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DTSTART;TZID=America/Detroit:20181203T160000
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SUMMARY:MICDE Seminar: Youssef Marzouk\, Department of Aeronautics and Astronautics\, MIT
DESCRIPTION:Bio: Youssef Marzouk is an associate professor in the Department of Aeronautics and Astronautics at MIT\, and co-director of the MIT Center for Computational Engineering. He is also director of MIT’s Aerospace Computational Design Laboratory. \nHis research interests lie at the intersection of physical modeling with statistical inference and computation. In particular\, he develops methodologies for uncertainty quantification\, inverse problems\, large-scale Bayesian computation\, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering\, environmental\, and geophysics applications. \nHe received his SB\, SM\, and PhD degrees from MIT and spent several years at Sandia National Laboratories before joining the MIT faculty in 2009. He is a recipient of the Hertz Foundation Doctoral Thesis Prize (2004)\, the Sandia Laboratories Truman Fellowship (2004-2007)\, the US Department of Energy Early Career Research Award (2010)\, and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering (2012). He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing\, Advances in Computational Mathematics\, and the SIAM/ASA Journal on Uncertainty Quantification. He is also an avid coffee drinker and classical pianist. \nA TOUR OF TRANSPORT METHODS FOR BAYESIAN COMPUTATION\nBayesian inference provides a natural framework for quantifying uncertainty in parameter estimates and model predictions\, and for combining heterogeneous sources of information. Characterizing the results of Bayesian inference—by simulating from the posterior distribution—often proceeds via Markov chain Monte Carlo or sequential Monte Carlo sampling\, but remains computationally challenging for complex posteriors and large-scale models. \nThis talk will describe a broad framework for using measure transport in Bayesian computation. This framework seeks deterministic couplings of the posterior measure with a tractable “reference” measure (e.g.\, a standard Gaussian). Such couplings are induced by transport maps\, and enable direct simulation from the desired measure simply by evaluating the transport map at samples from the reference. Approximate transports can also be used to “precondition” and accelerate standard Monte Carlo schemes. Within this framework\, one can describe many useful notions of low-dimensional structure associated with inference: for instance\, sparse or decomposable transports underpin modeling and computation with non-Gaussian Markov random fields\, and low-rank transports arise frequently in inverse problems. \nWe will then describe recent work specializing transport maps to the problem of nonlinear filtering in high-dimensional state-space models. The idea is to transform a forecast ensemble into samples from the current filtering distribution via a sequence of nonlinear transport maps\, computed via convex optimization. Construction of the maps is regularized by leveraging potential structure in the filtering problem—e.g.\, decay of correlations\, approximate conditional independence\, and local likelihoods—thus extending notions of localization to nonlinear updates. The proposed framework can be understood as a non-Gaussian generalization of the ensemble Kalman filter. \nThis is joint work with Alessio Spantini\, Daniele Bigoni\, Ricardo Baptista\, and Matthew Parno. \nProf. Marzouk is being hosted by Prof. Duraisamy (Aerospace). If you would like to meet him during his visit please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Marzouk for lunch please RVSP here by Friday\, November 30.
URL:https://micde.umich.edu/event/micde-seminar-youssef-marzouk-department-of-aeronautics-and-astronautics-mit/
LOCATION:107 Gorguze Family Laboratory\, 2609 Draper Dr\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20181212T160000
DTEND;TZID=America/Detroit:20181212T170000
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CREATED:20230905T171422Z
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SUMMARY:MICDE Seminar: Aaron Frank\, Chemistry and Biophysics\, University of Michigan
DESCRIPTION:Bio: Aaron Frank is originally from Grenada\, a small island in the Caribbean. After moving to the US in 2001\, Aaron received his BA in chemistry from Brooklyn College in 2006\, where he carried out research in the groups of Professors Charlene Forest\, Shaneen Singh\, and Alexander Greer. He then moved to Michigan to attend graduate school at the University of Michigan and then\, with his Ph.D advisor Professor Ioan Andricioaei\, moved to UC Irvine in 2008. Aaron received his Ph.D in chemistry in 2011. Following a 2 year stint at Nymirum Inc. — a small biotech company in Ann Arbor founded by a close collaborator\, Professor Hashimi Al-Hashimi — he returned to the University of Michigan as a Presidential Postdoctoral Fellow where he was mentored by Professor Charles L. Brooks\, III. Aaron is now an Assistant Professor at the University of Michigan in the Chemistry Department and the Biophysics Department. \nDATA SCIENCE AT THE INTERFACE OF BIOLOGY\, CHEMISTRY\, AND PHYSICS\nIn this talk\, I will describe examples of how my research group uses data science tools to tackle research problems that fall at the interface between Biology\, Chemistry\, and Physics. First\, I will describe ongoing research focused on mapping the structure-landscape of functional ribonucleic acids (or RNAs). In this project\, we combined machine learning and secondary structure modeling tools to predict the structure of RNAs conditioned on available NMR chemical shift data. This method now enables us to model individual conformational states\, including previously invisible states of an RNA\, based on its sequence and available chemical shift data. Second\, I will describe ongoing research centered around decoding structure-kinetic relationships (SKRs) in sparse datasets. There is now immense interest in developing drugs that exhibit elevated residence times on their target. In this project\, we used machine learning to encapsulate SKRs for CDK2\, a prominent cancer target\, from a dataset containing only fourteen (14) samples. I will describe our efforts to build and test CDK2-specific SKR models that take as input\, the atomic structure of receptor-ligand complexes and output estimates of their residence times. Additionally\, I will describe proof-of-concept studies that demonstrate the utility of our CDK2-specific SKR models as tools to help efficiently explore chemical space in search of novel chemical scaffolds that are enriched with high-residence time and potent inhibitors of CDK2.
URL:https://micde.umich.edu/event/micde-seminar-aaron-frank-chemistry-and-biophysics-university-of-michigan/
LOCATION:1210 Chemistry & Willard H Dow Laboratory\, 930 University Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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