Podcasts about mathematical modeling

…where we talk immune systems, zombies and being presidential. Jane Heffernan is a disease modeller. In her own words “My research aims to understand immunity development and maintenance in people, and in populations, after infection or vaccination. When I am not working, you can find me at soccer pitches or hockey arenas.” Jane's current research interests lie within mathematical immunology and the modelling of waning and boosting immunity. Her lab is interested in understanding characteristics of pathogens, individual hosts, and populations that allow for disease spread and to determine public health and medical intervention strategies that will be needed to contain or eradicate infectious disease Learn more about Jane on her Wikipedia page: https://en.wikipedia.org/wiki/Jane_Heffernan And the link to the Mathematical Modeling of Zombies: ​​https://yfile.news.yorku.ca/2014/10/30/york-profs-investigate-the-mathematics-of-the-undead/ Find out more about SMB on: The website: smb.org Twitter: @smb_mathbiology Facebook: @smb.org Linkedin: @smb_mathbiology The Bulletin of Mathematical Biology

This article details the development of design principles to support teachers in planning for a Community-Based Mathematical Modeling task with a focus on social justice in the elementary grades. By reflecting on the dilemmas we encountered in the design and enactment of the tasks, we developed five design principles that allowed us to address issues of social justice as well as attend to powerful mathematical ideas to bring awareness and take action around a local problem. Through our article, we hope to share with mathematics teacher educators design principles to help plan for tasks with pre- and in-service teachers that prioritize connecting mathematics to social issues and empower both teachers and students to take action to make a positive impact in the community. Special Guests: Holly Nicole Tate and Jennifer Suh.

Mike Smart of Egress Solutions has an exhilarating discussion with successful entrepreneur Esben Friis-Jensen. He is working on his second start-up in less than 10 years. Esben adds a unique perspective on the product-led growth (PLG) initiative with offers his thoughts on how the enterprise software business will shift and change in the future. We discuss the progression of PLG and how it will change how software companies organize teams to improve their effectiveness. Esben is Co-founder and Chief Growth Officer at Userflow, a no-code onboarding flow builder that enables customized in-app tours and checklists. Previously he was Co-founder and Chief Customer Officer at Cobalt, a security platform that offers Pentest as a Service (PtaaS). Esben began his career as a consultant with Accenture responsible for the deployment management of global IT implementations. Esben' Bio: Esben Friis-Jensen is the co-founder and Chief Growth Officer at Userflow, a no-code builder for in-app onboarding and surveys, allowing SaaS businesses to be more product-led. Prior to Userflow, Esben co-founded Cobalt, which today is a 200+ employee company. Esben holds BEng in Mathematics and MEng in Mathematical Modeling from Technical University of Denmark (DTU). ----------- Guest: Esben Friis-Jensen | Linked In: Esben Friis-Jensen Host: Mike Smart | www.EgressSolutions.net ----------- This is a Mr. Thrive Media production. Learn more at www.MrThrive.com

PTF sits down with Marshall Gramm for a lengthy discussion about computer betting and computer teams -- who are they, what do they do, where do they come from, and what do they mean to you? Throughout the chat, several resources to learn more are mentioned and we'll link to those in these show notes.Here's Marshall's tweet about calculating imputed odds.Articles and books:Bill Benter (1994) “Computer Based Horse Racing Handicapping and Wagering Systems: A Report” from Bill Ziemba's anthology “Efficiency of Racetrack Betting”(1994-benter)Ruth Bolton and Randall Chapman (1986) “Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Racing” in Management Science (Bolton_Chapman_1986)The Gambler Who Cracked the Horse-Racing Code by Kit Chellel in Bloomberg (2018)The Gambler Who Cracked the Horse-Racing Code - BloombergI used AI to bet on horse-racing. Here's what happened in FT Magazine (2023)I used AI to bet on horse-racing. Here's what happened | Financial Times (ft.com)BooksPrecision by CX Wong (2011)Precision: Statistical and Mathematical Methods in Horse Racing: Wong, C X: 9781432768522: Amazon.com: BooksCalculated Bets by Steve Skiena (2001)Amazon.com: Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win (Outlooks): 9780521009621: Skiena, Steven S.: Books

PTF sits down with Marshall Gramm for a lengthy discussion about computer betting and computer teams -- who are they, what do they do, where do they come from, and what do they mean to you? Throughout the chat, several resources to learn more are mentioned and we'll link to those in these show notes.Here's Marshall's tweet about calculating imputed odds.Articles and books:Bill Benter (1994) “Computer Based Horse Racing Handicapping and Wagering Systems: A Report” from Bill Ziemba's anthology “Efficiency of Racetrack Betting”(1994-benter)Ruth Bolton and Randall Chapman (1986) “Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Racing” in Management Science (Bolton_Chapman_1986)The Gambler Who Cracked the Horse-Racing Code by Kit Chellel in Bloomberg (2018)The Gambler Who Cracked the Horse-Racing Code - BloombergI used AI to bet on horse-racing. Here's what happened in FT Magazine (2023)I used AI to bet on horse-racing. Here's what happened | Financial Times (ft.com)BooksPrecision by CX Wong (2011)Precision: Statistical and Mathematical Methods in Horse Racing: Wong, C X: 9781432768522: Amazon.com: BooksCalculated Bets by Steve Skiena (2001)Amazon.com: Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win (Outlooks): 9780521009621: Skiena, Steven S.: Books

This Perspectives on Practice manuscript focuses on an innovation associated with “Engaging Teachers in the Powerful Combination of Mathematical Modeling and Social Justice: The Flint Water Task” from Volume 7, Issue 2 of MTE. The Flint Water Task has shown great promise in achieving the dual goals of exploring mathematical modeling while building awareness of social justice issues. This Perspectives on Practice article focuses on two adaptations of the task—gallery walks and What I Know, What I Wonder, What I Learned (KWL) charts—that we have found to enhance these learning opportunities. We found that the inclusion of a gallery walk supported our students in the development of their mathematical modeling skills by enhancing both the mathematical analyses of the models and the unpacking of assumptions. The KWL chart helps students document their increase in knowledge of the social justice issues surrounding the water crisis. Using the mathematical modeling cycle to explore social justice issues allows instructors to bring humanity into the mathematics classroom. Special Guests: Dana L. Grosser-Clarkson and Joel Amidon.

This Perspectives on Practice manuscript focuses on an innovation associated with “Engaging Teachers in the Powerful Combination of Mathematical Modeling and Social Justice: The Flint Water Task” from Volume 7, Issue 2 of MTE. We built on Aguirre et al.'s (2019) integration of mathematical modeling and social justice issues in mathematics teacher education to similarly integrate statistical investigations with social justice issues. Link to resources: www.modules2.com Special Guests: Liza Bondurant and Stephanie Casey.

Dr. Jessica Lee, scientist for the Space Biosciences Research Branch at NASA's AIMS Research Center in Silicon Valley uses both wet-lab experimentation and computational modeling to understand what microbes really experience when they come to space with humans. She discusses space microbiology, food safety and microbial food production in space and the impacts of microgravity and extreme radiation when sending Saccharomyces cerevisiae to the moon. Ashley's Biggest Takeaways Lee applied for her job at NASA in 2020. Prior to her current position, she completed 2 postdocs and spent time researching how microbes respond to stress at a population level and understanding diversity in microbial populations. She has a background in microbial ecology, evolution and bioinformatics. Model organisms are favored for space research because they reduce risk, maximize the science return and organisms that are well understood are more easily funded. Unsurprisingly, most space research does not actually take place in space, because it is difficult to experiment in space. Which means space conditions must be replicated on Earth. This may be accomplished using creative experimental designs in the wet-lab, as well as using computational modeling. Links for the Episode: Out of This World: Microbes in Space. Register for ASM Microbe 2023. Add “The Math of Microbes: Computational and Mathematical Modeling of Microbial Systems,” to your ASM Microbe agenda. Let us know what you thought about this episode by tweeting at us @ASMicrobiology or leaving a comment on facebook.com/asmfan.

In this episode, I sit down with Dr. Michael Huth to talk about the ethics of data collection, privacy, and the new age of “privacism.” We talk about his new platform, Xayn, we discuss what it looks like to build a company based on ethical principles like privacy and user autonomy, and Michael explains why we should care about our privacy online. Professor Michael Huth is Co-Founder and Chief Research Officer of Xayn. He teaches at Imperial College London, where he is on the faculty of the department of Engineering, and he serves as the Head of the Department of Computing, at the Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning, with with expertise in trust and policy. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. He holds associations with the Centre for Cryptocurrency Research and Engineering; the Centre for Smart Connected Futures; the Engineering Secure Software Systems; the Immuno-Pathology Network; and the Quantitative Analysis and Decision Science Section. In 2017, he founded the privacy tech company together with Leif-Nissen Lundbæk and Felix Hahmann. Xayn offers a privacy-protecting search engine that enables users to gain back control over algorithms and data harvesting. Production and research support from Jared Maslin.

Welcome to Episode 4 of the BioHackers Podcast! In this episode, David and Alex welcome Marc Birtwistle to the show. Together, they discuss mathematical modeling as a time machine for the cell, the lab of the future, great advice for careers in science, kinesthetic learning, and Marc's cool new company, Blotting Innovations.   Watch the Video Podcast on YouTube:  https://youtu.be/ATWPnCblx0M  Here is a list of topics: Welcome to Episode 4 (00:00)In Silico Science (01:01)Modeling Weather of the Cell (03:26)Welcome Marc to the Show (06:42)Don't Panic – Marc's Story I (08:54)Pivot into Biosciences (13:49)Lab of the Future (18:58)When You Stop Moving … You're Dead (24:54)Modeling is a Time Machine (27:01)Causality: Standing on the Shoulders of Giants (30:26)Modeling is an Aha Moment for Cell Biology (33:15)Models as Kinesthetic Learning Tools (40:45) Blotting Innovations – Marc's Story II (43:20)What is a BioHacker to You? (50:08)   Enjoy the Show! 

