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DTSTART;TZID=America/Detroit:20250515T090000
DTEND;TZID=America/Detroit:20250515T103000
DTSTAMP:20260621T004914
CREATED:20250501T205802Z
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SUMMARY:Bioinformatics PhD Dissertation Defense - Yueyang Shen: Complex Time Representation and Observability of Repeated Measurement  Processes with Applications to Spacekime Analytics
DESCRIPTION:Zoom link \nBio: Yueyang Shen is a PhD student in bioinformatics at the University of Michigan. His current research interests include spacetime analytics\, geometric deep learning\, applied neuroimaging studies\, and physics-inspired ML. I am broadly interested in mathematical\, statistical\, and physical modeling and its biological applications. Some of my past projects involve spatial analytics and studying symmetry effects on neural networks. I am currently working on decoding the music pathway in the brain using machine learning. \nComplex Time Representation and Observability of Repeated Measurement Processes with Applications to Spacekime Analytics\nAbstract: \nThis work develops and validates mathematical\, computational\, statistical\, and algorithmic techniques to represent observable processes as computable data objects\, which are amenable to subsequent modeling\, scientific inference\, AI prediction\, classification\, forecasting\, and spacekime analytics. Chapter 1 provides study motivation\, an overview of current knowledge\, and lays the foundation of complex-time (kime) representation of repeated measurement processes. \nThe core of this dissertation is organized in four integrated chapters with an overarching theme of observable process representation\, computational modeling\, scientific inference\, AI prediction\, classification\, and statistical forecasting using high-dimensional spatiotemporal data and (spacekime) analytics. In Chapter 2 we introduce non-local constraints to solve ultrahyperbolic equations. In Chapter 3\, we address a particular numerical strategy to convert repeated timeseries observations into richer mathematical objects\, kime-surfaces\, that can be used for novel statistical learning\, computational inference\, and artificial intelligence predictions. We show examples using neuroscience data to examine regional brain activation via tensor linear regression on kime-surfaces. We also develop a framework to analyze time-varying distribution modeling on differential equations using reproducing kernel Hilbert spaces (RKHS). \nIn Chapter 4\, we develop a theoretical statistical foundation for building robust and generalizable neural networks (NN). Specifically\, we use a string theory dataset to benchmark different NN architectures and discuss their group invariance. In Chapter 5\, we develop a brain tumor segmentation method with attention and fractal encoding NN architecture. We also study spatiotemporal analytics using an fMRI music genre dataset. The final\, Chapter 6 synthesizes the content of the whole dissertation\, draws overall conclusions\, and sets directions for future work.
URL:https://micde.umich.edu/event/bioinformatics-phd-defense-shen/
LOCATION:2903 Taubman Health Sciences Library\, 1135 CATHERINE ST\, Ann Arbor\, MI\, 48109
CATEGORIES:Biosciences,Computational Medicine,Graduate School,Graduate Students,Micde,Science
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250425T120000
DTEND;TZID=America/Detroit:20250425T130000
DTSTAMP:20260621T004914
CREATED:20250421T155355Z
LAST-MODIFIED:20250522T231416Z
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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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250422T120000
DTEND;TZID=America/Detroit:20250422T130000
DTSTAMP:20260621T004914
CREATED:20250114T140812Z
LAST-MODIFIED:20260522T152527Z
UID:10000798-1745323200-1745326800@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nApplications of the phase-field method to polycrystalline materials\nPhase-field modeling is a common diffuse interface method for simulating microstructure evolution due to its ability to capture complex morphologies without the need for explicitly tracking phase interfaces. A typical application of the phase-field method is polycrystalline grain growth during annealing\, where grain boundaries migrate toward their centers of curvature. Recent studies have shown abnormally large grains can be grown in shape memory alloys during cyclic annealing due to additional driving forces generated during the growth and dissolution of second-phase precipitates. In this work\, we model grain growth via a phase-field model that considers stored energy generated during the cyclic heat treatments. Applications of the phase-field method to experimentally acquired grain microstructures will also be discussed. \nZach Croft\, Applied Physics\nZach is a PhD student in the Applied Physics program. He works in the field of computational materials science with an emphasis on phase-field modeling of polycrystalline evolution and solidification of alloys under Professor Katsuyo Thornton. \n\nUsing causal inference to estimate counterfactual disparity measures for access to weight management treatments\nType 2 Diabetes (T2D) is a prevalent condition with significant variation in outcomes based on race and ethnicity\, underscoring the need for more improved prevention practices. Because effective weight management is a key component of T2D