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DTSTART;TZID=America/Detroit:20171201T083000
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DTSTAMP:20230905T171416Z
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SUMMARY:2017 U-M Data Science Research Forum
DESCRIPTION:Forum Highlights \n\nOral and poster presentations on\n\nTheoretical foundations of data science\nData science methodology\nData science applications in any research domain\nSocial impact of data science research\n\n\n\n\nHow to engage industry workshop\n\nAdrian Fortino\, Partner (Mercury Fund)\nMike Psarouthakis\, Director (U-M Venture Center)\nKevyn Collins-Thompson\, Associate Professor\, U-M School of Information\nMike Cafarella\, Associate Professor\, U-M Computer Science Engineering\n\n\n\n\nKeynote by Chris Rozell\n\n\nNetworking Reception\n\nAll presentations will come from submissions in response to our call for abstracts\n Abstract Submission Deadline: October 23\, 2017\n We welcome submission from all U-M data science researchers (faculty\, staff\, trainees) \nPlease register for this event.  Please also see the call for abstracts for instruction\, and submit through the Abstract Submission Form. \nPreliminary Schedule
URL:https://micde.umich.edu/event/data-science-research-forum/
LOCATION:Michigan League\, 911 N. University\, Ann Arbor\, MI\, 48104\, United States
CATEGORIES:Conference,Data Science,Seminar
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DTSTART;TZID=America/Detroit:20171204T100000
DTEND;TZID=America/Detroit:20171204T110000
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SUMMARY:ME Faculty Candidate Seminar Series: Maziar Raissi\, Brown University
DESCRIPTION:Bio: Maziar Raissi is an Assistant Professor of Applied Mathematics (research) in the Division of Applied Mathematics at Brown University. He received his Ph.D. in Applied Mathematics & Statistics\, and Scientific Computations from University of Maryland — College Park in December 2016. Raissi’s expertise lies at the intersection of Probabilistic Machine Leaning\, Deep Learning\, and Data Drive Scientific Computing. \nHidden Physics Models: Machine Learning of Non-linear Partial Differential Equations\n\nA grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws\, physical principles\, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering\, science\, and technology. At the intersection of probabilistic machine learning\, deep learning\, and scientific computations\, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision\, this work is exploring two complimentary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics\, expressed by time dependent and non-linear differential equations\, to extract patterns from high-dimensional data generated from experiments\, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data\, infer latent quantities of interest (e.g.\, the solution to a differential equation)\, and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics. \n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-maziar-raissi-brown-university/
LOCATION:MI
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/MaziarRaissi.jpg
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DTSTART;TZID=America/Detroit:20171206T120000
DTEND;TZID=America/Detroit:20171206T130000
DTSTAMP:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series: Xun (Ryan) Huan\, Combustion Research Facility at Sandia National Laboratories
DESCRIPTION:Bio: Xun (Ryan) Huan is a postdoctoral researcher in the Combustion Research Facility at Sandia National Laboratories. He received a Ph.D. in Computational Science and Engineering from MIT Department of Aeronautics and Astronautics. He also has a master’s degree from MIT and a bachelor’s degree from the University of Toronto\, both in Aerospace Engineering. Xun’s research interests broadly revolve around uncertainty quantification\, decision-making under uncertainty\, data-driven modeling\, and optimization for engineering applications. Outside work\, he is an ice hockey player and a pilot. \nFinding the Most Informative Data Using Model-based Optimal Experimental Design\nExperimental data play a crucial role in developing and refining models of physical systems. However\, some experiments produce more useful data than others\, and well-chosen experiments can provide substantial resource savings. Optimal experimental design (OED) thus seeks to systematically quantify and maximize the value of experiments. We introduce general mathematical frameworks and algorithmic approaches for OED with nonlinear models. The formalism employs Bayesian statistics and an information-theoretic objective\, and rigorously defines the conditions under which batch experiments (experiments chosen simultaneously) and sequential experiments (forward-looking designs with data feedback) are truly optimal. Finding these optimal designs using conventional means is generally intractable. We develop practical numerical methods for OED by advancing computational techniques on several fronts\, including stochastic optimization\, polynomial chaos surrogate modeling\, approximate dynamic programming\, and transport maps. Using the overall algorithm\, we design combustion experiments for optimal learning of Arrhenius kinetic parameters\, and sequential sensor placement for contaminant source inversion.\n\n* Lunch won’t be provided but you are welcome to bring your own\n\n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xun-ryan-huan/
LOCATION:MI
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/XunRyanHuan-e1583777526832.jpg
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