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X-WR-CALNAME:Michigan Institute for Computational Discovery and Engineering
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X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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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:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20250311T164812Z
CREATED:20250311T164812Z
LAST-MODIFIED:20250311T164812Z
UID:10000815-1741953600-1741957200@micde.umich.edu
SUMMARY:Workshop / Seminar:Frontiers in Scientific Machine Learning (FSML) Seminar: Alexander Tong (Post-doctoral Fellow\, Mila - Quebec AI Institute)
DESCRIPTION:Abstract:\nGenerative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery.\nBio:\nAlexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins.
URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-fsml-seminar-alexander-tong-post-doctoral-fellow-mila-quebec-ai-institute/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,Micde
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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
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DTSTART;TZID=America/Detroit:20241022T132000
DTEND;TZID=America/Detroit:20241022T170000
DTSTAMP:20241018T223652Z
CREATED:20241018T223652Z
LAST-MODIFIED:20241018T223652Z
UID:10000786-1729603200-1729616400@micde.umich.edu
SUMMARY:Conference / Symposium:MICDE ACES Mini-Symposium 2024
DESCRIPTION:This year’s focus of the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium is connecting advanced algorithms\, artificial intelligence (AI)\, and high-performance computing (HPC) architectures to advance scientific discovery. The event showcases the work of the University of Michigan faculty members at the intersection of AI\, HPC\, and advanced algorithms. It also includes a panel discussion on the current trends in AI\, novel algorithms\, and computer architectures for efficient scientific applications.\nACES is an event that promotes state-of-the-art research at the University of Michigan and the current trends and hot topics in computational science and engineering. Furthermore\, it is the nucleus for increasing collaboration at the University of Michigan by offering an opportunity for faculty members to share their ideas and network during a reception. Take advantage of this exciting opportunity to connect\, learn\, and be inspired!
URL:https://micde.umich.edu/event/conference-symposiummicde-aces-mini-symposium-2024/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor
CATEGORIES:Aces,Ai In Science And Engineering,Artificial Intelligence,Computation,Computational Science,Engineering,Free,High Performance Computing,In Person,Interdisciplinary,Micde,Michigan Engineering,parallel computing,Research,Science,Scientific Computing,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231114T080000
DTEND;TZID=America/Detroit:20231114T190000
DTSTAMP:20231020T163041Z
CREATED:20231020T163041Z
LAST-MODIFIED:20231020T163041Z
UID:10000660-1699948800-1699988400@micde.umich.edu
SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023
DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase
URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023-2/
LOCATION:Rackham Graduate School (Horace H.)
CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231113T080000
DTEND;TZID=America/Detroit:20231113T190000
DTSTAMP:20231020T163040Z
CREATED:20231020T163040Z
LAST-MODIFIED:20231020T163040Z
UID:10000659-1699862400-1699902000@micde.umich.edu
SUMMARY:Conference / Symposium:U-M Data Science & AI Summit 2023
DESCRIPTION:The U-M Data Science and AI Summit is the largest annual data science and AI event on campus. This event brings together the U-M data science and AI research community and their external collaborators to build research vision and collaboration. It also showcases the breadth and depth of U-M data science and AI research\, from theory and methodology development to the transformative use of data and AI to address scientific and societal challenges in all domains. The event is free for all attendees (U-M faculty\, staff\, and trainees\, as well as industry\, government and community members).\nTo view full Summit schedule\, please visit the event webpage at https://midas.umich.edu/midas-summit-2023/.\nKeynotes:\nSuresh Venkatasubramanian\, Director\, Center for Technological Responsibility\, Reimagination\, and Redesign\, Data Science Institute at Brown University; Professor of Data Science and Computer Science\, Brown University – Key player for the White House Blueprint of an AI Bill of Rights\nJulianne Dalcanton\, Director\, Center for Computational Astrophysics\, Flatiron Institute – The origina and evolution of galaxies\nEmre Kiciman\, Senior Principal Researcher\, Microsoft Research – A New Frontier at the Intersection of Causality and LLMs\nSummit Sessions:\nA panel discussion on: Federal priorities and opportunities in data science and AI\nPanelists:\n– Laura Biven\, Data Science Technical Lead\, Office of Data Science Strategy\, National Institutes of Health\n– Michael Molnar\, Director\, Advanced Manufacturing National Program Office\, National Institute of Standards and Technology\n– Hector Muñoz-Avila\, Program Director and Cluster Lead\, the Information Integration and Informatics Program\, National Science Foundation\n– Alvaro Velasquez\, Program Manager\, Information Innovation Office\, Defense Advanced Research Projects Agency\nResearch vision talks by University of Michigan faculty researchers\nThe Propelling Original Data Science grant awardees showcase\nPoster session\, lightning talks\, and awards\nUniversity of Michigan data science and AI organizations showcase
URL:https://micde.umich.edu/event/conference-symposiumu-m-data-science-ai-summit-2023/
LOCATION:Rackham Graduate School (Horace H.)
CATEGORIES:Ai In Science And Engineering,Applications,Artificial Intelligence,big data,Biostatistics,Climate and Space Sciences and Engineering,Computational Modeling,Computational Science,Computational Social Science,computing,Data Curation,Data Science,data visualization,Deep Learning,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Faculty,Free,Graduate,Graduate Students,Industrial and Operations Engineering,Information and Technology,Interdisciplinary,Lecture,Machine Learning,Michigan Engineering,Midas,Natural Language Processing,Networking,Science,Social Impact,symposium
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