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DTSTART;TZID=America/New_York:20220505T153600
DTEND;TZID=America/New_York:20220505T173600
DTSTAMP:20240301T102954Z
CREATED:20240214T084023Z
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UID:10002592-1651764960-1651772160@cmsa.fas.harvard.edu
SUMMARY:Qianfang: a type-safe and data-driven healthcare system starting from Traditional Chinese Medicine
DESCRIPTION:Abstract: Although everyone talks about AI + healthcare\, many people were unaware of the fact that there are two possible outcomes of the collaboration\, due to the inherent dissimilarity between the two giant subjects. The first possibility is healthcare-leads\, and AI is for building new tools to make steps in healthcare easier\, better\, more effective or more accurate. The other possibility is AI-leads\, and therefore the protocols of healthcare can be redesigned or redefined to make sure that the whole infrastructure and pipelines are ideal for running AI algorithms. \nOur system Qianfang belongs to the second category. We have designed a new kind of clinic for the doctors and patients\, so that it will be able to collect high quality data for AI algorithms. Interestingly\, the clinic is based on Traditional Chinese Medicine (TCM) instead of modern medicine\, because we believe that TCM is more suitable for AI algorithms as the starting point. \nIn this talk\, I will elaborate on how we convert TCM knowledge into a modern type-safe large-scale system\, the mini-language that we have designed for the doctors and patients\, the interpretability of AI decisions\, and our feedback loop for collecting data. \nOur project is still on-going\, not finished yet.Bio: Yang Yuan is now an assistant professor at IIIS\, Tsinghua. He finished his undergraduate study at Peking University in 2012. Afterwards\, he received his PhD at Cornell University in 2018\, advised by Professor Robert Kleinberg. During his PhD\, he was a visiting student at MIT/Microsoft New England (2014-2015) and Princeton University (2016 Fall). Before joining Tsinghua\, he spent one year at MIT Institute for Foundations of Data Science (MIFODS) as a postdoc researcher. He now works on AI+Healthcare\, AI Interpretability and AI system.
URL:https://cmsa.fas.harvard.edu/event/5-5-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.05.2022-1583x2048-1.png
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DTSTART;TZID=America/New_York:20220512T153800
DTEND;TZID=America/New_York:20220512T173800
DTSTAMP:20240301T102818Z
CREATED:20240214T084325Z
LAST-MODIFIED:20240301T102818Z
UID:10002594-1652369880-1652377080@cmsa.fas.harvard.edu
SUMMARY:Geometric Models for Sets of Probability Measures
DESCRIPTION:Abstract: Many statistical and computational tasks boil down to comparing probability measures expressed as density functions\, clouds of data points\, or generative models.  In this setting\, we often are unable to match individual data points but rather need to deduce relationships between entire weighted and unweighted point sets. In this talk\, I will summarize our team’s recent efforts to apply geometric techniques to problems in this space\, using tools from optimal transport and spectral geometry. Motivated by applications in dataset comparison\, time series analysis\, and robust learning\, our work reveals how to apply geometric reasoning to data expressed as probability measures without sacrificing computational efficiency.
URL:https://cmsa.fas.harvard.edu/event/5-12-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.12.22-1583x2048-1.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220519T090000
DTEND;TZID=America/New_York:20220519T100000
DTSTAMP:20240301T102658Z
CREATED:20240214T084730Z
LAST-MODIFIED:20240301T102658Z
UID:10002598-1652950800-1652954400@cmsa.fas.harvard.edu
SUMMARY:The geometry of conditional independence models with hidden variables
DESCRIPTION:Abstract: Conditional independence (CI) is an important tool instatistical modeling\, as\, for example\, it gives a statistical interpretation to graphical models. In general\, given a list of dependencies among random variables\, it is difficult to say which constraints are implied by them. Moreover\, it is important to know what constraints on the random variables are caused by hidden variables. On the other hand\, such constraints are corresponding to some determinantal conditions on the tensor of joint probabilities of the observed random variables. Hence\, the inference question in statistics relates to understanding the algebraic and geometric properties of determinantal varieties such as their irreducible decompositions or determining their defining equations. I will explain some recent progress that arises by uncovering the link to point configurations in matroid theory and incidence geometry. This connection\, in particular\, leads to effective computational approaches for (1) giving a decomposition for each CI variety; (2) identifying each component in the decomposition as a matroid variety; (3) determining whether the variety has a real point or equivalently there is a statistical model satisfying a given collection of dependencies. The talk is based on joint works with Oliver Clarke\, Kevin Grace\, and Harshit Motwani. \nThe papers are available on arxiv: https://arxiv.org/pdf/2011.02450\nand https://arxiv.org/pdf/2103.16550.pdf
URL:https://cmsa.fas.harvard.edu/event/5-19-2022-cmsa-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.19.22-1583x2048-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220526T090000
DTEND;TZID=America/New_York:20220526T100000
DTSTAMP:20240301T102538Z
CREATED:20240214T085539Z
LAST-MODIFIED:20240301T102538Z
UID:10002601-1653555600-1653559200@cmsa.fas.harvard.edu
SUMMARY:Extinction and coexistence for reaction-diffusion systems on metric graphs
DESCRIPTION:Abstract: In spatial population genetics\, it is important to understand the probability of extinction in multi-species interactions such as growing bacterial colonies\, cancer tumor evolution and human migration. This is because extinction probabilities are instrumental in determining the probability of coexistence and the genealogies of populations. A key challenge is the complication due to spatial effect and different sources of stochasticity. In this talk\, I will discuss about methods to compute the probability of extinction and other long-time behaviors for stochastic reaction-diffusion equations on metric graphs that flexibly parametrizes the underlying space. Based on recent joint work with Adrian Gonzalez-Casanova and Yifan (Johnny) Yang.
URL:https://cmsa.fas.harvard.edu/event/5-26-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-05.26.2022-1583x2048-1.jpg
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