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SUMMARY:MICDE Seminar: Marta D`Elia\, Principal Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Marta D’Elia is a Principal Member of the Technical Staff at Sandia National Laboratories\, where she works since 2014. She’s currently part of the Data Science and Computing group at the California site. She obtained her master degree in Mathematical Engineering at Politecnico of Milano with Prof. Quarteroni and she obtained her Ph.D in Applied Mathematics at Emory University with Prof. Veneziani. There\, she worked on optimal control in CFD for cardiovascular applications. She was a postdoctoral fellow at Florida State University where she worked with Prof. Gunzburger on optimization and control for nonlocal and fractional models. She’s an associate editor of the SIAM Journal on Scientific Computing\, Advances in Continuous and Discrete Models\, Numerical Methods for PDEs\, and the Journal of Peridynamics and Nonlocal Models. Also\, she’s a co-founder of the One Nonlocal World project. Her interests include nonlocal modeling and simulation\, optimization and optimal control\, and scientific machine learning. \nScientific interests: \n\nModeling and Computational aspects of Nonlocal and Fractional equations\,\nScientific Machine Learning\,\nOptimization and Uncertainty Quantification.\n\nDATA-DRIVEN LEARNING OF NONLOCAL MODELS: BRIDGING SCALES WITH NONLOCALITY \nNonlocal models are characterized by integral operators that embed length scales in their definition. As such\, they are preferable to classical partial differential equation models in situations where the dynamics of a system is affected by the small scale behavior\, yet the small scales would require prohibitive computational cost to be treated explicitly. In this sense\, nonlocal models can be considered as coarse-grained\, homogenized models that\, without resolving the small scales\, are still able to accurately capture the system’s global behavior. However\, nonlocal models depend on “kernel functions” that are often hand tuned.\nWe propose to learn optimal kernel functions from high fidelity data by combining machine learning algorithms\, known physics\, and nonlocal theory. This combination guarantees that the resulting model is mathematically well-posed and physically consistent. Furthermore\, by learning the operator rather than a surrogate for the solution\, these models generalize well to settings that are different from the ones used during training. We apply this learning technique to find homogenized nonlocal models for subsurface solute transport solely on the basis of breakthrough curves.\nWe also apply the same kernel-learning technique to design new stable and resolution-independent deep neural networks\, referred to as Nonlocal Kernel Networks (NKN). Stability of NKNs is obtained by imposing constraints derived from the nonlocal vector calculus\, whereas deep training is performed by means of a shallow-to-deep initialization technique. We demonstrate the accuracy and stability of NKNs on PDE-learning and image-classification problems. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational modeling and machine learning are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Department of Mechanical Engineering. Dr. D`Elia will be hosted by Dr. Krishna Garikipati\, Professor of Mechanical Engineering\, and of Mathematics. \nThis is a virtual event and will be broadcasted online via Zoom. MICDE students and fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-marta-delia-phd-principal-member-of-the-technical-staff-at-sandia-national-laboratories-california/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/01/Marta-DElia.png
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DTSTART;TZID=America/Detroit:20220321T160000
DTEND;TZID=America/Detroit:20220321T170000
DTSTAMP:20260606T080510
CREATED:20210805T184953Z
LAST-MODIFIED:20230713T170841Z
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SUMMARY:MICDE / MIDAS Seminar: Yun S. Song\, PhD\, Professor of Computer Science and Statistics\, University of California\, Berkeley
DESCRIPTION:ZOOM LINK\nBio: Professor Yun S. Song is a professor of EECS and Statistics working in mathematical and computational biology. He received his BS degrees in mathematics and physics from MIT\, and a PhD in physics from Stanford University.  Prof. Song’s research centers around computational and mathematical biology. He is generally interested in developing computational tools and statistical methods to facilitate the research of the broad biomedical community\, while also getting deeply involved in data analysis and interpretation.  Prof. Song is also interested in machine learning\, combinatorial optimization\, algorithms\, and Monte Carlo methods. \nRecent honors and awards include NIH Pathway to Independence Award K99/R00 (2006)\, Alfred P. Sloan Research Fellowship (2008)\, Packard Fellowship for Science and Engineering (2008)\, NSF CAREER Award (2009)\, Jim and Donna Gray Faculty Award for Excellence in Undergraduate Teaching (2013)\, Miller Research Professorship (2014)\, Math+X Simons Chair (2015)\, and Chan Zuckerberg Biohub Investigator Award (2017). \n\n  \nTalk Title: Mathematical and machine learning models for predicting protein synthesis and function\n  \nAbstract: Proteins are the workhorses of the cell and are involved in all aspects of cellular processes.  In spite of notable technological advances in protein biology and genomics over the past decade\, it remains an important challenge to unravel how protein synthesis and function are affected by genetic mutations.  In this talk\, I will describe our recent progress in tackling this challenge by leveraging new theoretical results on interacting particle systems and recent advances in natural language processing. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Michigan Institute for Data Science (MIDAS). Dr. Song will be hosted by Dr. George Zhang\, Professor of Ecology and Evolutionary Biology. \nThis is a hybrid event and will be held in-person and broadcast online via Zoom. Note: You may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-midas-seminar-yun-s-song-phd-professor-of-computer-science-and-statistics-university-of-california-berkeley/
LOCATION:West Hall 340
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/08/Yun-S.-Song.png
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