs and Manifolds. In our conversation we dig pretty deeply into the ideas behind geometric deep learning and how we can use it in applications like 3D vision, sensor networks, drug design, biomedicine, and recommendation systems. This is definitely a Nerd Alert show, and one that will get your multi-dimensional neurons firing. Enjoy!

Dr. V.A. Shiva Ayyadurai, the inventor of email and polymath, holds four degrees from MIT, is a world-renowned systems scientist, inventor and entrepreneur. He is a Fulbright Scholar, Lemelson-MIT Awards Finalist, India’s First Outstanding Scientist and Technologist of Indian Origin, Westinghouse Science Talent Honors Award recipient, and a nominee for the U.S. National Medal of Technology and Innovation. His love of medicine and complex systems began in India when he became intrigued with medicine at the age of five as he observed his grandmother, a farmer and healer in the small village of Muhavur in South India, apply Siddha, India’s oldest system traditional medicine, to heal and support local villagers. These early experiences inspired him to pursue the study of modern systems science, information technology and eastern and traditional systems of medicine to develop an integrative framework linking eastern and western systems of medicine. In 1978, as a precocious 14-year-old, after completing a special program in computer science at the Courant Institute of Mathematical Science at NYU, Ayyadurai was recruited by the University of Medicine and Dentistry of New Jersey (UMDNJ) as a Research Fellow, where he developed the first electronic emulation of the entire interoffice mail system (Inbox, Outbox, Folders, Address Book, Memo, etc.), which he named “EMAIL,” to invent the world’s first email system, resulting in him being awarded the first United States Copyright for Email, Computer Program for Electronic Mail System, at a time when Copyright was the only protection for software inventions.

A few years back, a prospective doctoral student sought out Sylvia Serfaty with some existential questions about the apparent uselessness of pure math. Serfaty, then newly decorated with the prestigious Henri Poincaré Prize, won him over simply by being honest and nice. “She was very warm and understanding and human,” said Thomas Leblé, now an instructor at the Courant Institute of Mathematical Sciences at New York University.