Returning to the podcast is Associate Professor and head of the Mathematical Modeling academic group, André Brodtkorb. Occasional podcast host Carla Hughes welcomed André back to the pod to talk about his exciting work on ash detection and eruption altitudes of volcanoes. In this episode, Carla and André discuss the recent Hunga Tonga–Hunga Ha'apai eruption and tsunami as well as André's personal experiences during the Eyjafjallajökull 2010 eruption. To learn more about André's work, you can visit his website. You can read a pre-print of a paper André co-authored with Håvard Heitlo Holm on this subject: Real-World Oceanographic Simulations on the GPUusing a Two-Dimensional Finite-Volume Scheme. Further reading Source code: https://github.com/babrodtk/VolcanicAshInversion Mathematical modeling webpage: https://uni.oslomet.no/matmod/

Returning to the podcast is Associate Professor and head of the Mathematical Modeling academic group, André Brodtkorb. Occasional podcast host Carla Hughes welcomed André back to the pod to talk about his exciting work on ash detection and eruption altitudes of volcanoes. In this episode, Carla and André discuss the recent Hunga Tonga–Hunga Ha'apai eruption and tsunami as well as André's personal experiences during the Eyjafjallajökull 2010 eruption. To learn more about André's work, you can visit his website. You can read a pre-print of a paper André co-authored with Håvard Heitlo Holm on this subject: Real-World Oceanographic Simulations on the GPUusing a Two-Dimensional Finite-Volume Scheme. Further reading Source code: https://github.com/babrodtk/VolcanicAshInversion Mathematical modeling webpage: https://uni.oslomet.no/matmod/

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!   Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.   Key Takeaways: What are Dr. Jerry's favorite AI design tools? Dr, Jerry shares his four primary tools: MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product. Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics. R Programming is Dr. Jerry's favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications. Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level. Does Dr. Jerry have any recommended books for causality? The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important. For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.   Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.   Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data. Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors. The seven rules of human behavior can be found in Eddie Rafii's latest book: Behaviology, New Science of Human Behavior. Dr. Jerry shares his favorite tools for casual analysis: Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.   Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon. BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.   What skills are needed for data science machinery and AI developers? Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.   Stay Connected with AI Live and Unbiased: Visit our website AgileThought.com Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!   Learn more about Dr. Jerry Smith   Mentioned in this episode: MATLAB MATLAB Mathematical Modeling Python Artificial Intelligence with R Compellon Causal Nex BayesiaLab   Dr. Jerry's Book Recommendations: The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie   Causal Inference in Statistics: A Primer, Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell   Counterfactuals and Causal Inferences: Methods and Principles,  Stephen L. Morgan and Christopher Winship   Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results, Florent Buisson   Designing for Behavior Change: Applying Psychology and Behavioral Economics, Stephen Wendel   Behaviology, New Science of Human Behavior, Eddie Rafii

Improving College Readiness Through Mathematical Modeling Date: January 25, 2022 Presenters: Denise Green, Alison Lynch What does it mean to be college-ready? How do we prepare more students to succeed in college-level math? In this session, we will share how integrating mathematical modeling into K-12 and post-secondary classrooms can change classroom practices and position more […]

MLOps Coffee Sessions #73 with Breno Costa and Matheus Frata, On Structuring an ML Platform 1 Pizza Team. // Abstract Breno and Matheus were part of an organizational change at Neoway in recent years. With the creation of cross-functional and platform teams in order to improve the value stream generated by these. They share their experience in creating a machine learning platform team. The challenges they faced along the way, how they approached using product thinking and the results achieved so far. // Bio Matheus Frata Matheus is an Electronics Engineer that got into Data Science by accident! During his graduation, Matheus joined Neoway as a Data Scientist, but during that time he saw a lot of problems that were related to engineers! This was Matheus' beginning with MLOPS. Today, Matheus works as a Machine Learning Engineer helping their Data Scientists to FLY!!! Breno Costa Breno uses his mixed background in Computer Science and Mathematical Modeling to design and develop ML-based software products. A brief period as an entrepreneur gives a different look at how to approach problems and generate more value. He has worked at Neoway for three years and currently works as a machine learning engineer on the Platform team. // Related links https://mlops.community/building-neoways-ml-platform-with-a-team-first-approach-and-product-thinking/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletter and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Breno on LinkedIn: https://www.linkedin.com/in/breno-c-costa/ Connect with Matheus on LinkedIn: https://www.linkedin.com/in/matheus-frata/

This week, WKU students Logan Stewart and Matthew Poynter discuss their research on modeling one-dimensional wave equations of vibrations on a string. Then, Kaylee-Dee Long (Georgetown College) describes her research on corticotropin-releasing-hormone production in Red-Sided Garter Snakes during their mating and migration seasons. These researchers all received awards for their presentations at the annual Kentucky Academy of Science conference held in November, 2021. Finally, astronomy/physics professor J. Scott Miller fills us in on what to see in the night sky in January, 2022. Here is the link for the KAS conference: https://www.kyscience.org/registration_abstracts.php. Public domain music is by Scott Holmes 'Positive and Fun' from freemusicarchive.com. Bench Talk is a weekly program that airs on WFMP Louisville ‘Forward Radio' 106.5 FM (forwardradio.org) every Monday at 7:30 pm, Tuesday at 11:30 am, and Wednesday at 7:30 am. Visit our Facebook page for links to the articles discussed in this episode: https://www.facebook.com/pg/BenchTalkRadio/posts/?ref=page_internal

Alfio Quarteroni is Professor of Numerical Analysis and Director of of the Laboratory for Modeling and Scientific Computing -- otherwise known as MOX -- at the Polytechnic University of Milan in Italy. He is the founder (and first director) of MOX and of MATHICSE at EPFL, Lausanne, where he is Emeritus Professor. He is co-founder (and President) of MOXOFF, a spin-off company. His research interests concern Mathematical Modelling, Numerical  Analysis, Scientific Computing, and applications in fluid mechanics, geophysics, medicine, epidemiology, and the improvement of sports performance. His research group at EPFL has contributed to the preliminary design of Solar Impulse, the Swiss, long-range experimental solar-powered aircraft project; they also carried out the mathematical simulation optimising the performances of the Alinghi yacht, twice winner of the America's Cup. He authored or edited 37 books and contributed more than 400 articles to international scientific journals and conference proceedings. He also serves on many editorial boards of journals and book series.He is a plenary speaker at ECM 2021, where he will give a talk on Mathematical Modeling of the Cardiac FunctionRelated Books and Journals and Springer homepage of the podcast: https://www.springer.com/gp/campaign/mathematics-podcasts

This episode features Dr. Jomar Rabajante, a faculty member of the Institute of Mathematical Sciences and Physics at the University of the Philippines Los Baños. He is the OIC-Dean of the Graduate School of UPLB, a fellow of the UP Resilience Institute, and a lead researcher of the UP COVID-19 Pandemic Response Team. We talked about simulation theory and our concept of reality, the surprising and unexpected things that numbers can reveal about real life, being named one of the Philippines' Outstanding Young Scientists, using data and numbers to deliver crucial pandemic information to policymakers, how to recover from being overwhelmed by numbers and statistics, and more.

On Rhode Island College Week: Mathematical models help with predicating the future. David Abrahamson, professor of mathematical sciences, discusses this exciting part of mathematical study. Dave grew up in suburban Los Angeles and was a mathematics major at Harvey Mudd College.  He came to Brown University to earn a doctorate in differential equations in their […]

How data is leveraged to create Mathematical models impacts many industries, particularly those such as finance and insurance. As technology improves, the possibilities for fintech applications are nearly endless. Amrit Santhirasenan, Co-founder and CEO of Hyperexponential, as well as host of the Startup Dads Podcast joins to explore. Find show notes and more at: https://www.soarpay.com/podcast/

Harvard Epidemiologist, Dr. Stephen Kissler and John Houghton discuss data modeling for diseases with special consideration of SARS-CoV-2 variants, including B.1.1.7 (UK), P.1 (Brazil), and B.1.351 (South Africa).  This is a deep dive on the nuts and bolts of COVID-19 modeling including insightful definitions of R0, generation interval, start date, and population size. How R0 as a measure becomes substituted with Rt as an outbreak progresses Review data model created by John at the beginning of the epidemic The SIR model (Susceptible, Infectious, or Recovered) How does reinfection change the SIR model? Introducing SIRS (or SIS) Probabilistic vs deterministic modeling The explosiveness of exponential growth Can new variants be introduced at an exponential rate? Discussion of Lancet paper: Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence - The Lancet Math check: If a variant is 40% more infectious than an R0 3.0 wild type-virus, what is the new R0? Check out Dr. Kissler's podcast: Pandemic: Coronavirus Edition.

Panos Pardalos is a Distinguished Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science & Information & Engineering departments. In addition, he is the director of the Center for Applied Optimization. Dr. Pardalos is a world renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, and Data Sciences. He is a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.” Dr. Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher's entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline. Dr. Pardalos is also a Member of several Academies of Sciences, and he holds several honorary PhD degrees and affiliations. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, Computational Management Science, and Springer Nature Operations Research Forum. He has published over 500 journal papers, and edited/authored over 200 books. He is one of the most cited authors and has graduated 64 PhD students so far. Details can be found in www.ise.ufl.edu/pardalos Panos Pardalos has lectured and given invited keynote addresses worldwide in countries including Austria, Australia, Azerbaijan, Belgium, Brazil, Canada, Chile, China, Czech Republic, Denmark, Egypt, England, France, Finland, Germany, Greece, Holland, Hong Kong, Hungary, Iceland, Ireland, Italy, Japan, Lithuania, Mexico, Mongolia, Montenegro, New Zealand, Norway, Peru, Portugal, Russia, South Korea, Singapore, Serbia, South Africa, Spain, Sweden, Switzerland, Taiwan, Turkey, Ukraine, United Arab Emirates, and the USA.