prevention\, increasing access to evidence-based treatments for those most at-risk for developing T2D is imperative. Yet\, existing population health management approaches do not typically measure disparities in access to treatments or do so in ways that do not account for the increased risk experienced by certain patient populations. This talk will (1) describe how causal inference was used to calculate counterfactual estimates of disparities in referral to weight management treatments among a population of adults with obesity\, (2) compare counterfactual estimates generating from the standard approach vs. a risk-based approach \, and (3) share UM research\, computing\, and other resources that supports this research. \nCassie Turner\, Health Infrastructures and Learning Systems\nCassie has a joint appointment at Michigan Medicine and the Ann Arbor Veteran Affairs Health System\, where she contributes to health research and practice focused on improving metabolic health through leveraging analytics\, novel care models\, and learning health systems approaches.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-22-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250415T120000
DTEND;TZID=America/Detroit:20250415T133000
DTSTAMP:20260621T004914
CREATED:20250114T141442Z
LAST-MODIFIED:20260522T152446Z
UID:10000797-1744718400-1744723800@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nTemporal relationship between acute noise exposure and heart rate variability change\nExcessive noise in daily activities and during sleep is disturbing and causes annoyance and stress over time. Noise\, among numerous environmental pollutants\, also independently contributes to the risk of cardiovascular diseases potentially through stress responses. Heart rate variability (HRV) change\, which reflects the neurohormonal and automatic neural responses to stress\, has been evaluted as an outcome to air pollution (PM 2.5\, ozone)\, smoking\, and other exposures. This analysis explored feasibility of using time series analysis to examine the noise and HRV association in a large longitudinal cohort. Alternative modeling approaches were also explored to accommodate the complex structure of this time series data. \nXin Zhang\, EHS and Scientific Computing\nXin Zhang is a 3rd year PhD candidate in the Department of Environmental Health Sciences at the University of Michigan. Her research focuses on evaluating the effects of environmental noise exposure on auditory and cardiovascular health outcomes using integrated data from personal devices with wearable sensors. \n\nEngineering The Immune Response To Improve Muscle Regeneration\nJesus Castor\, Biomedical Engineering\n 
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-15-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250409T190000
DTEND;TZID=America/Detroit:20250409T200000
DTSTAMP:20260621T004914
CREATED:20250407T165916Z
LAST-MODIFIED:20250407T165916Z
UID:10000818-1744225200-1744228800@micde.umich.edu
SUMMARY:The Orren C. Mohler Prize Lecture
DESCRIPTION:Volker Springel\, Professor\, Max Planck Institute for Astrophysics & Ludwig Maximilian University of Munich\, Germany \n“Simulated Universes: Origin and Fate of Our Milky Way”\nGalaxies contain hundreds of billions of stars and display a wide variety of shapes and sizes. But these cosmic lighthouses are just markers for even much vaster structures lying underneath. In fact\, astrophysicists are convinced that the vast majority of the energy and matter content of the Universe does not consist of ordinary matter\, but is dominated by enigmatic dark matter and dark energy components. Supercomputer simulations play a crucial role in testing this seemingly daring cosmological hypothesis. The astonishing performance of today’s supercomputers makes it possible to link the relatively simple initial conditions left by the Big Bang directly with the complex\, developed state of the present universe and thus trace the life of galaxies in detail. They show how a cosmic network of dark matter is created over 13 billion years\, at the intersections of which structures of various sizes form\, from small dwarf galaxies to enormous galaxy clusters. The supercomputers also make predictions about the specific formation history of the Milky Way\, and how it should develop in the future. At the same time\, the simulations can also help us to explain extreme phenomena such as the effect of supermassive black holes on the cosmic evolution of galaxies. \nSponsored by the Department of Astronomy
URL:https://micde.umich.edu/event/the-orren-c-mohler-prize-lecture/
LOCATION:Forum Hall\, Palmer Commons\, 100 Washtenaw Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Astronomy,Computation,Computational Modeling,Computational Science,computing
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2025/04/event_133141_original-1.jpeg
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250408T113000
DTEND;TZID=America/Detroit:20250408T130000
DTSTAMP:20260621T004914
CREATED:20250114T141754Z
LAST-MODIFIED:20260413T190501Z