V.A. Shiva Ayyadurai, the inventor of email and other revolutionary innovations, has been passionately interested in science and technology throughout his life. This passion has earned Shiva high honors in the academic and corporate worlds. It has also given him an opportunity to confront the financial and power dynamics that affect scientific innovation, especially when innovation arises from sources considered outside the mainstream - as Shiva, in fact, proudly considers himself to be. Born in Mumbai in 1963, at the age of five Shiva began observing his grandmother -- a farmer and healer in the small village of Muhavur, in South India - as she applied Siddha, India's oldest system of traditional medicine, to heal and support local villagers. He saw how his grandmother’s work was a multi-faceted, comprehensive system that impacted her patients physically, mentally, and even spiritually. When Shiva’s family immigrated to the United States, those early experiences inspired him to pursue the study of modern systems science and information technology. He has never lost touch with India’s healing traditions, and much of his work has been directed toward integrating the tools and techniques of East and West. At the age of 14, after completing a special program in computer science at New York University’s Courant Institute of Mathematical Science, Shiva was recruited by the University of Medicine and Dentistry of New Jersey as a Research Fellow. His mentor at UMDNJ soon presented Shiva with a difficult but irresistible challenge. Shiva was asked to create an electronic equivalent of the interoffice mail system, in which hard copies of documents were circulated throughout an office environment. The interoffice mail system was standard operating procedure in literally millions of companies, hospitals, schools, and other institutions around the world. It was literally everywhere. And in practice, preparing interoffice mail was virtually always tasked to female secretaries or assistants in service of their male bosses and managers. Shiva understood this assignment in human terms as well in a scientific context. Creating an electronic alternative to interoffice mail would be not only a technical advance, but also a revolutionary work-saving innovation that would benefit everyone from secretaries to CEOs. After writing 50,000 lines of computer code, Shiva introduced the world’s first true email system, incorporating Inbox and Outbox, Folders, Address Book, Memo, and other now-familiar features of every email system. He named the system “EMAIL,” and was awarded the first United States Copyright for “Email, Computer Program for Electronic Mail System.” This legally recognized Shiva as the inventor of email, at a time when Copyright was the only protection for software inventions. Since then Shiva Ayyadurai has become a world-renowned systems scientist, inventor, and entrepreneur. He has been a Fulbright Scholar, Lemelson-MIT Awards Finalist, India’s First Outstanding Scientist and Technologist of Indian Origin, Westinghouse Science Talent Honors Award recipient, and a nominee for the U.S. National Medal of Technology and Innovation. Shiva has earned four degrees from the Massachusetts Institute of Technology (MIT), including a Bachelor’s in electrical engineering and computer science, and a dual Master’s Degree in mechanical engineering and visual studies from the MIT Media Laboratory. In 2003 he completed his doctoral work in systems biology in the Department of Biological Engineering. Shiva’s love of complex systems, which began in India, has continued to inform all his work. After receiving his PhD he returned to India on a Fulbright grant, where he researched the systems theoretic basis of Eastern medicine. Systems Health®, a new educational program that provides a scientific foundation for integrative medicine, was based on these findings. Shiva is also the inventor of CytoSolve®, a scalable computational platform for modeling the cell using dynamic integration of molecular pathways models. Like all of Shiva’s work, CytoSolve draws on the principle that nature’s intelligence is decentralized. While we might expect the nucleus to dominate the cell’s function, the work itself is done on the periphery of the cell, in the membrane. While at MIT, Shiva developed Systems Visualization, a pioneering course integrating systems theory, data, metaphor, and narrative storytelling to enable visualization of complex systems. After winning a White House competition to automatically analyze and sort President Clinton’s email, Shiva started EchoMail, Inc. which grew to nearly $200 million in market va lua t io n. Shiva has appeared in The MIT Technology Review, The Wall Street Journal, New York Times, NBC News, USA Today and other major media. He was named to the “Top 40” in the Improper Bostonian. He has also authored four books: Arts and the Internet, The Internet Publicity Guide, The Email Revolution, and most recently The System and Revolution. His passion for entrepreneurialism continues as Managing Director of General Interactive, a venture fund that incubates, mentors, and funds new startups in in rural healthcare, media, biotechnology, and information technology. Shiva also founded Innovation Corps, to inspire and enable innovation among teenagers worldwide. He serves as a consultant to CEOs and Executive Management at Fortune 1000 companies, as well as government organizations such as the United States Postal Service, Office of Inspector General. Shiva is the Chairman & CEO of CytoSolve Inc., which provides a revolutionary platform for modeling complex diseases and developing multi-combination therapeutics. His recent efforts at CytoSolve have led to an FDA allowance and exemption on a multi-combination drug for pancreatic cancer, development of innovative nutraceutical products, and numerous industry and academic partnerships. Shiva’s earlier research on pattern recognition and large - scale systems development has also resulted in multiple patents, numerous industry awards, commercial products including EchoMail, and coverage by scientific and industry publications. Shiva serves as Executive Director of the International Center for Integrative Systems, a non- profit research and education foundation dedicated to the application of systems thinking across a range of disciplines. Research on Genetically Modified Organisms (GMOs) is a specific and urgent focus of this foundation. Along these lines, Shiva has met with world leaders including former President Bill Clinton, Prime Minister Narendra Modi of India, and French President Francois Hollande, who have sought his advice on innovative technologies and their applications to food and healthcare systems. Shiva Ayyadurai is a member of Sigma-Xi, Eta Kappa Nu and Tau Beta Pi. He supports the Shanthi Foundation, which raises money to provide scholarships for education of orphaned girls. He is also a supporter of non-profit organizations including the Guggenheim Museum, Very Special Arts, National Public Radio, and the National Geographic Society. Shiva enjoys yoga, travel, tennis, animals, art and architecture. He resides in Belmont, Massachusetts, and travels extensively between there, Malibu, California, and New York City.