Today Pouya is speaking with Tyler Martin, Physical and Mathematical Specialist with extensive research into Mathematical modeling of COVID-19 data and String Theory.   Tyler's Social:  Instagram: https://www.instagram.com/tylerjamartin/   Pouya's Social: Instagram: https://www.instagram.com/pouyalj/ Twitter: https://twitter.com/pouyalj LinkedIn: https://www.linkedin.com/in/pouyalajevardi/   The article discussed in the talk: NYT article on modelling paths to herd immunity in the USA   Episode Transcript... ----more----   SUMMARY KEYWORDS herd immunity, people, models, vaccination, assumptions, lockdowns, mathematical modeling, vaccinate, talking, deterministic, masks, number, means, mathematical models, strategies, politicians, frontline workers, account, restrictions, predict SPEAKERS Pouya LJ, Tyler Martin   Tyler Martin  00:16 Hello, ladies and gentlemen, welcome back to yet another episode of The BGP podcast. I'm here joined today by my good friend and colleague, Tyler Martin. He is okay. Why don't I hand him I'll hand it over to him to tell. Tell him tell you guys about himself. Hey, Tyler, how are you? Hey, I'm good. How are you?   Pouya LJ  00:36 Very good.   00:36 Thank you for having me on podcast. I've always heard your podcast. So.   Tyler Martin  00:43 Thank you for being here. It's a pleasure. to to to be talking to you. Now. Okay, so why don't you tell us a little bit about yourself? What do you do? What are you? What are your likes and dislikes in this? crazy world? Okay, um, a little bit about me. I'm doing physical mathematical specialist utsc with, with you, we're in a lot of the same classes. And so my dislikes COVID right now is a big dislike. Yeah, yeah. But I love physics and math. That's my go to. Yeah. No, it's great stuff. I do agree with you in that. In that sense, we share   Pouya LJ  01:26 the love of physics and math. I so I don't know if you remember, Mr. He was on the podcast A while back. Now, now we got you. And hopefully bunch of other people join in the cohort. But yeah, today, actually, we're going to talk about something very relatively timely to all the COVID stuff. And so you have done some research and studies on, you know, mathematical modeling, modeling, which I think it's I guess people are getting tired of hearing it. That is surprising, because now politicians are talking about it, right? Like, oh, yeah, these are the numbers and mathematical models based on these, we're making these decisions. Never, you could you could never get politicians to pay so much attention to science, I suppose. As we do now. So now, all of that is about the spread, mostly, that's what they were talking about. But all of that also applies to vaccination strategies, which is probably the most timely because those are the decisions that politicians and then the world leaders around the world are making decisions on, right, all of these numbers that are jumped out. So now, for the audience, we are trying today, with the help of Tyler to make sense of all of this. What are these? You know, modeling that they talk about? What What is it behind the scenes, and to simplify it to a degree basically. So why don't you Why don't you go ahead and like started us start us off with? What was the what was the starting point of your research early on? And what were your, you know, thoughts going into it? And immediately after you started reading some papers and articles.   Tyler Martin  03:10 Yeah, definitely. So first, like, you watch the news. And you see all these politicians, like you said, talking about mathematical modeling, and then you go, what is even mathematical modeling in the first place. So then you have to do a little bit of research if when it first started. First off, we haven't even done a ton of research and mathematical modeling. It only started around, like going up around 40 years ago. And recently, there's two big definitions of mathematical modeling we can use today. One is the deterministic, and the stochastic, those are big fancy words. But they pretty much mean stochastic as in random randomness. So we can capture the randomness of humans, because no one can actually predict human behavior or human psychology. deterministic is a little less complex. It just kind of puts humans as a person with no emotion, no thoughts of what they're gonna do. They're just there. And then we can judge how a disease reacts from these two different types of models.   Pouya LJ  04:22 Right. Right. And do you know, so I mean, that's a natural, we're gonna delve into what they are in a second, maybe in further depth. But do you know, what are the when people talk about these models? Is it is it mainly stochastic or deterministic? Or sometimes just sometimes that are a combination of both? What are they usually talking about? Or what are the most effective   Tyler Martin  04:46 perhaps I don't know, the most effective from my point of view from what I found is stochastic modeling is most effective, although it's more complex, meaning we have to have big Fancy computers to run all our simulations, it's more effective in actually grasping our results and accurate results. What to when you compare it to deterministic modeling? I would say, for deterministic modeling is more better for handing paper. So if you're wanting to do a model by hand and paper, like we all do in class, then that's a good way. But stochastic modeling is definitely the way to go. When you have the time. Yeah.   Pouya LJ  05:30 Right. So so the deterministic model doesn't take into account just to clarify, right, it doesn't take into account human behavior. So for example, if you're supposed to be social distancing, your social this, that's absolute state, like, it doesn't consider that, you know, if you're on a lockdown, you're going into grocery, and you might happen to you remove your mask to unlock your phone. So none of these is accounted for. I mean, I guess it's not specifically accounted for in this   Tyler Martin  05:58 model, either.   Pouya LJ  06:00 But it basically treated as absolute steady state, meaning that it's everything being perfect. Is it? Well, I guess it depends on the assumptions you make to right you can also be assumption that, right? So so but whatever assumption you make is a fixed one in this in the, in the, what do you call it? The deterministic modeling? Right? Yeah, everything is fixed. For the stochastic model, we actually have a probability, right? So like, if you're more probable to go outside, or if you're more probable to stay inside, so it's not like you're fixed to do one certain thing. We have a probability density. Right? So instead of means 01, it's somewhere between zero and one. Exactly. Potentially. And our deals models like this stochastic ones. Are these probabilities dynamic, maybe changing in time? time?   Tyler Martin  06:51 Yeah, for sure. They change in time because people's reactions to a pandemic changes with time as well. So like, like we saw when the pandemic first started, a lot of people were outside and about not really caring. But as soon as the stay at home orders and stuff came along in the lockdowns, then we had to stay inside. So then our model has to account for that as well. Yeah.   Pouya LJ  07:17 Yeah, that's, that's fair. Is there anything specific you want to talk about in either of the two? In the technicality? So what are the factors that we're looking at? When we're saying probability of, for example, you mentioned human behavior, but what other factors are relevant here,   Tyler Martin  07:35 because actually, it depends on how complex you want to make your model. So if you want to make a super complex model, then you could take in a ton of factors like not only just human behavior, but like traveling around the world, and which planes travel to which countries and which are bringing back stuff. Or another common thing is for Western societies, we like to shake hands. And so for other societies, we don't have that type of contact. Like in Asian societies, it's normal to bow. So just like even the smallest things, just like that you can take into account into our model. And but as the more you take into account, the more complex it gets, so it's kind of like a trade off. Right, right.   Pouya LJ  08:23 Yeah. And then you did mention the, we get to be practicing soon enough, I suppose. But you didn't mention like, it is really deep. It's all these these models all started with some sort of assumption, right? And that assumption, determines what the what so let me take actually a couple of steps back for people who are not maybe thinking about so the idea is that you want to see, you want to model meaning try to predict what will happen given a certain guesses like so you you, you say okay, if there's no lockdowns, right, I'm correct me if I'm wrong here or if I'm slightly off, or you can add a caveat to it. But the idea is that if we make certain assumptions, meaning for example, there's no lockdowns, everybody's behaving like they would there's no pandemic at all right? What is the number what are the numbers are going to look like? What are the number, the number of people who are getting sick or who are dying, what demographics what you know, geographical neighborhoods, perhaps are the country, the city etc. And based on that, and then you combine and then you create different models with different sets of set of assumptions and find out what you want to do depending on what you want to achieve. So for example, you want so what is that absolutely no restrictions What? at all, one with minimal restrictions, maybe just socially distancing, and mask but then do whatever you want. Or maybe to 20% capacity, restaurants, whatever or absolutely locked out. So you create certain, you take certain assumptions, and you model these and you see, try to see into the future, essentially, and then try and then politicians come up and based on those predictions, if you will make certain decisions about what to do, what restrictions they were they want to impose on the population and whatnot. Is that Is that a fair summary of what what is the point of these models? in the first place?   Tyler Martin  10:25 Yeah, yeah, that was a great summary. And the big point is, is the relationship between the politicians and the scientific researchers, so if they don't have a good relationship, and they're not constantly communicating over what they're finding from these models, then the politicians will have a harder time making decisions on health policy issues, right. So that you have to have that constant communication going back and forth. So you can make those good decisions. Exactly. Now, that's   Pouya LJ  10:57 a very fair, fair point, actually. And so, now, I said all of this, to clarify all of this, but the beef I have with these models at some point, not not all the time. But first of all, they're not the so this is the this is the idea that some people talk about, actually, my dad always talks about this, he's like, the carpenter only cares about the wood, or the shoemaker cares about his shoes, and the electrician cares about his wires. At the end of the day, when you're talking to somebody whose job is to save lives, the only thing they're going to care about is to save lives. And yeah, the save lives doesn't comprehensively and take into account everything. It just takes into account saving lives who are being lost due to COVID. Period. Yeah, you know, like, if it if it. I mean, I'm not saying those people actually thinking like this, but that's their priority, because that's their job. The same way My job is, I don't know what it is. But right now to talk. So all I'm gonna focus on this fucking right.   Tyler Martin  12:00 So   Pouya LJ  12:03 my point is that, okay, all these things are getting done, I guess, supposedly, the politicians job is to take into account all of these models from the, you know, the, the scientific community from the, from the health community first in the first place. And then similar models are going to be done slightly different, obviously. But similar mathematical models are going to be done in on the economical side by the economist, or what is what are the impacts are going to be based on different assumptions, again, to the economy, and then eventually politician is going to be a general person, taking all of these into account, that's at least the idea, and then make some some decisions. Anyways, let's back up. So the beef that I have is that there, there, the there is no caveats, by when when you're talking about me, and you know it, the scientists know it, but when you're communicating this to the public, there's no caveat that all these modern things, though, they predict into the future, they have, they highly depend on your assumptions. And as you mentioned, ultimately, they're completely probabilistic. Like I, some of these models I have seen specifically restricted to Ontario is where we are in Canada. So and, and some of these don't take in taking don't take into account at all that they're, in fact, travelers coming from different countries. And I'm not saying they shouldn't, right. And, and they're, they're their only variable is human behavior due to lockdowns or restrictions or whatnot. And sure, that changes the numbers. But But let's let's let's toy around with no travels whatsoever, or where are these? You know, where are these outbreaks actually coming from? Is it is it because of travelers? Or is it not? Or is it because people are going to restaurant or is not? So I think this is very last Sunday? Again, and it portrays outside to the public so much that, you know, these are God given things, which I think and Would you agree with that they're they're very, very varying, depending on your assumptions.   Tyler Martin  14:12 Definitely assumptions is like, probably one of the biggest things like you can have a model that is almost exactly the same. But if you vary one thing, they can go completely different directions, like you can be off by if you're calculating the number of deaths, you can be off by quite a lot. So our underlying assumptions of our model are particularly important that we make accurate assumptions from what we actually perceive in the world.   Pouya LJ  14:45 Perfect. Now, I just wanted to make sure that I'm on track. They're not just spewing nonsense out there. Now, obviously now, the more interesting subject today has become the vaccination and vaccination. strategies, how are you vaccinated when vaccinate, who which population to vaccinate, which geographical location to vaccinate, etc. And so all of these are very good questions. And again, similar models are being done. And I know you were talking about before we started this conversation live recorded. You were talking about this new york times article, which was looking at different vaccination strategies. And essentially, they were trying so this is the title of the article, if I let me read it out, when when could the United States reach herd immunity? Well, question question mark. And the answer is, it's complicated. And hey, answer this. So first of all, let's define herd immunity. What is hurting herd immunity for those who don't know it?   Tyler Martin  15:43 Okay, I have to define one more thing before I define herd immunity. Okay, fair enough. Oh, first, there's a reproduction number. So a reproduction number basically just says, If I had the virus, how many people on average, would I pass the virus on to? So say, I have a reproduction number of two, that means me having the virus on average, I pass it on to two more people. So a herd immunity says that our reproduction number is less than less than one. So when we have less than one, then there's no chance of an outbreak or epidemic happening. And this means that there's less risk of the situation getting more serious. So herd immunity, basically just says, um, let me get a good definition that the state of the population where the fraction protected is sufficient to prevent outbreaks. And so herd immunity kind of just is basically what we want to reach from vaccination efforts. Yeah.   Pouya LJ  16:53 vaccination and the fact that people already some people already got and and recovered, right. And supposedly they can't get reinfected.   Tyler Martin  17:00 Exactly. So they're like, we have to take into account or remove population when we're doing these, the removed population is basically people who've gotten it and can't get it again, or people who have tragically passed away from it, or people who have immunity to it COVID. We don't know if it's any immunity to it yet. Like underlying immunity, but there are other diseases with immunity.   