UID:10000796-1744111800-1744117200@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n  \n  \n\nProbabilistic Rounding Uncertainty Analysis for Floating-Point Statistical Models\nAdvancements in computer hardware now allow low- and mixed-precision arithmetic to improve efficiency\, especially on new architectures. It is thus critical that the rounding uncertainty be rigorously quantified alongside traditional sources of uncertainty including those from observations\, sampling\, and numerical discretization. Traditional deterministic rounding uncertainty analysis (DBEA) assumes that the absolute rounding errors equal the unit roundoff u\, considering the worst-case scenario. This work presents a novel probabilistic rounding uncertainty analysis called VIBEA. By treating rounding errors as i.i.d. random variables and leveraging concentration inequalities\, VIBEA provides high-confidence estimates for rounding uncertainty using higher-order rounding error statistics. The presented framework is valid for all problem sizes n\, unlike DBEA\, which requires nu<1. Further\, it can account for the potential cancellation of rounding errors\, resulting in rounding uncertainty estimates that grow slowly with n. We demonstrate that quantifying rounding uncertainty alongside traditional sources allows for a more efficient allocation of computational resources\, balancing efficiency with accuracy. This study takes a step towards a comprehensive mixed-precision approach to enhance model reliability and optimize resource allocation in predictive modeling. The talk will conclude with a vision for end-to-end\, formally verified numerics for scientific computing. \nSahil Bhola\, Aerospace Engineering and Scientific Computing\nSahil Bhola is a 4th-year Ph.D. candidate in Aerospace Engineering and Scientific Computing at the University of Michigan. He is a MICDE Fellow and a J.N. Tata Scholar\, advised by Prof. Karthik Duraisamy. He holds a master’s degree in Aerospace Engineering from the University of Michigan and a bachelor’s degree in Mechanical Engineering from Thapar University\, India. His research focuses on adaptive mixed-precision methods\, experimental design for potential energy surfaces\, and flow-based generative models for Bayesian inference. \n\nHomogenous Cities? How Conflict and Politics Shape the Urban Topography\nThe relationship between armed conflict\, politics\, and the urban built environment \nMartin Macias Medellin\, Political Science\nMartin Macias Medellin is interested in the dynamics of mass political dissent\, political and criminal violence\, and state-building processes. In his doctoral dissertation he studies how conflict affects the way in which cities are built and how the physical structures of urban areas affect the dynamics of armed conflict. \n\nMinimally Orthogonal Causal Effect Estimation\nCausal Machine Learning \nYiman Ren\, Business Economics\nYiman Ren is a final year PhD student in Business Economics and Scientific Computing at Ross School of Business. Her research focuses on financial economics and causal machine learning.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-8-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250403T160000
DTEND;TZID=America/Detroit:20250403T170000
DTSTAMP:20260621T004914
CREATED:20250308T043518Z
LAST-MODIFIED:20250310T172122Z
UID:10000813-1743696000-1743699600@micde.umich.edu
SUMMARY:Scientific Computing in the Biological and Health Sciences information session
DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Biology\, Kinesiology\, Medicine\, Pharmacy\, Public Health\, or any other biological or health-related science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend
URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-biological-and-health-sciences/
LOCATION:Weiser Hall – 170
CATEGORIES:Basic Science,Biology,Biomedical Engineering,Biosciences,Computation,Computational Modeling,Computational Science,Computational Social Science,computing,Ecology And Evolutionary Biology,Epidemiology,Evolutionary Biology,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,In Person,Interdisciplinary,Kinesiology,Life Science,Machine Learning,Medicine,Micde,Natural Sciences,Neuroscience,Pharmacy,Prospective Graduate Students,Psychology,Public Health,Rackham,Research,Science,Scientific Computing,Virtual
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Biological-and-Health-Sciences.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250401T160000
DTEND;TZID=America/Detroit:20250401T170000
DTSTAMP:20260621T004914
CREATED:20250308T043515Z
LAST-MODIFIED:20250310T172100Z
UID:10000812-1743523200-1743526800@micde.umich.edu
SUMMARY:Scientific Computing in the Physical Sciences information session
DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Applied Physics\, Astronomy\, Biophysics\, Chemistry\, Earth and Environmental Sciences\, Math\, Physics\, or any other physical science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend
URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-physical-sciences/
LOCATION:Weiser Hall – 170
CATEGORIES:Applied Physics,Astronomy,Biophysics,Chemistry,Computation,Computational Modeling,Computational Science,computing,Earth And Environmental Sciences,Environment,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Life Science,Machine Learning,Mathematics,Micde,Natural Sciences,Physics,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Virtual
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Physical-Sciences.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250401T120000
DTEND;TZID=America/Detroit:20250401T130000
DTSTAMP:20260621T004914
CREATED:20250114T141907Z
LAST-MODIFIED:20260522T154208Z