Sandra May works at the Seminar for Applied Mathematics at ETH Zurich and visited Karlsruhe for a talk at the CRC Wave phenomena. Her research is in numerical analysis, more specifically in numerical methods for solving PDEs. The focus is on hyperbolic PDEs and systems of conservation laws. She is both interested in theoretical aspects (such as proving stability of a certain method) and practical aspects (such as working on high-performance implementations of algorithms). Sandra May graduated with a PhD in Mathematics from the Courant Institute of Mathematical Sciences (part of New York University) under the supervision of Marsha Berger. She likes to look back on the multicultural working and learning experience there. We talked about the numerical treatment of complex geometries. The main problem is that it is difficult to automatically generate grids for computations on the computer if the shape of the boundary is complex. Examples for such problems are the simulation of airflow around airplanes, trucks or racing cars. Typically, the approach for these flow simulations is to put the object in the middle of the grid. Appropriate far-field boundary conditions take care of the right setting of the finite computational domain on the outer boundary (which is cut from an infinite model). Typically in such simulations one is mainly interested in quantities close to the boundary of the object. Instead of using an unstructured or body-fitted grid, Sandra May is using a Cartesian embedded boundary approach for the grid generation: the object with complex geometry is cut out of a Cartesian background grid, resulting in so called cut cells where the grid intersects the object and Cartesian cells otherwise. This approach is fairly straightforward and fully automatic, even for very complex geometries. The price to pay comes in shape of the cut cells which need special treatment. One particular challenge is that the cut cells can become arbitrarily small since a priori their size is not bounded from below. Trying to eliminate cut cells that are too small leads to additional problems which conflict with the goal of a fully automatic grid generation in 3d, which is why Sandra May keeps these potentially very small cells and develops specific strategies instead. The biggest challenge caused by the small cut cells is the small cell problem: easy to implement (and therefore standard) explicit time stepping schemes are only stable if a CFL condition is satisfied; this condition essentially couples the time step length to the spatial size of the cell. Therefore, for the very small cut cells one would need to choose tiny time steps, which is computationally not feasible. Instead, one would like to choose a time step appropriate for the Cartesian cells and use this same time step on cut cells as well. Sandra May and her co-workers have developed a mixed explicit implicit scheme for this purpose: to guarantee stability on cut cells, an implicit time stepping method is used on cut cells. This idea is similar to the approach of using implicit time stepping schemes for solving stiff systems of ODEs. As implicit methods are computationally more expensive than explicit methods, the implicit scheme is only used where needed (namely on cut cells and their direct neighbors). In the remaining part of the grid (the vast majority of the grid cells), a standard explicit scheme is used. Of course, when using different schemes on different cells, one needs to think about a suitable way of coupling them. The mixed explicit implicit scheme has been developed in the context of Finite Volume methods. The coupling has been designed with the goals of mass conservation and stability and is based on using fluxes to couple the explicit and the implicit scheme. This way, mass conservation is guaranteed by construction (no mass is lost). In terms of stability of the scheme, it can be shown that using a second-order explicit scheme coupled to a first-order implicit scheme by flux bounding results in a TVD stable method. Numerical results for coupling a second-order explicit scheme to a second-order implicit scheme show second-order convergence in the L^1 norm and between first- and second-order convergence in the maximum norm along the surface of the object in two and three dimensions. We also talked about the general issue of handling shocks in numerical simulations properly: in general, solutions to nonlinear hyperbolic systems of conservation laws such as the Euler equations contain shocks and contact discontinuities, which in one dimension express themselves as jumps in the solution. For a second-order finite volume method, typically slopes are reconstructed on each cell. If one reconstructed these slopes using e.g. central difference quotients in one dimension close to shocks, this would result in oscillations and/or unphysical results (like negative density). To avoid this, so called slope limiters are typically used. There are two main ingredients to a good slope limiter (which is applied after an initial polynomial based on interpolation has been generated): first, the algorithm (slope limiter) needs to detect whether the solution in this cell is close to a shock or whether the solution is smooth in the neighborhood of this cell. If the algorithm thinks that the solution is close to a shock, the algorithm reacts and adjusts the reconstruted polynomial appropriately. Otherwise, it sticks with the polynomial based on interpolation. One commonly used way in one dimension to identify whether one is close to a shock or not is to compare the values of a right-sided and a left-sided difference quotient. If they differ too much the solution is (probably) not smooth there. Good reliable limiters are really difficult to find. Literature and additional material S. May, M. Berger: An Explicit Implicit Scheme for Cut Cells in Embedded Boundary Meshes, Preprint available as SAM report, number 2015-44, 2015. S. May, M. Berger: A mixed explicit implicit time stepping scheme for Cartesian embedded boundary meshes, Finite Volumes for Complex Applications VII - Methods and Theoretical Aspects, pp. 393-400, Springer, 2014. S. May, M. Berger: Two-dimensional slope limiters for finite volume schemes on non-coordinate-aligned meshes, SIAM J. Sci. Comput. 35 (5) pp. A2163-A2187, 2013.