Pouya LJ  17:25 Right? Exactly. So so then that, and that, because there's a certain portion of the population whatever that number may be, that is removed, then they are not, which is the reproductive number drops below one which ends up and over time this virus decays, because it cannot. So if I get it, if my r naught is one mean, means that if the average is means that if I get it, I can only give it to one more person, so I'm only replacing myself, I'm not growing. And if it's less than one, on average, it means that I'm not even replacing myself. So over time, this is gonna vanish, because that's exactly okay. So yeah, right. So So in that sense, it's a combination of these, whether you're vaccinated and your immune or your so if I got it, and I come to contact with you, I'm assuming you're vaccinated, then you can't possibly get it. Whereas if you were not vaccinated, I would give it to you. And my Arnott would be at least plus one, because you're not you. You are not vaccinated. You're not Yeah. Yeah, that immune not being immune, or whatever. So either that person has passed. So it doesn't even exist to, you know, contract it, or they already got it. So there they have immunity because they cannot be reinfected. At least for a period of time. We don't know what the period of time is exactly. But let's just say for now, for the purpose of this argument, let's just say it's indefinite, and or persons vaccinated. Again, same idea. Now, now, let's go back to the article, I suppose and you can take the range from there, but I'm going to reiterate the question. So they were trying to researchers were trying to see when, you know, says reaches this herd immunity, meaning that the reproductive number will be less than one. So eventually the virus will die out over a period of time, and it definitely cannot grow. And their conclusion in one sentence was this complicated. So why did they say that on what, what what were they looking at? What they find what happened? Go ahead.   Tyler Martin  19:31 Yeah, it is actually very complicated. I think as a Canadian to looking at what the states is doing is definitely beneficial for us. Because we don't vary a ton from them. Some of the states have a lot more relaxed. laws as in like, they can walk around without masks and stuff, but we're actually fairly the same. So just looking at this is very interesting. One thing they want to look at was They sped up the rate of vaccination. So on average, the US is administering about 1.7 vaccination shots a day. So if they continue to do this, their reach herd immunity by July, and around 100,000 people pass. However, if they sped it up, it would increase to around 13 million shots per day, then they reach herd herd immunity By May, and 90,000 people with pass. And if they increased it even more, which is very improbable to 5 million day, that's kind of insane. They reach herd immunity by enpro. And 80,000, people would pass. I think the more interesting part of this article is looking up is looking how herd immunity and vaccination along with with relaxing social distancing measures comes into effect. So, if you actually keep 1.7 million shots per day, and then look at relaxing your social distancing measures, they return her to me by July, like I said before, 100,000 people would pass. But if you lift restrictions, when 15% of the population is vaccinated, then you reach herd immunity by June, so a little earlier than July and 17. Or say 170,000 people have passed. So that's a big jump from 100,000. And then even more interesting, if they end all restrictions right now. Then they reach herd immunity by May. But in that case, 3200 or 320,000 people who pass so these jumps to me are just like, insane. When you look at how many people would pass if you just relax the restrictions on social distancing?   Pouya LJ  22:15 Mm hmm. Right. And, yeah, that isn't saying the same thing we were talking about. The initial assumptions can change a lot. And the same thing happens in the vaccination strategies and social distance. So I think so. Now, I don't know if I got it. So with currently with the with, what do you call it, their current rates of vaccination? And the if we don't, if we keep the measures in place, like the social distancing, or at least the basic measures, such as the social distancing and the masks, yeah, now, that number of deaths in the United States until the herd immunity is achieved is 100,000. People. Yeah. Right. So if so let's let's pick this again. So if the same rate of giving vaccine to the US population is continued, not increased, not decreased, which is 1.7 million per day, which is impressive, by the way, is a lot. Yeah. Do they have a lot of big population too? Yeah. Bigger than Canada. I mean, so anyways, so 1.7 million per day until the next foreseeable future, like unless next few months, and then you still do social distancing, you still do wear masks? Maybe not no major parties or anything. And then the estimated number of deaths from COVID until July which is the time that they reach herd immunity is 100,000. But if they don't take the if they ease up the measures, meaning don't wear masks, maybe don't social distance, maybe throw away some parties but not a lot then then that then that number jumps by almost twice and 171 point seven 170 1000 also, which is and and and and now let's say we keep this social distancing measures and hold on a second. Let me see if I get this article. Right. Excuse me. Now if you do increase the supply to 3 million a day, yeah, but that but then they didn't do any investigation as to what happens if you do measures or don't do measures today? No. If you increase the supply, but also keep the measures   Tyler Martin  24:39 Oh, no, they didn't. They didn't do that. Okay,   Pouya LJ  24:41 yeah, okay, okay. Okay. They didn't do that. Okay, but it is very interesting and okay, but if they do increase the however if they do almost double the shots, although they reach herd immunity much sooner, still number of deaths is like 10,000 people. That's 490 Yeah. That doesn't make a lot of sense. How's that? You know,   Tyler Martin  25:03 I'm not too sure. That would also depend on what we're talking about before their underlying assumptions. Right. Right. So   Pouya LJ  25:08 they didn't talk about those assumptions, I suppose.   Tyler Martin  25:12 Right? And yeah, they Yes, they do. Put it in the beginning a little bit, but not too   Pouya LJ  25:19 long. Because,   Tyler Martin  25:20 yeah, they also do cover the different types of variants. Oh, interesting. So with the current variant, like I said before, 100,000 by July. So 100,000, people would pass, and they'd return immediately by July. But for the more contagion, like, very more contagious variants with precautions, and they in the states gets all of those variants, they would have around 200,000 people pass, and they reach herd immunity by July. But if they have the most contagious variants with no precautions at all, they reached an insane number of 530,000 people. And the herd immunity by April.   Pouya LJ  26:16 So more number of people in short amount of time, basically. Mm hmm.   Tyler Martin  26:20 Exactly. Yeah, it's it's quite a number to look at. The death toll at that point would be just insane.   Pouya LJ  26:29 So in a way, the the the immunity due to getting the virus and recovering from it as actually acting much faster than the vaccination process, basically. So that's why they're getting to the herd immunity earlier, because the virus is infecting everybody and whoever survives just as immune. So the immunity increases fast moving, but, but obviously, a lot of people are   Tyler Martin  26:53 doing cost. Yeah,   Pouya LJ  26:54 yeah. Well, that's, that isn't, like these numbers that that is looking at these numbers is actually quite, we will, by the way, I should say this, we will put the link a link to this article in the show notes. So if anybody wants to go and look at these numbers, themselves, feel free to do so. Okay, let's, let's now move move forward. Unless you want to talk about this article more. I don't know if there's anything left? No, no. Okay. Yeah. Okay. So let's move forward a little bit and talk about what are the discussed around the table, if you will, the different strategies of vaccinations? And what is the argument for each of them in terms of who to vaccinate, which areas to vaccinate, why and why not? Etc.   Tyler Martin  27:40 Yeah, there's a couple different methods of vaccination. One very promising one is called a focus method of vaccination. That's where you focus in on a certain group of people. Give them all the vaccination that we have. And then once there are not basically as getting better, then you move out to a little more like diverse, further out rural areas and start vaccinating back. Oh, so   Pouya LJ  28:09 it's mostly thinking geographically, right? Yeah. If you're, if you're in our big, if you're in a big, congested populated city, for example, let's say Toronto, New York, whatever, then you focus on that and leave the rest of the state under province a lot, right? That's the idea. Okay. Okay. Go ahead.   Tyler Martin  28:26 And so like that strategy is actually one of the more promising strategies. So we actually, as Canadians, we see this happening now. Nova Scotia is giving up some of their vaccination to other places in Canada, so that we can actually get to a herd immunity for Canadians as a whole faster. Yeah. Interesting.   Pouya LJ  28:51 And do you know it is now across Canada? One story, but within Ontario, do you know if they're using this strategy or not? Like the focus wrench?   Tyler Martin  29:00 I'm not too sure. From what I know, it's more of just everyone gets to or the most people. important people get a first as in the people who are doctors who are Yeah, doctors, frontline workers, or people who need it, like the elderly need it. So people like that would get it first. So it's more than not focusing on a particular area. They're just trying to get the people who are who   Pouya LJ  29:34 can't think of the word more vulnerable, maybe Exactly. Yeah. Yeah. Look, I get I get the I think the doctors and nurses is a bit clear to me because we want them to be healthy to take care of all of us, not just for COVID for everything really. So that's even from a very selfish point of view, not acknowledging their sacrifices, complete selfish point of view. You still want them to get it first. I think the frontline worker Especially doctors and nurses, and the rest of the frontline workers, perhaps to paramedics, police officers, etc. So those are because there's a very important, like they have to be able to function. And at the same time, like very urgently be able to function at the same time, they're much higher at much higher risk. So that I think it makes complete sense. But after that, although there are there really are most vulnerable, but they're not mixing as much. I'm not so convinced that the focus after that prioritizing those people, the focus approach will not be more successful. But they still also, by the way, so they still do these similar modeling, get taking different assumptions, right, for example, assuming that you give it to elderly and assuming that you give it to like a focus strategy to give it to parts of the population that are mixing and mingling more. Maybe there are denser neighborhoods, for example, the basically the places that have the highest numbers, geographical places, right? Yeah. So get them. You know, as soon as they get contained, they can't really move it on either. Right? as much. What do you think on that? That would be your thoughts? personally.   Tyler Martin  31:16 I think mathematical modeling wise, it's a, it's a better way to get down to death toll to vaccinate the elderly first, like if you think about it, our death toll will go down. If we vaccinate the elderly first compared to everyone else, because the elderly are dying the most Sure. So if you're just looking at it, from a mathematical modeling point of view, it's like if we want to get down the death toll, vaccinate the elderly. In general, though, I'm not too sure. I would say the focus strategies, probably the better strategy to go to, but, um, after the frontline workers get their vaccination, I don't know. I'm not too sure. Who should get it after that. Right. Really not. It's a very complicated story, obviously. Yeah. And you don't get on people's feet by saying that you shouldn't get vaccinated. Yeah, so yeah, no, I   Pouya LJ  32:17 mean, I'm definitely not saying that I think focus is the right way to go. I'm just saying it's not really clear which one is? And maybe there is no one right answer or one wrong, and maybe both answers are wrong, or both of them? Yeah. So it's just a slightly better, I think they're actually at the end of the day in the long margins of things, depending on what you're looking at. Yeah, sure. Maybe if you're here, purely looking at death tolls due to COVID specific because on the other hand, look, there have been reports and studies done on the the side effects of this whole COVID thing like not, you know, not just the deaths and despair from the COVID, but also that loss of job economic distress, you know, suicide rates, people who couldn't get their scans and for cancer, etc, their operations. And that all of that is obviously costly as well, we cannot just ignore that, although the forefront is to COVID disaster, but it has, you know, side effect that is rippling through our societies and communities as well. Right. So we definitely would like to, if we do like to look at it comprehensively, I think, at the end of the day that the approaches are not really clear cut. And that is what you were mentioning, like in terms of, if you want to introduce more variables, it just keeps getting more complicated. Exactly, yeah. And perhaps even impossible to to predict anything with any good amount of good measure of accuracy. So yeah, I guess I bought my way complicated. It's not as easy as this is the right way to go. So maybe we can relinquish that arrogance, I suppose to a degree, because it is a complicated problem. So yeah. Is there anything we left on the vaccination fund that you wanted to talk about that we   Tyler Martin  34:14 didn't? I think we pretty much covered it all. By no means am I also a vaccination expert. No, we're just   Pouya LJ  34:22 discussing our own, you know, experience with these articles.   Tyler Martin  34:27 Yeah,   Pouya LJ  34:28 yeah. That's good. Because I think, two to a high degree because you actually did study these matters to a degree. I mean, again, I'm not quite we're not claiming to be experts, neither of us but because you have done specifically the math, math, mathematical modeling. I'm sure you have more understanding than many including myself. So it's good to. We don't need to listen to the greatest experts to increase our knowledge. I think it doesn't. That's as long as you know more than me. I can learn from you. That's it. Yeah.   Tyler Martin  35:00 Yeah, exactly.   Pouya LJ  35:02 Right. Okay, yeah. So I think it's a good place to stop, like, end that conversation. I'm gonna give you a few moments after this to, you know, gather your thoughts. final words, if you want to save, but before that, I think it's a good point. I think this was a good. Good understand, I think you had this epiphany, I suppose. And I definitely did that it is, in fact, a complicated matter. It's not as easy as 123 go. It's a bit requires in depth contemplation. And at the end of the day, there are going to be mistakes, there are going to be errors, there are going to be things that are not going to get to the right answer. Or what is even the right answer. Right. So all of these are subject to a lot of assumptions. And I think that was that was the some of the most important epiphany of all in this in this journey today. Exactly. Do you have any final thoughts that you want to add to it?   Tyler Martin  36:07 Maybe one last thought, and that is that no mathematical model can accurately predict the future. Like no matter what if we take in the as many complex variables as we can, we can predict the exact amount of people who will die. So take every everything these politicians say about mathematical modeling with a grain of salt when they're saying it, but some of them are actually fairly accurate at the same time.   Pouya LJ  36:34 Yeah. They're the best worst thing we have. Exactly.   Tyler Martin  36:39 Exactly. Okay.   Pouya LJ  36:40 Fair enough. Okay. Thanks. Thanks, Tyler. It was a pleasure talking to you today.   Tyler Martin  36:46 It was pleasure. Thank you so much for having me on.   Pouya LJ  36:48 No problem. I'm thank you all for tuning in and listening to yet another episode and I hope you enjoyed it. Leave your comments, suggestions, questions below there are there's going to be shownotes as I mentioned, which we're going to include the New York Times article in it and until later episode, have a good one. Take care

