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DTSTART;TZID=America/Detroit:20250711T120000
DTEND;TZID=America/Detroit:20250711T130000
DTSTAMP:20250708T153951Z
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SUMMARY:Frontiers in Scientific Machine Learning Seminar - Romit Maulik (Penn State University): SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
DESCRIPTION:Zoom link \nBio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics\, geophysical modeling\, nuclear fusion\, and beyond. He is an Early Career Awardee of the Army Research Office. \nSALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning\nAbstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However\, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation\, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions)\, a novel RL framework that models control actions as dynamic\, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system\, our approach enables interpretability through local stability analysis\, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation\, SALSA-RL provides a powerful tool for advancing the design\, analysis\, and theoretical understanding of RL systems.
URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-romit-maulik-penn-state-university/
LOCATION:2004 Lay Auto Lab
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/07/Romit-Maulik-PennState-University.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250620T120000
DTEND;TZID=America/Detroit:20250620T130000
DTSTAMP:20250620T145718Z
CREATED:20250619T132221Z
LAST-MODIFIED:20250620T145718Z
UID:10000823-1750420800-1750424400@micde.umich.edu
SUMMARY:Frontiers in Scientific Machine Learning Seminar - Pan Du (University of Notre Dame): Conditional neural field latent diffusion model for generating spatiotemporal turbulence
DESCRIPTION:Zoom link \nBio: Pan Du received his bachelor’s degree in Thermal Engineering from Tsinghua University and completed his master’s in Mechanical Engineering at Washington University in St. Louis. He is currently a Ph.D. candidate in Aerospace and Mechanical Engineering at the University of Notre Dame under the guidance of Prof. Jian-Xun Wang. Pan’s research spans multiple disciplines\, including scientific machine learning\, Bayesian inference\, uncertainty quantification\, geometric deep learning\, and computational fluid mechanics. \nConditional neural field latent diffusion model for generating spatiotemporal turbulence\nAbstract: Pan Du will present the CoNFiLD model\, a novel generative framework for simulating complex turbulent flows in 3D irregular domains. While traditional eddy-resolved simulations are accurate\, their high computational cost limits usability. CoNFiLD addresses this by integrating neural field encoding with latent diffusion\, enabling efficient\, probabilistic modeling of spatiotemporal dynamics. It supports a wide range of tasks—such as flow super-resolution\, sparse reconstruction\, and data restoration—via Bayesian conditional sampling\, all without retraining. Results across diverse turbulent scenarios highlight its potential for advancing data-driven turbulence modeling.
URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-pan-du-university-of-notre-dame/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/Pan-Du-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250606T120000
DTEND;TZID=America/Detroit:20250606T130000
DTSTAMP:20250604T181957Z
CREATED:20250602T103951Z
LAST-MODIFIED:20250604T181957Z
UID:10000822-1749211200-1749214800@micde.umich.edu
SUMMARY:Frontiers in Scientific Machine Learning Seminar - Ashwin Renganathan (Penn State): Sample-efficient and Principled Decision-making  with Expensive Stochastic Oracles
DESCRIPTION:Zoom link \nBio: Ashwin Renganathan is an assistant professor of aerospace engineering at Penn State and holds a joint appointment with the Penn State Institute of Computational and Data Sciences (ICDS). He directs the Computational complex engineered Systems Design Laboratory (CSDL) at Penn State. He is broadly interested in developing novel and scalable computational techniques for surrogate modeling\, uncertainty quantification\, and numerical optimization\, with a focus on aerospace applications. He earned his Ph.D. in aerospace engineering from Georgia Tech and previously completed a postdoctoral appointment in applied mathematics at the Argonne National Laboratory. \nSample-efficient and Principled Decision-making with Expensive Stochastic Oracles\nAbstract: Modern day engineering decision-making involves one or more computer simulation oracles of an engineered system which can be queried on-demand to learn the system response to control input. Querying simulation oracles\, also called “computer experiments”\, incur a non-trivial computational cost\, which increases with the level of fidelity in the underlying models. For instance\, a realistic computational aerodynamic simulation of an aircraft can cost several thousands of CPU hours to compute—anything more than a few dozens of such simulations is prohibitive. Therefore\, a central goal of engineering decision-making is to optimally design computer experiments\, to maximize the value of information extracted at minimal computational effort.\nIn this talk\, we will address problems anchored in\, what we coin\, the “decision-making triad” which includes: surrogate modeling\, uncertainty quantification (UQ)\, and numerical optimization/control. Specifically\, using variants of a probabilistic surrogate model and a Bayesian decision theoretic framework\, we will show that problems in the decision-making triad can be solved in a principled\, theoretically sound and\, yet (computational) cost-effective manner. We will show demonstrations on applications in computational aerodynamics.
URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-seminar-15-ashwin-renganathan/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/MICDE-Seminar-Series-Speaker-Portraits-4.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250523T120000
DTEND;TZID=America/Detroit:20250523T130000
DTSTAMP:20250522T231313Z
CREATED:20250522T231313Z
LAST-MODIFIED:20250522T231313Z
UID:10000821-1748001600-1748005200@micde.umich.edu
SUMMARY:FSML Lecture Series - Smita Krishnaswamy (Yale University): Dynamics Models of Cellular and Neuronal Interactions
DESCRIPTION:Zoom link \nBio: Smita Krishnaswamy is an Associate Professor in the departments of Computer Science (SEAS) and Genetics (YSM). She is part of the programs in Applied Mathematics\, Computational Biology & Bioinformatics and Interdisciplinary Neuroscience. She is also affiliated with the Yale Institute for the foundations of data science\, Wu-Tsai Institute\, Yale Cancer Center. Her lab works on fundamental deep learning and machine learning developments for representing and learning from big data. Her techniques incorporate mathematical priors from graph spectral theory\, manifold learning\, signal processing\, and topology into machine learning and deep learning frameworks\, in order to denoise and model the underlying systems faithfully for predictive insight. Currently her methods are being widely used for data denoising\, visualization\, generative modeling\, dynamics. modeling\, comparative analysis and domain transfer in datasets arising from stem cell biology\, cancer\, immunology and structural biology (among others). \nPrior to joining Yale\, she completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She obtained her Ph.D. from EECS department at University of Michigan where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan\, she spent 2 years at IBM’s TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic. Her work over the years has won several awards including the NSF CAREER Award\, Sloan Faculty Fellowship\, and Blavatnik fund for Innovation. \nDynamics Models of Cellular and Neuronal Interactions
URL:https://micde.umich.edu/event/fsml-lecture-series-smita-krishnaswamy/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,big data,College Of Engineering,data,FSML,Machine Learning,North Campus,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/05/MICDE-Seminar-Series-Speaker-Portraits-3.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250425T120000
DTEND;TZID=America/Detroit:20250425T130000
DTSTAMP:20250522T231416Z
CREATED:20250421T155355Z
LAST-MODIFIED:20250522T231416Z
UID:10000819-1745582400-1745586000@micde.umich.edu
SUMMARY:FSML Lecture Series - Julie Bessac (National Renewable Energy Laboratory): Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution
DESCRIPTION:Zoom link \nBio: Julie Bessac received her Ph.D. degree in 2014 in Applied Mathematics from the University of Rennes 1\, France. Between 2014 and 2023\, she was a post-doctoral appointee and a research scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. She joined National Renewable Energy Laboratory in 2023 as a computational statistician. She is an adjunct professor at the Department of Statistics at Virginia Tech. Her research focuses on statistical and machine learning methods for modeling\, forecasting and uncertainty quantification for diverse applications: geophysical processes and their applications to energy systems\, computer science and nuclear physics. \nSummary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution\nAbstract: In the first part of this talk\, we will discuss summary statistics of physics-based model outputs and their correction with observational data. Physics-based models capture broad-scale dynamics across various spatial and temporal scales\, they often face challenges such as modeling biases\, high computational costs\, along with large outputs that are challenging to manipulate. On the other hand\, observations capture localized variability but are typically sparse. This talk presents an innovative approach to address these challenges by utilizing summary statistics from physics-based model outputs and enhancing them with observational information via neural networks.\nIn the second part of the talk\, we will present neural networks with closed-form probabilistic loss that applied to super-resolution of surface wind speed. We will illustrate that the use of a closed-form probabilistic loss provides the neural network with a sampling capability and a spatial covariance for super-resolved wind fields.\nThese are joint work with Atlanta Chakraborty (NREL)\, Harrison Goldwyn (NREL)\, Daniel Getter (USC)\, Johann Rudi (Virginia Tech) and Mitchell Krock (University of Missouri).
URL:https://micde.umich.edu/event/fsml-lecture-13-julie-bessac/
LOCATION:GG Brown Laboratory – 2636
CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,big data,College Of Engineering,data,FSML,Machine Learning,North Campus,Statistics
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/04/MICDE-Seminar-Series-Speaker-Portraits-1.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241107T180000
DTEND;TZID=America/Detroit:20241107T190000
DTSTAMP:20241030T170447Z
CREATED:20241029T230120Z
LAST-MODIFIED:20241030T170447Z
UID:10000787-1731002400-1731006000@micde.umich.edu
SUMMARY:Taking the Next Step: Graduate Studies in Computation/AI for Science and Engineering at U-M
DESCRIPTION:PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master’s students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend. \nPlease register via the link: https://sessions.studentlife.umich.edu/p/track/12857 \nZoom option available after registering.
URL:https://micde.umich.edu/event/taking-the-next-step-2024/
LOCATION:GG Brown Laboratory – 2147
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Artificial Intelligence,Astronomy,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Science,Computational Social Science,computer science,computing,Earth And Environmental Sciences,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Epidemiology,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Michigan Engineering,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scientific Computing,Statistics,Talk,Undergraduate,Undergraduate Students,Virtual,Workshop
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Happening@UM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240419T110000
DTEND;TZID=America/Detroit:20240419T120000
DTSTAMP:20240412T140752Z
CREATED:20240412T011911Z
LAST-MODIFIED:20240412T140752Z
UID:10000680-1713524400-1713528000@micde.umich.edu
SUMMARY:MICDE Fellowships Information Session
DESCRIPTION:Applications for the $4\,500 2024-2025 MICDE Graduate Fellowships will open May 1\, 2024. Please join this Zoom session to learn more about the fellowships and how to submit a great application! \nThese fellowships are sponsored by the Michigan Institute for Computational Discovery & Engineering. For more information\, see https://live-umor-micde.pantheonsite.io/academic-programs/fellowships/.