UID:10000795-1743508800-1743512400@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nMonitoring the fidelity of the LIGO detectors\nThe detection of gravitational waves depends on LIGO’s ability to discriminate authentic signals from instrumental noise. To improve this capability\, the LIGO Scientific Collaboration employs hardware injections\, controlled\, simulated signals introduced directly into the detectors. These injections validate the analysis pipelines and refine the calibration of the detector. This study focuses on continuous- wave signals from the initial phase of the fourth observing run (O4a)\, using simulated emissions from rapidly rotating neutron stars as benchmarks to assess sensitivity and data-processing efficiency. The analysis employs a template generation approach that uses complex conjugates to align observational data with theoretical signal templates and offers probabilistic validation of detected signals. An investigation explores the role of hardware injections in the refinement of software models and the maintenance of the timing and amplitude. By utilizing daily diagnostic plots for a diverse array of synthetic neutron star signals\, including both binary and isolated systems\, the detector’s responsiveness is evaluated over a broad frequency spectrum. The results emphasize the importance of hardware injections in sustaining calibration standards and affirming LIGO’s reliability in gravitational wave detection \nPreet Baxi\, Physics and Scientific Computing\nPreet Baxi is an innovative Data Scientist and Algorithm Developer with experience in scientific computing\, data pipeline optimization\, and business data analysis. Specializing in developing advanced algorithms and has worked extensively in gravitational wave data analysis\, contributing to cutting-edge research in astrophysics. Currently working in large language models (LLMs)\, focusing on their development and optimization. \n\nFast Summation for Geophysical Fluid Dynamics\nFast Summation refers to a family of techniques for the fast approximation of N-body sums. While traditionally fast summation has been applied to problems coming from astrophysics or electrodynamics\, many problems in geophysical fluid dynamics can be rewritten as the computation of a spherical convolution\, and when these integrals are discretized\, the resulting problem is a N-body problem. In this talk\, I discuss a novel spherical tree code/fast multipole method based on barycentric Lagrange interpolation\, as well as applications to problems coming from geophysical fluid dynamics\, including tidal modeling and the problem of computing Self Attraction and Loading in the ocean model MOM6. \nAnthony Chen\, Applied and Interdisciplinary Mathematics and Scientific Computing\nAnthony Chen is a 4th year in Applied and Interdisciplinary Mathematics working on fast summation for problems in geophysics.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-1-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250328T120000
DTEND;TZID=America/Detroit:20250328T130000
DTSTAMP:20260621T004914
CREATED:20250324T152136Z
LAST-MODIFIED:20250324T152136Z
UID:10000816-1743163200-1743166800@micde.umich.edu
SUMMARY:FSML Lecture Series - Lianghao Cao (Caltech): Derivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion
DESCRIPTION:Zoom link \nBio: Dr. Lianghao Cao is a Postdoctoral Scholar Research Associate from the Department of Computing and Mathematical Sciences at the California Institute of Technology. He obtained a B.S. in Engineering Mechanics from the University of Illinois at Urbana-Champaign and a Ph.D. in Computational Science\, Engineering\, and Mathematics from The University of Texas at Austin. His research blends mechanistic modeling\, uncertainty quantification\, and scientific machine learning to understand\, enhance\, and control the quality\, validity\, and reliability of simulation-based predictions of complex physical systems. \nDerivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion\nAbstract: This talk focuses on a derivative-informed supervised learning method for efficiently building machine learning surrogates of high-fidelity computational models\, particularly those governed by parametric partial differential equations. Unlike the conventional supervised learning method that treats the model as a black box\, our approach leverages additional model sensitivity information\, extracted via solving forward or adjoint sensitivity equations. This sensitivity information is integrated into the surrogate’s architecture and training process based on rigorous error analysis. We refer to such a surrogate construction as DINO (derivative-informed neural operator). \nDINO offers two key advantages over conventional surrogate construction. First\, it significantly improves the cost-accuracy trade-off for a wide range of models\, often by one to two orders of magnitude. Second\, it directly controls the surrogate Jacobian (Fréchet derivative) errors\, thus enhancing performance in surrogate-driven outer-loop problems that use gradient- and Hessian-based optimization algorithms. We demonstrate DINO’s capability to accelerate infinite-dimensional Bayesian inversion. First\, we show that geometric MCMC driven by DINO achieves a 2–9x speed up in asymptotically exact posterior sampling. Second\, we introduce LazyDINO\, a DINO-driven measure transport method for amortized Bayesian inversion\, which is one to two orders of magnitude more cost-efficient than competing methods.\nThis talk is based on joint work with Michael Brennan\, Joshua Chen\, Omar Ghattas\, Youssef Marzouk\, and Thomas O’Leary-Roseberry.