In this episode, Gabriela Rucker, Eden Yonas, and Genia Kim speak with Dr. Talea Mayo, an Assistant Professor in the Department of Mathematics at Emory University. Dr. Mayo specializes in developing hurricane storm surge models, which can be used to investigate how climate change impacts coastal flood risk, build resilient infrastructure, and create effective response policies. During this interview, Dr. Mayo discusses her work, her personal experience with Hurricane Irma, and the environmental justice impacts of hurricanes. Learn more about Dr. Talea Mayo: https://www.taleamayo.com/

About the guest: An engineer, a tech idealist, and an energetic leader - our guest today is Yash Khandor. If you feel there's always a fixed path to success and you can't make the cut, talk to this guy. From studying Chemical Engineering as an undergrad to a Master's in Mathematical Modeling and Optimization from Carnegie Mellon University, he is currently an Engineering Manager at Mark43 and the Founder/CEO of International Cricket Network - ICN360. While the journey might look haphazard, there's a method to his madness. Website: https://www.icn360.com Facebook: https://www.facebook.com/cricketnetwork360 Instagram: https://www.instagram.com/icn360 Twitter: https://www.twitter.com/cricketnet360 LinkedIn: https://www.linkedin.com/company/43214637 Other Resources mentioned in the episode: 1. Managing Humans: Biting and Humorous Tales of a Software Engineering Manager by Michael Lopp 2. The Manager's Path: A Guide for Tech Leaders Navigating Growth and Change Book by Camille Fournier 3. The Making of a Manager: What to Do When Everyone Looks by Julie Zhuo Timestamps: [02:00] Intro [04:00] Pivoting careers [05:00] What is mathematical modeling? [07:00] Software Engineering at SOTI [12:00] Learning on the job [15:00] Internet of Things [18:00] Immigrate to Canada [22:00] ICN360 [31:00] Creating niche content [38:00] Importance of having a process [40:00] Mental Model to use in everyday life [42:00] Things software engineer should know [44:00] Top 3 ------------------------------------------------------------------ About What the HAT!? We are three friends who met in our engineering college catching up with our old friends and acquaintances in this show. Each guest has a different journey, different story, and different insights. We are connecting and learning from people who have carved their journeys from creating funded startups in India to key roles in big companies. We are talking to people who went to Ivy League colleges and are academicians in India. We are working on gathering stories from these great minds. This is the podcast for you if you are currently pursuing or have completed engineering. If you haven't studied engineering, this podcast is still for you, as we will dwell deep into various industries and sectors such as finance, technology, supply chain, manufacturing, chemical, education, and a lot more. Each journey is inspiring. Each story gives you an opportunity to learn something new. Extraordinary insights from not so ordinary people. Read more about What the HAT!? on the website: https://www.whatthehatpodcast.com SUBSCRIBE TO WHAT THE HAT!? Listen to What the HAT!? on Anchor: https://anchor.fm/what-the-hat-podcast Listen to What the HAT!? on Spotify: https://open.spotify.com/show/0JLZXaAgrIDtbxXVtqemWh Listen to What the HAT!? on Apple Podcasts: https://podcasts.apple.com/in/podcast/what-the-hat/id1513959425 Listen to What the HAT!? on Google Podcast: https://podcasts.google.com/?feed=aHR0cHM6Ly9hbmNob3IuZm0vcy8yMWFkMDA5MC9wb2RjYXN0L3Jzcw%3D%3D All other streaming platforms: https://linktr.ee/whatthehat/ --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Este podcast é oferecido por HiDoctor – o software médico mais usado em consultórios e clínicas no país O resumo da semana de 11/01 a 15/01 traz as seguintes publicações: - Estudo identificou oportunidades perdidas de prevenção da hospitalização por insuficiência cardíaca: pacientes com IC apresentaram sinais de alerta prévios não reconhecidos pelos médicos (JACC: Heart Failure) - Novo teste de escaneamento encontra células de câncer de próstata ocultas no corpo (The New York Times) - Tratamento com hormônio do crescimento na infância está associado a um risco aumentado de morbidade cardiovascular em longo prazo (JAMA Pediatrics) - Estudo mostra que o burnout é empiricamente distinto da depressão e ansiedade em profissionais de terapia intensiva, destacando a necessidade de triagem (JAMA Network Open) - Estudo sugere que infecção do corpo carotídeo pelo SARS-CoV-2 pode ser responsável pela hipoxemia silenciosa em pacientes com COVID-19 (Function) - Consumo excessivo de álcool no início da adolescência está associado a resultados deletérios na maturação microestrutural da substância branca (JAMA Psychiatry) - Estudo estima que nova variante do SARS-CoV-2, VOC 202012/01, seja 56% mais transmissível do que as variantes preexistentes (Centre for Mathematical Modeling of Infectious Diseases) Veja mais notícias em news.med.br.