URL:https://micde.umich.edu/event/livestream-virtualmicde-fellowships-information-session/
LOCATION:Zoom Event
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Astronomy,big data,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Modeling,Computational Science,Computational Social Science,computer science,Data Science,Earth And Environmental Sciences,Ecology,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Fellowship,Free,Funding,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Life Science,Lsaresearch,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Natural Language Processing,Natural Sciences,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,parallel computing,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scholarship,Science,Scientific Computing,Sciml,Sociology,Statistics,Virtual,Webcast,Workshops
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/04/Fellowships-2024-info-session.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20190219T100000
DTEND;TZID=America/Detroit:20190219T163000
DTSTAMP:20230905T171358Z
CREATED:20230905T171358Z
LAST-MODIFIED:20230905T171358Z
UID:10000175-1550570400-1550593800@micde.umich.edu
SUMMARY:The 2nd Annual Data for Public Good Symposium
DESCRIPTION:Do you have experience in working alongside community partners in data analysis or program evaluation? Do you want to connect with others who are using their skills for public good? National efforts from organizations such as DataKind\, Data Science for Social Good\, and Statistics without Borders have been expanding in recent years as more individuals recognize their potential to impact social change.  Great things can happen when individuals are empowered to dedicate time\, resources\, and knowledge to the pursuit of public good. Whether we work in the foreground or the background\, we can all contribute to improving the lives of those around us. \nStatistics in the Community (STATCOM)\, in collaboration with the Center for Education Design\, Evaluation\, and Research (CEDER) and the Community Technical Assistance Collaborative (CTAC)\, invite you to attend the 2nd Annual Data for Public Good Symposium hosted by the Michigan Institute for Data Science (MIDAS). The symposium will take place on Tuesday\, February 19\, 2019 and will showcase the many research efforts and community-based partnerships at U-M that focus on improving humanity by using data for public good. If you are interested in attending\, please register here. \nSchedule:\n10:00 – 10:30: Registration and Networking\n10:30 – 11:30: Presentations \n\nPartners for Preschool: The Added Value of Learning Activities at Home During the Preschool Year\, Amanda Ketner\, School of Education\nUniversity-Community Partnership to Support Ambitious STEM Teaching: Leveraging University of Michigan expertise in education\, research\, and evaluation to support innovative\, interactive teaching across the S.E. Michigan region and beyond\, C. S. Hearn\, Center for Education Design\, Evaluation\, and Research (CEDER)\nOpen Data Flint\, Stage II\, Kaneesha Wallace\, MICHR\nResearch-Practice Partnerships at the Youth Policy Lab\, A Foster\, ISR Youth Policy Lab and School of Education\nThe LOOP Estimator: Adjusting for Covariates in Randomized Experiments\, Edward Wu\, Statistics\n\n11:30 – 01:00: Lunch/Poster Session\n01:00 – 02:00: Presentations \n\nBarrier Busters: Unconditional Cash Transfers as a Strategy to Promote Economic Self-Sufficiency\, Elise Gahan\, School of Public Health\nImplementing Trauma-Informed Care at University Libraries\, Monte-Angel Richardson\, School of Social Work\nWhy did the global crude oil price start to rise again after 2016?\, Shin Heuk Kang\, Economics\nPoverty and economic hardship in Michigan communities: Data from the Michigan Public Policy Survey (MPPS)\, Natalie Fitzpatrick\, Center for Local\, State\, and Urban Policy\nUnderstanding Networks of Influence on U.S. Congressional Members’ Public Personae on Twitter\, Angela Schopke\, Chris Bredernitz\, Caroline Hodge\, School of Information\n\n02:00 – 02:30: UM Student Organization Presentations\n02:30 – 04:30: Workshop Debrief & Closing \n\nAbout the Organizers: STATCOM is a community outreach organization offering the expertise of statistics graduate students – free of charge – to nonprofit governmental and community organizations. CTAC is a community-university partnership convened to serve a universal need identified by community partners around data and evaluation. CEDER is a School of Education center devoted exclusively to offering high-quality designs\, evaluations\, and research on teaching\, learning\, leadership\, and policy at multiple levels of education. This symposium is part of our effort to bring together university organizations that promote similar ideals and individuals whose research provides a service for the greater good. \nQuestions: Please contact salernos@umich.edu. \n  \n    \n  \n  \n  \n 
URL:https://micde.umich.edu/event/2nd-annual-data-for-public-good-symposium/
LOCATION:Forum Hall\, Palmer Commons
CATEGORIES:Conference,Statistics,Workshops
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