URL:https://micde.umich.edu/event/fsml-lecture-series-lianghao-cao-caltech-derivative-informed-operator-learning-with-applications-to-cost-efficient-bayesian-inversion/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250320T153000
DTEND;TZID=America/Detroit:20250320T163000
DTSTAMP:20260621T004914
CREATED:20241011T222200Z
LAST-MODIFIED:20250120T170935Z
UID:10000782-1742484600-1742488200@micde.umich.edu
SUMMARY:MICDE-EECS Seminar: Mikhail Belkin\, Professor\, University of California San Diego
DESCRIPTION:Bio: Mikhail Belkin is a Professor at Halicioglu Data Science Institute and Computer Science and Engineering Department at UCSD and an Amazon Scholar. Prior to that he was a Professor at the Department of Computer Science and Engineering and the Department of Statistics at the Ohio State University. He received his Ph.D. from the Department of Mathematics at the University of Chicago (advised by Partha Niyogi). His research interests are broadly in theory and applications of machine learning\, deep learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps\, Graph Regularization and Manifold Regularization algorithms\, which brought ideas from classical differential geometry and spectral graph theory to data science. His more recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. The empirical evidence necessitated revisiting some of the classical concepts in statistics and optimization\, including the basic notion of over-fitting. One of his key findings has been the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. His recent work focusses on understanding feature learning and over-parameterization in deep learning. Mikhail Belkin is an ACM Fellow and a recipient of a NSF Career Award and a number of best paper and other awards. He had served on the editorial boards of IEEE Proceedings on Pattern Analysis Machine Intelligence and the Journal of the Machine Learning Research. He is the editor-in-chief of SIAM Journal on Mathematics of Data Science (SIMODS). \nEmergence and grokking in “simple” architectures\nAbstract: In recent years\, transformers have become a dominant machine learning methodology.\nA key element of transformer architectures is a standard neural network (MLP). I argue that MLPs alone already exhibit many remarkable behaviors observed in modern LLMs\, including emergent phenomena. Furthermore\, despite large amounts of work\, we are still far from understanding how 2-layer MLPs learn relatively simple problems\, such as “grokking” modular arithmetic. I will discuss recent progress and argue that feature-learning kernel machines (Recursive Feature Machines) isolate some key computational aspects of modern neural architectures and are preferable to MLPs as a model for analysis of emergent phenomena.
URL:https://micde.umich.edu/event/mikhail-belkin/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Mikhail-Belkin-2.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250318T120000
DTEND;TZID=America/Detroit:20250318T130000
DTSTAMP:20260621T004914
CREATED:20250114T142206Z
LAST-MODIFIED:20250228T184043Z
UID:10000794-1742299200-1742302800@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n  \n\nSolving High Reynolds Number Flows on Cartesian Cut-cell Meshes using a Jacobian-Free Newton–Krylov Method\nIn this work\, we developed a Newton–Krylov method for a second-order Cartesian cut cell Reynolds-averaged Navier–Stokes (RANS) solver\, Viscous Aerodynamic Cartesian Cut cells (VACC)\, with the one equation Spalart–Allmaras (SA) turbulence model. The Newton–Krylov method uses pseudo-transient continuation and a point Jacobi preconditioner to accelerate convergence. Then various wall functions were compared on a finite flat plate and 2D bump cases. The SA analytical wall function was used as a baseline. An ordinary differential equation (ODE) wall function and wall-modeled RANS (WMRANS) approach were also implemented. Although these methods all showed promise\, the interior viscous fluxes resulted in oscillatory pressures. These oscillations degraded the accuracy of all of the solutions. \nAlex Kleb\, Aerospace Engineering\nAlex Kleb is a fifth year PhD candidate in the CFD Group and MDO lab in the Aerospace Engineering department. \n\nGeometrically Nonlinear Methods for High-Fidelity MDO of Very-Flexible Aircraft\nOver the past decade\, advances in Multidisciplinary Design Optimization (MDO) have enabled the optimization of aircraft wings using high-fidelity simulations of their coupled aerodynamic and structural behavior. Using RANS CFD and detailed structural finite element wingbox models\, the aerodynamic shape and internal structural sizing of a wing can be optimized concurrently to tailor the wing’s aeroelastic behavior and optimally trade-off drag and structural mass. This capability makes MDO a key enabling technology for the next generation of efficient high-aspect-ratio transport aircraft. However\, as their aspect-ratios increase\, these wings increasingly exhibit geometrically nonlinear behavior that cannot be correctly modeled by typical linear