In this week's tea-riffic episode, researchers use physics and computer simulations to develop the perfect shot of espresso! Paper referenced: "Systematically Improving Espresso: Insights from Mathematical Modeling and Experiment"

C'est la dernière ! Un grand récap de tout ce qu'on a vu sur la morale et le travail des doux rêveurs qui essaient de l'expliquer avec les lois naturelles. Ce n'est que grâce à vos dons que je peux faire des vidéos ! Si vous aimez mon travail et souhaitez qu'il continue, n'hésitez pas à me soutenir financièrement sur uTip ou Tipeee : https://utip.io/homofabulus https://tipeee.com/homofabulus/ (Vous pouvez aussi y acheter des t-shirts et mugs stylés. Merci à toutes et tous pour votre soutien !) Facebook : https://www.facebook.com/H0moFabulus/ Twitter : https://twitter.com/homofabulus pour les infos strictement liées à la chaîne et https://twitter.com/stdebove pour mon compte perso alimenté plus régulièrement Insta : https://www.instagram.com/stephanedebove/ Youtube : https://www.youtube.com/channel/UC-Dmq5q3-FIBknv1TVIR__Q Musique :  Dapper - Gregory David Réfs : [1] https://en.wikipedia.org/wiki/Value_of_life#Estimates_of_the_value_of_life [2] A Biologist's Guide to Mathematical Modeling in Ecology and Evolution, by Sarah P. Otto Troy Day. Ou un exemple de modèle mathématique pour l'évolution de la coopération en particulier : Geoffroy, F., Baumard, N., and J.B. André. 2019. Why cooperation is not running away. Journal of Evolutionary Biology

With storms named Gamma and Delta making their way to US shores – we really couldn't have timed this release better from a hurricane-content-meets-alts-investment podcast; it's the: perfect storm (buh-dum-ch). Today's guest is creator of Weather Underground and the Cat 6 blog, a person who has flown through an actual hurricane, and a whiz at modeling weather data in a way that us non-meteorologists can understand – Dr. Jeff Masters. In today's podcast we're talking with Jeff about hedging commodities based on storms, water futures contracts, the Cat 6 blog, historical context of extreme weather events, the transformation of weather modeling, heat output dissipating from WFH, fat tails based on 1-in-100-year events, economic fragility around climate change, hurricane Hugo, weather data inputs that matter, starting weather underground, Jeff's upcoming book Eye of the Superstorm, hedge fund world catastrophe bonds, Jeff's most dangerous storm experience, and the impact of COVID on weather modeling. Follow along with Jeff and subscribe to his content at Yale Climate Connections. And last but not least, don't forget to subscribe to The Derivative, and follow us on Twitter, or LinkedIn, and Facebook, and sign-up for our blog digest. Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit www.rcmalternatives.com/disclaimer Chapters: 00:00-01:29 = Intro 01:30-20:35 = Background, “The Final Flight”, & Weather Underground 20:36-32:19 = Building Weather Models 32:20-57:35 = Storm Financial Impacts, Catastrophic Bonds & Fat Tails ( It's a complex system) 57:36-1:04:10 = Water Shortages & What We Need to Do 1:04:11-1:14:02 = Favorites

Dr. James Cooke is a Neuroscientist, Writer & Speaker, Focusing on Consciousness, Meditation, Psychedelic States, Reconciling Science & Spirituality. He posits the Living Mirror Theory of Consciousness, that "All Living Things Need to Know Their World." He splits his time between London and the mountains of Portugal where he is building a retreat center, The Surrender Homestead. https://drjamescooke.com YouTube ► https://bit.ly/CookeYT Writing ► https://realitysandwich.com/author/j-cooke SHOW NOTES

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.28.225755v1?rss=1 Authors: Lopez, G., Nicolas, A., Roman, G., Godinez, R., Castro, M. A. Abstract: With an aperiodic, self-similar distribution of two-dimensional arrangement of atrial cells, it is possible to simulate such phenomena as Fibrillation, Fluttering, and a sequence of Fibrillation-Fluttering. The topology of a network of cells may facilitate the initiation and development of arrhythmias such as Fluttering and Fibrillation. Using a GPU parallel architecture, two basic cell topologies were considered in this simulation, an aperiodic, fractal distribution of connections among 462 cells, and a chessboard-like geometry of 60x60 and 600x600 cells. With a complex set of initial conditions, it is possible to produce tissue behavior that may be identified with arrhythmias. Finally, we found several sets of initial conditions that show how a mesh of cells may exhibit Fibrillation that evolves into Fluttering. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.17.205351v1?rss=1 Authors: Latash, E. M., Lecomte, C. G., Danner, S. M., Frigon, A., Rybak, I. A., Molkov, Y. I. Abstract: Rhythmic limb movements during locomotion are controlled by a central pattern generator (CPG) circuits located in the spinal cord. It is considered that these circuits are composed of individual rhythm generators (RGs) for each limb interacting with each other through multiple commissural and propriospinal circuits. The organization and operation of each RG are not fully understood, and different competing theories exist about interactions between its flexor and extensor components, as well as about left-right commissural interactions between the RGs. The central idea of circuit organization proposed in this study is that with an increase of excitatory input to each RGs (or an increase in locomotor speed) the rhythmogenic mechanism within the RGs changes from "flexor-driven" rhythmicity to a "classical half-center" mechanism. We test this hypothesis using our experimental data on changes in duration of stance and swing phases in the intact and spinal cats walking on the ground or tied-belt treadmills (symmetric conditions) or split-belt treadmills with different left and right belt speeds (asymmetric conditions). We compare these experimental data with the results of mathematical modeling, in which simulated CPG circuits operate in similar symmetric and asymmetric conditions with matching or differing control drives to the left and right RGs. The obtained results support the proposed concept of state-dependent changes in RG operation and specific commissural interactions between the RGs. The performed simulations and mathematical analysis of model operation under different conditions provide new insights into CPG organization and limb coordination during locomotion. Copy rights belong to original authors. Visit the link for more info

The onset of the COVID-19 pandemic this past spring saw universities across the country close their campuses and teachers rapidly transition their curriculums to a virtual classroom setting. Now, after months of social distancing and online learning, these same institutions are looking ahead to the fall semester. Simultaneously however, as states are beginning the process of reopening, many at different rates, we are also seeing a spike in infection rates. So in the face of this, can universities safely reopen their campuses? The answer may truly surprise you. Joining me to discuss how his university is approaching the upcoming fall semester is Peter Frazier, professor at Cornell University in Ithaca, NY.

The mathematics curriculum of today was created before computers. Conrad Wolfram has written a book, The Math(s) Fix and how AI can be part of this process of learning new math. He says we're "making humans into third rate calculators instead of helping humans be problem solvers." He also argues that math should be approachable for everyone and we spend too much time in schools on the wrong tasks. We even talk about some of the charts and numbers shared during the covid19 health crisis and the need for critical thinking in every area of society. www.coolcatteacher.com/e680 Sponsor: Welcome TGR Foundation and Discovery Education as a sponsor to my podcast. For nearly 25 years, the TGR Foundation, a Tiger Woods Charity, visualized a world where opportunity is universal and potential is limitless. Their mission is to empower students to pursue their passions through education - instilling in them the strength and skills to persevere and define their own path. Their award-winning curricula has already reached more than one million students.  Now in the age of COVID-19 the TGR Foundation and Discovery Education introduce the first module in a new series of no-cost digital professional learning resources, empowering educators with new strategies to support student success far beyond school campuses. The PD series provides every educator, especially those from under-resourced communities, the access and materials needed to transform teaching during COVID-19 and beyond. Check it out at coolcatteacher.com/tgr. Conrad Wolfram - Bio as Submitted Conrad Wolfram – Strategic Director and European CEO/Co-Founder, Wolfram Research Conrad Wolfram, physicist, mathematician and technologist, is Strategic Director and European Co-Founder/CEO of Wolfram - the “computation company” behind Mathematica, Wolfram Language and Wolfram|Alpha (which powers knowledge answers for Apple's Siri) for over 30 years. Wolfram pioneers new approaches to data science and computation-based development, with technology and consulting solutions that drive innovation in analytics, software development and modelling. Working with start-ups to Fortune 500 companies, it spans industries as diverse as medicine, finance and telecoms. Conrad is recognised as a thought leader in AI, data science and computation, pioneering a Multi-Paradigm data science approach. Conrad is also a leading advocate for a fundamental shift of maths education to become computer-based or alternatively introduce a new core subject of computational thinking. He founded computerbasedmath.org and computationalthinking.org to fundamentally fix maths education for the AI age - rebuilding the curriculum assuming computers exist. The movement is now a worldwide force in re-engineering the STEM curriculum. His groundbreaking book 'The Math(s) Fix - an education blueprint for the AI age' www.themathsfix.org is released on 10th June. Conrad regularly appears in the media to talk about subjects ranging from decisions and data science to 21st-century education. He attended Eton College and holds degrees in Natural Sciences and Maths from the University of Cambridge. Disclosure of Material Connection: This is a sponsored podcast episode. The company who sponsored it compensated me via cash payment, gift, or something else of value to include a reference to their product. Regardless, I only recommend products or services I believe will be good for my readers and are from companies I can recommend. I am disclosing this in accordance with the Federal Trade Commission 16 CFR, Part 255: "Guides Concerning the Use of Endorsements and Testimonials in Advertising."

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

Throughout history mankind has tried to predict the future. From reading the stars, to crystal balls to even reading the entrails of dead animals. The ways and methods have changed from time to time. But the objective has always remained the same: try to peer into the future, and see what lies ahead. At 10 this morning, in the meeting room of the US Senate, we will get the latest version of peering into the future. On display will be the latest, and most advanced version of future prediction, Mathematical Modeling. Or more precisely, Econometrics or Quantitative Economics, “Quant” Econ if you will.