Sandra May works at the Seminar for Applied Mathematics at ETH Zurich and visited Karlsruhe for a talk at the CRC Wave phenomena. Her research is in numerical analysis, more specifically in numerical methods for solving PDEs. The focus is on hyperbolic PDEs and systems of conservation laws. She is both interested in theoretical aspects (such as proving stability of a certain method) and practical aspects (such as working on high-performance implementations of algorithms). Sandra May graduated with a PhD in Mathematics from the Courant Institute of Mathematical Sciences (part of New York University) under the supervision of Marsha Berger. She likes to look back on the multicultural working and learning experience there. We talked about the numerical treatment of complex geometries. The main problem is that it is difficult to automatically generate grids for computations on the computer if the shape of the boundary is complex. Examples for such problems are the simulation of airflow around airplanes, trucks or racing cars. Typically, the approach for these flow simulations is to put the object in the middle of the grid. Appropriate far-field boundary conditions take care of the right setting of the finite computational domain on the outer boundary (which is cut from an infinite model). Typically in such simulations one is mainly interested in quantities close to the boundary of the object. Instead of using an unstructured or body-fitted grid, Sandra May is using a Cartesian embedded boundary approach for the grid generation: the object with complex geometry is cut out of a Cartesian background grid, resulting in so called cut cells where the grid intersects the object and Cartesian cells otherwise. This approach is fairly straightforward and fully automatic, even for very complex geometries. The price to pay comes in shape of the cut cells which need special treatment. One particular challenge is that the cut cells can become arbitrarily small since a priori their size is not bounded from below. Trying to eliminate cut cells that are too small leads to additional problems which conflict with the goal of a fully automatic grid generation in 3d, which is why Sandra May keeps these potentially very small cells and develops specific strategies instead. The biggest challenge caused by the small cut cells is the small cell problem: easy to implement (and therefore standard) explicit time stepping schemes are only stable if a CFL condition is satisfied; this condition essentially couples the time step length to the spatial size of the cell. Therefore, for the very small cut cells one would need to choose tiny time steps, which is computationally not feasible. Instead, one would like to choose a time step appropriate for the Cartesian cells and use this same time step on cut cells as well. Sandra May and her co-workers have developed a mixed explicit implicit scheme for this purpose: to guarantee stability on cut cells, an implicit time stepping method is used on cut cells. This idea is similar to the approach of using implicit time stepping schemes for solving stiff systems of ODEs. As implicit methods are computationally more expensive than explicit methods, the implicit scheme is only used where needed (namely on cut cells and their direct neighbors). In the remaining part of the grid (the vast majority of the grid cells), a standard explicit scheme is used. Of course, when using different schemes on different cells, one needs to think about a suitable way of coupling them. The mixed explicit implicit scheme has been developed in the context of Finite Volume methods. The coupling has been designed with the goals of mass conservation and stability and is based on using fluxes to couple the explicit and the implicit scheme. This way, mass conservation is guaranteed by construction (no mass is lost). In terms of stability of the scheme, it can be shown that using a second-order explicit scheme coupled to a first-order implicit scheme by flux bounding results in a TVD stable method. Numerical results for coupling a second-order explicit scheme to a second-order implicit scheme show second-order convergence in the L^1 norm and between first- and second-order convergence in the maximum norm along the surface of the object in two and three dimensions. We also talked about the general issue of handling shocks in numerical simulations properly: in general, solutions to nonlinear hyperbolic systems of conservation laws such as the Euler equations contain shocks and contact discontinuities, which in one dimension express themselves as jumps in the solution. For a second-order finite volume method, typically slopes are reconstructed on each cell. If one reconstructed these slopes using e.g. central difference quotients in one dimension close to shocks, this would result in oscillations and/or unphysical results (like negative density). To avoid this, so called slope limiters are typically used. There are two main ingredients to a good slope limiter (which is applied after an initial polynomial based on interpolation has been generated): first, the algorithm (slope limiter) needs to detect whether the solution in this cell is close to a shock or whether the solution is smooth in the neighborhood of this cell. If the algorithm thinks that the solution is close to a shock, the algorithm reacts and adjusts the reconstruted polynomial appropriately. Otherwise, it sticks with the polynomial based on interpolation. One commonly used way in one dimension to identify whether one is close to a shock or not is to compare the values of a right-sided and a left-sided difference quotient. If they differ too much the solution is (probably) not smooth there. Good reliable limiters are really difficult to find. Literature and additional material S. May, M. Berger: An Explicit Implicit Scheme for Cut Cells in Embedded Boundary Meshes, Preprint available as SAM report, number 2015-44, 2015. S. May, M. Berger: A mixed explicit implicit time stepping scheme for Cartesian embedded boundary meshes, Finite Volumes for Complex Applications VII - Methods and Theoretical Aspects, pp. 393-400, Springer, 2014. S. May, M. Berger: Two-dimensional slope limiters for finite volume schemes on non-coordinate-aligned meshes, SIAM J. Sci. Comput. 35 (5) pp. A2163-A2187, 2013.

Dr. Ernest Davis is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University. He is an expert on artificial intelligence - he studies the problem of representing commonsense knowledge (basic knowledge about the real world that is common to all humans) and expressing it in a form that is systematic enough to be used by a computer program. Dr. Davis' research includes topics such as the problem of reasoning about containers and the ontology of matter, both of which are discussed in this episode. We also go into detail about potential applications and whether or not artifical intelligence poses a threat. Visit his website for more information here.

Our new book, "Loving and Hating Mathematics," is about the emotional, social and political aspects of mathematical life. A major chapter tells of mathematical communities, such as Gottingen in the early 20th century, Bourbaki in Paris, and the Courant Institute in New York. The creation of such a productive community often depends on the leadership and vision of a vital, charismatic figure How the community continues and endures depends on how its members internalize and develop that vision.

Polar Land Ice by David Holland, Center for Atmosphere Ocean Science (CAOS), Courant Institute of Mathematical Sciences, New York University Part 1: Some of the basics of land ice. Part 2: In the second half, Holland speaks about the rapid disappearance of land ice in some areas, and the need for new math and science to analyze that disappearance.