structural analysis methods. This work demonstrates the first simultaneous optimization of a wing’s aerodynamic shape and structural sizing using high-fidelity geometrically nonlinear models. Our methods are implemented in the open-source finite element library\, TACS\, and include a geometrically nonlinear shell element formulation\, an efficient and robust nonlinear solver\, and a constitutive model for stiffened shells. We demonstrate the ability to couple these nonlinear structural analysis tools to a high-fidelity RANS CFD solver using a geometrically nonlinear load and displacement transfer scheme. Finally\, we use this capability to optimize a single-aisle commercial transport aircraft wing featuring 547 design variables and 1277 constraints. \nAlasdair Christison Gray\, Aerospace Engineering\nAlasdair Christison Gray is a 5th year PhD student in the Aerospace Engineering department’s MDO Lab. His research focuses on applying high performance computing to the large scale design optimization of aircraft wings.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-3-18-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-03-18-Kleb-Christison-Gray.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20260621T004914
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20260621T004914
CREATED:20250311T130516Z
LAST-MODIFIED:20250311T130831Z
UID:10000814-1741953600-1741957200@micde.umich.edu
SUMMARY:FSML Lecture Series - Alexander Tong (Mila - Quebec AI Institute): Flow matching in cell trajectories and protein design
DESCRIPTION:Zoom link \nBio: Alexander 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. \nFlow matching in cell trajectories and protein design\nAbstract: Generative 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.
URL:https://micde.umich.edu/event/fsml-lecture-series-alexander-tong-mila/
LOCATION:1642 GGBL\, 2350 HAYWARD ST\, Ann Arbor\, 48109\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Alexander-Tong.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250311T120000
DTEND;TZID=America/Detroit:20250311T130000
DTSTAMP:20260621T004914
CREATED:20250225T214118Z
LAST-MODIFIED:20250225T214448Z
UID:10000810-1741694400-1741698000@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nUnraveling Rotator Cuff Tendon Tear Growth Mechanisms with Full-Volume Strains and Data-Driven Modeling\nIn this talk\, I will show how full-volume methods\, which can probe internal locations of a material\, enable the detection of regions with high shear strain concentration in intact and torn rotator cuff tendons. I will also explain my approach to use these full-volume datasets to develop a finite element model of this tendon using variational system identification\, and future work to obtain validated computational models that can predict tear growth. \nNathaly Villacis\, Mechanical Engineering and Scientific Computing\nNathaly is a fifth year Ph.D. candidate in Mechanical Engineering\, who works at the Soft Tissue Mechanics Lab\, supervised by Dr. Ellen Arruda. Her research involves the characterization of rotator cuff tendon tear growth with experimental and computational methods. She will work on machine learning models of the rotator cuff once she finishes her Ph.D. \n\nIncremental Tensor Decompositions for Machine Learning and Bayesian Inference\nWith recent advancements in large-scale parallel computing\, there is an increased interest in constructing high-fidelity digital twins of complex systems. Especially for systems that have limited physical experimentation possibilities\, high-fidelity simulations may provide the main source of information for constructing digital twins. However\, performing such simulations is computationally intensive and generates extreme amounts of data. The size of the generated simulation data makes it challenging to use the data in further analysis. As the spatial and temporal resolution of these simulations grow\, even storing the data may become a serious bottleneck. This talk proposes a solution to this multi-faceted problem through the use of low-rank tensor decompositions. Specifically\, we present incremental algorithms that provide computationally efficient ways of compressing data with accuracy guarantees. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques. \nDoruk Aksoy\, Aerospace Engineering and Scientific Computing\nDoruk is a 5th year Ph.D. candidate in the Department of Aerospace Engineering in the Computational Autonomy group. His research focuses on developing incremental low-rank tensor decomposition algorithms to compress large-scale data for downstream machine learning applications.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-20250311/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/2025-03-11-Villacis-Aksoy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250228T120000
DTEND;TZID=America/Detroit:20250228T130000
DTSTAMP:20260621T004914
CREATED:20250225T171723Z
LAST-MODIFIED:20250227T225237Z
UID:10000809-1740744000-1740747600@micde.umich.edu
SUMMARY:FSML Lecture Series - Doruk Aksoy: From Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition
DESCRIPTION:Zoom link \nBio:Doruk Aksoy is a 5th year PhD candidate in Aerospace Engineering and Scientific computing at the University of Michigan\, working under the supervision of Prof. Alex Gorodetsky. Prior to joining UM\, he studied Mechanical Engineering at Bogazici University in Istanbul Turkey. During his PhD\, he worked on developing incremental tensor decomposition algorithms to accelerate scientific machine learning through data reduction. \nFrom Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition\nAbstract: The rapid advancement of large-scale parallel computing created a surge of interest in developing high-fidelity digital twins for complex systems. However\, the computational demands for training these models are immense\, requiring vast amounts of data. As the spatial and temporal resolution of simulations increases\, even data storage becomes a critical bottleneck. This talk presents how low-rank tensor decomposition methods can be used to exploit the structure in large-scale data. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques.
URL:https://micde.umich.edu/event/fsml-lecture-series-doruk-aksoy/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Aksoy-Doruk.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250225T120000
DTEND;TZID=America/Detroit:20250225T130000
DTSTAMP:20260621T004914
CREATED:20250114T142545Z
LAST-MODIFIED:20250221T144301Z
UID:10000793-1740484800-1740488400@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nNumerical simulation of the collapse of a cavitation bubble near a deformable solid surface\nThe exact mechanisms leading to the permanent deformation of solid surfaces\, following a cavitation event\, are still unclear. Specifically\, the relationship between the characteristics of a given cavitation bubble and the shape of the resulting pit is unknown. In this study\, we numerically investigate the collapse of a single cavitation bubble near a solid surface\, with the objective of characterizing how the pit shape (height and depth) changes with the bubble initial radius\, its distance from the solid and the initial pressure difference at the bubble interface. To this end\, we implement a diffuse interface method for the interaction of multiple compressible fluids and hyperelastic solids in an Eulerian frame of reference. This method numerically solves the evolution equations of mass\, momentum\, energy as well as volume fractions of each material and of the mixture. The model is closed by splitting the internal energy of each material into hydrodynamic and elastic contributions\, with appropriate equations of state. A set of evolution equations of local cobasis\, with a plastic source term\, are used to compute the elastic Finger tensor\, which is needed to obtain the elastic energy and the deviatoric stress. We additionally provide improvements to the numerical method to preserve interface conditions. The proposed method allows to elucidate some of the mechanisms of cavitation pitting.  \nBaudouin Fonkwa Kamga\, Mechanical Engineering and Scientific Computing\nBaudouin is a 4th year PhD student in the department of Mechanical Engineering\, under the supervision of Eric Johnsen. His research combines the theoretical study of cavitation in viscoelastic medium and the development of numerical methods for multimaterial compressible flows. \n\nScalable foundation model training for computational pathology\nScalable and efficient foundation model training is critical for advancing computational pathology. In this talk\, we present a two-stage self-supervised pipeline for whole slide image (WSI) analysis. First\, HiDisc leverages the inherent patient–slide–patch hierarchy to learn robust visual representations efficiently without relying on heavy data augmentation\, outperforming existing methods in cancer diagnosis and genetic mutation prediction. Building on these high-quality patch-level features\, our second stage\, Slide Pre-trained Transformers (SPT)\, treats WSI patches as tokens and integrates data transformation strategies from both language and vision models to capture the rich morphological diversity of gigapixel images. Together\, these methods offer a scalable\, efficient framework for training foundation models that drive robust performance across a range of diagnostic tasks. \nXinhai Hou\, Bioinformatics and Scientific Computing\nXinhai Hou is a PhD candidate in the department of computational medicine and bioinformatics. His research focuses on self-supervised learning\, computer vision\, and multimodal machine learning\, with a particular emphasis on real-world applications such as AI in healthcare and medicine.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-2-25-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-25-Fonkwa-Kamga-Hou-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250214T093000
DTEND;TZID=America/Detroit:20250214T103000
DTSTAMP:20260621T004914
CREATED:20250212T173700Z
LAST-MODIFIED:20250212T173805Z
UID:10000808-1739525400-1739529000@micde.umich.edu