John is back to show the how machine learning can vastly speed up the selection of mathematical models. His presentation provides great visual intuition on how machine learning methods can help select mathematical models, even as measurement noise increases. It's a huge improvement over selecting models by hand!

John discusses his work in the precision medicine program at the Statistical and Applied Mathematical Sciences Institute (SAMSI) to model wound healing. He describes the physiological mechanisms of wound healing and how to select a applications that are appropriate for mathematical modelling.

Learn about whether it’s a good idea to rub dirt on your wounds; how funny memes can help save endangered species like the proboscis monkey; and how space travel changes the shape of astronauts’ hearts. When it comes to wounds, science says "rub some dirt on it" might be good advice by Cameron Duke Dillow, C. (2013, May 23). Got A Wound? Science Says Rub Some Dirt In It. Popular Science; Popular Science. https://www.popsci.com/science/article/2013-05/antibacterial-clays-can-kill-antibiotic-resistant-e-coli-and-mrsa/  Juang, L. J., Mazinani, N., Novakowski, S. K., Prowse, E. N. P., Haulena, M., Gailani, D., Lavkulich, L. M., & Kastrup, C. J. (2020). Coagulation factor XII contributes to hemostasis when activated by soil in wounds. Blood Advances, 4(8), 1737–1745. https://doi.org/10.1182/bloodadvances.2019000425 Otto, C. C., & Haydel, S. E. (2013). Exchangeable Ions Are Responsible for the In Vitro Antibacterial Properties of Natural Clay Mixtures. PLoS ONE, 8(5), e64068. https://doi.org/10.1371/journal.pone.0064068 Soil in wounds can help stem deadly bleeding. (2020). EurekAlert! https://www.eurekalert.org/pub_releases/2020-04/uobc-siw042420.php  Funny memes can help people care about unpopular and unappealing species by Kelsey Donk The new Noah’s Ark: beautiful and useful species only. Part 1. Biodiversity conservation issues and priorities. (2011). Biodiversity. https://www.tandfonline.com/doi/full/10.1080/14888386.2011.642663  Lenda, M., Skórka, P., Mazur, B., Sutherland, W., Tryjanowski, P., Moroń, D., Meijaard, E., Possingham, H. P., & Wilson, K. A. (2020). Effects of amusing memes on concern for unappealing species. Conservation Biology. https://doi.org/10.1111/cobi.13523  Proboscis Monkey | National Geographic. (2010, November 9). Nationalgeographic.com. https://www.nationalgeographic.com/animals/mammals/p/proboscis-monkey/  Proboscis monkey meme mentioned: Typowy Polak: Janusz Nosacz - Posts. (2018). Facebook.com. https://www.facebook.com/TypowyPolakJanuszNosacz/posts/1658533764253793/  Space Travel Changes The Shape of Astronauts' Hearts by Haley Otman Study Finds Astronauts’ Hearts Become More Spherical in Space - American College of Cardiology. (2014). American College of Cardiology. https://www.acc.org/about-acc/press-releases/2014/03/29/09/09/may-hearts-in-space  May, C., Borowski, A., Martin, D., Popovic, Z., Negishi, K., Hussan, J. R., Gladding, P., Hunter, P., Iskovitz, I., Kassemi, M., Bungo, M., Levine, B., & Thomas, J. (2014). Affect of Microgravity on Cardiac Shape: Comparison of Pre- and In-Flight Data to Mathematical Modeling. Journal of the American College of Cardiology, 63(12), A1096. https://doi.org/10.1016/s0735-1097(14)61096-2  Subscribe to Curiosity Daily to learn something new every day with Cody Gough and Ashley Hamer. You can also listen to our podcast as part of your Alexa Flash Briefing; Amazon smart speakers users, click/tap “enable” here: https://www.amazon.com/Curiosity-com-Curiosity-Daily-from/dp/B07CP17DJY

Today’s “Think” show is dedicated to talking with experts to try to explain some of the more mysterious elements of the coronavirus. Host Krys Boyd talks with Dr. Peter Hotez, founding dean and chief of the Baylor College of Medicine National School of Tropical Medicine, about the push for a COVID-19 vaccine. Wired senior correspondent Adam Rogers talks about the math behind epidemiological models. And Courtney Cogburn, associate professor of social work at Columbia University, discusses why black and Latino Americans are disproportionately affected.

Incorporating modeling activities into classroom instruction requires flexibility with pedagogical content knowledge and the ability to understand and interpret students’ thinking, skills that teachers often develop through experience. One way to support preservice mathematics teachers’ (PSMTs) proficiency with mathematical modeling is by incorporating modeling tasks into mathematics pedagogy courses, allowing PSMTs to engage with mathematical modeling as students and as future teachers. Eight PSMTs participated in a model-eliciting activity (MEA) in which they were asked to develop a model that describes the strength of the magnetic field generated by a solenoid. By engaging in mathematical modeling as students, these PSMTs became aware of their own proficiency with and understanding of mathematical modeling. By engaging in mathematical modeling as future teachers, these PSMTs were able to articulate the importance of incorporating MEAs into their own instruction. Special Guest: Kimberly Corum.

Two major challenges in mathematics teacher education are developing teacher understanding of (a) culturally responsive, social justice–oriented mathematics pedagogies and (b) mathematical modeling as a content and practice standard of mathematics. Although these challenges may seem disparate, the innovation described in this article is designed to address both challenges in synergistic ways. The innovation focuses on a mathematical modeling task related to the ongoing water crisis in Flint, Michigan. Through qualitative analysis of instructor field notes, teacher- generated mathematical models, and teacher survey responses, we found that teachers who participated in the Flint Water Task (FWT) engaged in mathematical modeling and critical discussions about social and environmental justice. The evidence suggests that integrating these 2 foci—by using mathematical modeling to investigate and analyze important social justice issues—can be a high-leverage practice for mathematics teacher educators committed to equity-based mathematics education. Implications for integrating social justice and mathematical modeling in preservice and in-service mathematics teacher education are discussed. Special Guest: Julia Aguirre.

Biomedical data scientist Sylvia Plevritis is an expert in computational modeling of cancer risk and treatment options hidden in the remarkable quantity of data available today. Rarely is a tumor made up of a single mutation, she says, but more commonly of a mix of different mutations. Such heterogenous tumors may require complex combinations of drugs to produce the most effective treatments. That's where computers can help. Using mathematical simulations, Plevritis is helping patients and their doctors understand the genetic makeup of a given cancer for the purpose of identifying drug combinations that stand a better chance of success. Some of the models Plevritis works with can be run in an hour or less and yet return invaluable guidance that can save a patient's life. Plevritis says these computational approaches can even help those without cancer understand their inherent genetic risks to assess whether and when additional screening or risk-reducing interventions are warranted. Join host Russ Altman and biomedical data scientist Sylvia Plevritis as they dive into the promising intersection of computers and cancer care. You can listen to The Future of Everything on Sirius XM Insight Channel 121, iTunes, Google Play, SoundCloud, Spotify, Stitcher or via Stanford Engineering Magazine.

Margaret Brandeau may carry a business card that reads Professor of Management Science and Engineering, but her expertise is in using complex systems models to solve challenges in public health policy. For instance, she recently created a sophisticated computer model of the national opioid crisis, which led her to the stark –and surprising – conclusion that it may take a short-term rise in deaths to ultimately reduce them. She didn't come to that conclusion lightly, but made no less than 10 models of drug-user behaviors to analyze interventions. Nonetheless, each model led her to the same basic conclusions. First, policies are needed that lead to cutbacks in the number of prescriptions of opioids for pain management. Second, fewer prescriptions of opioids for pain management will cause some individuals to turn to more-deadly heroin. Third, because of this unintended consequence, it is essential to also scale up treatment for opioid-addicted individuals. But her fourth finding was the most sobering of all: No one of these policies will suffice; they must all be combined if we are to curb the opioid epidemic – and the epidemic is not likely to abate significantly anytime soon. Mathematical modeling is an art, Brandeau says, but it's a powerful art that is only going to grow in influence. Her advice for those looking to solve big problems – from reducing sodium intake to battling the return of measles – is to start out simple. Know what question you want to answer and create a model that captures just the most salient elements of the problem. Things will flow from there. Join host Russ Altman and mathematical modeling expert Margaret Brandeau for a deep look at the many ways algorithms are changing our understanding of and approaches to the challenges of public health. You can listen to the Future of Everything on Sirius XM Insight Channel 121, iTunes, SoundCloud and Stanford Engineering Magazine.

Stanford University researchers developed a mathematical model that could help public health officials and policymakers decrease the effects of the opioid epidemic, which took the lives of roughly 49,000 Americans in 2017. The model includes data about addictions, prescriptions and overdoses in the United States which can be used for “what if” scenarios similar to those that business leaders run through to project how changing product features or prices affect sales and profits, said Margaret Brandeau, PhD, the Coleman F. Fung Professor in the School of Engineering and a professor of management science and engineering who worked on the study. The paper cites the hard facts of the opioid crisis: Between 1990 and 2010, there was a 400 percent spike in prescriptions for opioid painkillers. Today, roughly 3.5 million Americans suffer from an addiction to opioids as a result of being exposed to opioid pills. Yet, as doctors have begun responding to the crisis by reducing prescriptions, overdose deaths have increased because those addicted to pills and unable to obtain prescription medication, are buying heroin as an alternative. And much of the heroin supply found in the US is now laced with fentanyl, making it 50 times more potent. Unfortunately, the model indicates the interventions studied can only make a small dent in the death toll which just goes to show the magnitude of the crisis. Researchers hope this model can aid policymakers in selecting the best mix of interventions to fight the epidemic. Today Greg is joined today by Allison Pitt, one of the research team members from Stanford University to discuss this innovative study. Listen as they discuss how it can be used by policy makers and community leaders to help ease the opioid crisis in our country.

Accomplished astrophysicist and mathematician Larry Liebovitch discusses his current work on the AC4 Sustainable Peace Project with project lead, Peter T. Coleman. Liebovitch is a Professor of Physics and Psychology at Queens College of the City University of New York and also currently an Adjunct Senior Research Scientist at AC4. The professors discuss their collaborative work over the past three years with a team of scholars and practitioners on trying to understand peaceful societies and their core dynamics. Professor Liebovitch shares insights about the mix of time-honored and contemporary tools they are using on this project to understand complex systems of peaceful societies and to create models and real world applications from their understanding so that policymakers will be able to think more carefully about what they're doing in their communities to create peace. "This has been fun for me to work with because it is applying things from the physical sciences to a situation that is a lot more difficult to understand." Read more about Larry Liebovitch and the AC4 Sustainable Peace Project: http://ac4.ei.columbia.edu/research-themes/dst/sustainable-peace/

Adam Schrum and Steven Neier describe a technique for identifying patient-specific protein complexes and how it revealed altered signaling in T cells from patients with the autoimmune disease alopecia areata.

Steven Wiley describes a quantitative study that shows that adaptor proteins, not core signaling components, control the flow of information through the EGF pathway.

Kevin Janes explains how a statistical modeling approach was used to discover how insulin signaling can suppress inflammatory signaling.

Ivan Bogeski explains how redox-insensitive ORAI calcium channels enable monocytes to sustain calcium signaling while still producing bactericidal reactive oxygen species.

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Auf den Vorschlag von Henning Krause verbreiteten viele Forschende unter dem Hashtag #1TweetForschung ihr Forschungsthema in Kurzform. So auch Lorenz Adlung, der in der Abteilung Systembiologie der Signaltransduktion am Deutschen Krebsforschungszentrum in Heidelberg die mathematische Modellbildung für biologische Prozesse erforscht. Bei der Anwendung einer Chemotherapie leiden Krebspatienten oft unter Blutarmut. Hier kann neben der Bluttransfusion das Hormon Erythropoetin, kurz EPO, helfen, da es die körpereigene Erzeugung von roten Blutkörperchen (Erythrozyten) unterstützt. Leider ist EPO als Dopingmittel bekannt, und um dem Doping noch deutlicher Einhalt zu gebieten, wurde im November 2014 in Deutschland ein Entwurf eines Anti-Doping-Gesetz vorgelegt. Trotz gängigem Einsatz und erprobter Wirkung von EPO ist die genaue Wirkung von EPO auf Krebszellen nicht bekannt. Daher verfolgt Lorenz Adlung den Ansatz der Systembiologie, um im Zusammenwirken von Modellbildung und Mathematik, Biologie und Simulationen sowohl qualitativ und quantitativ analysieren und bewerten zu können. Vereinfacht sind rote Blutkörperchen kleine Sauerstoff-transportierende Säckchen aus Hämoglobin, die auch die rote Farbe des Bluts verursachen. Sie stammen ursprünglich aus Stammzellen, aus denen sich im Differenzierungs-Prozess Vorläuferzellen bzw. Progenitorzellen bilden, die wiederum durch weitere Spezialisierung zu roten Blutkörperchen werden. Da es nur wenige Stammzellen gibt, aus denen eine unglaubliche große Anzahl von Trillionen von Blutkörperchen werden müssen, gibt es verschiedene Teilungs- bzw. Proliferationsprozesse. Das Ganze ergibt einen sehr komplexen Prozess, dessen Verständnis zu neuen Methoden zur Vermehrung von roten Blutkörperchen führen können. Den durch Differenzierung und Proliferation gekennzeichnete Prozess kann man mathematisch beschreiben. Eine zentrale Ansichtsweise in der Systembiologie der Signaltransduktion ist, Zellen als informationsverarbeitende Objekte zu verstehen, die zum Beispiel auf die Information einer höheren EPO-Konzentration in der Umgebung reagieren. Von diesem Ansatz werden durch Messungen Modelle und Parameter bestimmt, die das Verhalten angemessen beschreiben können. Diese Modelle werden in Einklang mit bekannten Prozessen auf molekularer Ebene gebracht, um mehr über die Abläufe zu lernen. Die erforderlichen quantitativen Messungen basieren sowohl auf manuellem Abzählen unter dem Mikroskop, als auch der Durchflusszytometrie, bei der durch Streuung von Laserlicht an Zellen durch Verwendung von Markern sogar Aussagen über die Zelloberflächen getroffen werden können. Zusätzlich kann mit der Massenspektrometrie auch das Innere von Zellen ausgemessen werden. In diesem Anwendungsfall werden die mathematischen Modelle in der Regel durch gekoppelte gewöhnliche Differenzialgleichungen beschrieben, die Zell- oder Proteinkonzentrationen über die Zeit beschreiben. Die Differenzialgleichungen und deren Parameter werden dabei sowohl mit Messungen kalibriert, als auch mit den Kenntnissen in der Molekularbiologie in Einklang gebracht. Die Anzahl der Parameter ist aber oft zu hoch, um naiv auf geeignete zu den Messungen passende Werte zu gelangen. Daher wird unter anderem das Latin Hypercube Sampling verwendet, um schnell nahe sinnvollen Parameterwerten zu gelangen, die durch gradienten-basierte Optimierungsverfahren verbessert werden können. Die Basis für diese Art von Optimierungsverfahren ist das Newton-Verfahren, mit dem man Nullstellen von Funktionen finden kann. Ein wichtiger Aspekt im Umgang mit Messergebnissen ist die Berücksichtigung von Messfehlern, die auch vom Wert der Messung abhängig verstanden werden muss- denn nahe der Messgenauigkeit oder der Sättigung können die relativen Fehler extrem groß werden. Die Bestimmung der Modellparameter ist schließlich auch ein Parameteridentifikationsproblem, wo insbesondere durch eine Sensitivitätsanalyse auch der Einfluss der geschätzten Parameter bestimmt werden kann. Sowohl die Parameter als auch die Sensitivitäten werden mit den biologischen Prozessen analysiert, ob die Ergebnisse stimmig sind, oder vielleicht auf neue Zusammenhänge gedeuten werden können. Hier ist die Hauptkomponentenanalyse ein wichtiges Werkzeug, um zentrale beeinflussende Faktoren erfassen zu können. Ein wichtiges Ziel der Modellbildung ist die numerische Simulation von Vorgängen, die als digitale Experimente sich zu einem eigenen Bereich der experimentellen Forschung entwickelt haben. Darüber hinaus ermöglicht das digitale Modell auch die optimale Planung von Experimenten, um bestimmte Fragestellungen möglichst gut untersuchen zu können. Die Umsetzung auf dem Computer erfolgt unter anderem mit Matlab, R (The R Project for Statistical Computing) und mit der spezialisierten und freien Software D2D - Data to Dynamics.Literatur und Zusatzinformationen M. Boehm, L. Adlung, M. Schilling, S. Roth, U. Klingmüller, W. Lehmann: Identification of Isoform-Specific Dynamics in Phosphorylation-Dependent STAT5 Dimerization by Quantitative Mass Spectrometry and Mathematical Modeling, Journal of Proteome Research, American Chemical Society, 2014. (PubMed) Studium der Systembiologie D2D-Software L. Adlung, C. Hopp, A. Köthe, N. Schnellbächer, O. Staufer: Tutorium Mathe für Biologen, Springer Spektrum, 2014. Science: NextGen Voices zur globalen wissenschaftlichen Zusammenarbeit- mit Lorenz Adlung Lorenz Adlung auf Twitter L. Adlung, et. al: Synbio meets Poetry, CreateSpace, 2013. Kollaborationspartner: U.a. Thomas Höfer, Heidelberg, Jens Timmer, Freiburg i. B., Fabian Theis, München Resonator-Podcast 015: DKFZ-Forscher Christof von Kalle Resonator-Podcast 014: Das DKFZ in Heidelberg Omega Tau-Podcast 069: Grundlagen der Zellbiologie Omega Tau-Podcast 072: Forschung in der Zellbiologie Konscience-Podcast 024, Kapitel 5: Das Hochlandgen aus "Wie kam das bloß durch die Ethikkommission?"

Scherzer, O (Universität Wien) Monday 10 February 2014, 14:15-15:00

Penelope Morel and James Faeder discuss their finding that the duration of T cell receptor signaling determines the fate of regulatory versus helper T cells.

Ben and Sam talk to Colin Wyers about how he got hired by the Astros, working in baseball, and where sabermetrics is headed.

Variability in receptor protein abundance affects the sensitivity of T cells to the cytokines IL-2 and IL-7.

Did you ever wish you could have a wizard at your fingertips, to help you make tough program and project decisions easier? IBM develops technical wizardry and makes it more accessible. One of the ways we do this is by encapsulating difficult problems into models, which can then be made available to those who need solutions in a more consumable way. Learn how IBM Rational helps make tough program and project management decisions easier. Murray Cantor and Brian Nolan, speakers.

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Partha Niyogi Memorial Conference: "Mathematical Modeling of Color Categorization in Humans". This conference is in honor of Partha Niyogi, the Louis Block Professor in Computer Science and Statistics at the University of Chicago. Partha lost his battle with cancer in October of 2010, at the age of 43. Partha made fundamental contributions to a variety of fields including language evolution, statistical inference, and speech recognition. The underlying themes of learning from observations and a rigorous basis for algorithms and models permeated his work.

If you experience any technical difficulties with this video or would like to make an accessibility-related request, please send a message to digicomm@uchicago.edu. Partha Niyogi Memorial Conference: "Mathematical Modeling of Color Categorization in Humans". This conference is in honor of Partha Niyogi, the Louis Block Professor in Computer Science and Statistics at the University of Chicago. Partha lost his battle with cancer in October of 2010, at the age of 43. Partha made fundamental contributions to a variety of fields including language evolution, statistical inference, and speech recognition. The underlying themes of learning from observations and a rigorous basis for algorithms and models permeated his work.

Mathematical analysis reveals how a graded signal can induce a homogeneous response across a field of cells.

Stevens, A (Heidelberg) Thursday 09 September 2010, 10:00-11:00

Lateral inhibition in proneuronal clusters in the fruit fly relies on competition between cis and trans Notch signaling.

Mathematical modeling of signaling pathways can be used to identify candidate targets for cancer therapies.