SUMMARY:FSML Lecture Series - Ricardo Vinuesa: Identifying coherent structures and controlling turbulent flows through deep learning
DESCRIPTION:Zoom link \nBio: Dr. Ricardo Vinuesa is joining the Department of Aerospace Engineering at the University of Michigan in the Fall of 2025. He is currently an Associate Professor at the Department of Engineering Mechanics\, KTH Royal Institute of Technology in Stockholm. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain)\, and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand\, control and predict complex wall-bounded turbulent flows\, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received\, among others\, an ERC Consolidator Grant\, the TSFP Kasagi Award\, the MST Emerging Leaders Award\, the Goran Gustafsson Award for Young Researchers\, the IIT Outstanding Young Alumnus Award\, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain. \nIdentifying coherent structures and controlling turbulent flows through deep learning\nAbstract: In this work we first use explainable deep learning based on Shapley explanations to identify the most important regions for predicting the future states of a turbulent channel flow. The explainability framework (based on gradient SHAP) is applied to each grid point in the domain\, and through percolation analysis we identify coherent flow regions of high importance. These regions have around 70% overlap with the intense Reynolds-stress (Q) events in two-dimensional vertical planes. Interestingly\, these importance-based structures have high overlap with classical turbulence structures (Q events\, streaks and vortex clusters) in different wall-normal locations\, suggesting that this new framework provides a more comprehensive way to study turbulence. We also discuss the application of deep reinforcement learning (DRL) to discover active-flow-control strategies for turbulent flows\, including turbulent channels\, three-dimensional cylinders and turbulent separation bubbles. In all the cases\, the discovered DRL-based strategies significantly outperform classical flow-control approaches. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations. \n 
URL:https://micde.umich.edu/event/fsml-lecture-series-ricardo-vinuesa-identifying-coherent-structures-and-controlling-turbulent-flows-through-deep-learning/
LOCATION:2210 Lurie Engineering Center\, 1221 Beal Ave\, Ann Arbor\, MI\, 48105
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/Ricardo-Vinuesa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250211T120000
DTEND;TZID=America/Detroit:20250211T130000
DTSTAMP:20260621T004914
CREATED:20250114T150617Z
LAST-MODIFIED:20260522T152322Z
UID:10000792-1739275200-1739278800@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAdaptive Deep Learning-Powered Multi-fidelity Stratified Sampling for Efficient Failure Analysis of Nonlinear Dynamic Systems\nCurrent stochastic simulation-based frameworks that leverage variance reduction techniques still require a substantial number of model evaluations to estimate small failure probabilities associated with rare events. In the context of high-fidelity modeling environments\, these frameworks can become computationally challenging\, especially when dealing with complex nonlinear systems. Despite the potential of providing remarkable computational efficiency\, low-fidelity models may yield bias if used directly. To address this challenge\, this work introduces a multi-fidelity framework within the setting of stratified sampling\, termed Multi-Fidelity Stratified Sampling (MFSS)\, for efficient estimation of failure probabilities given various limit states of interest. In this approach\, the strata-wise failure probabilities\, associated with a carefully selected stratification variable\, are estimated by multi-fidelity Monte Carlo. To minimize the computational budget\, the high-fidelity data used in the stratified multi-fidelity estimator is also employed as training data for developing a deep learning-based metamodel\, which then serves as a low-fidelity model. To derive the trade-off between the approximation quality and computational demand associated with the metamodel\, an adaptive strategy is proposed to seek the minimal training data that ensures a desired correlation between the high- and low-fidelity models. Through application to a full-scale high-rise steel building subject to stochastic wind excitation\, the proposed scheme is demonstrated to be capable of accurately reproducing exceedance probability curves of nonlinear responses of interest with significant computational gains\, compared to variance reduction techniques relying solely on high-fidelity models. \nLiuyun Xu (Civil Engineering and Scientific Computing)\nLiuyun Xu is a fourth-year Ph.D. candidate in Civil Engineering and Scientific Computing at the University of Michigan. Her research lies in enhancing the resilience and adaptation of civil infrastructures against climate-related hazards by leveraging AI/ML\, scientific computing and data science.  \n\nA Hybrid Surrogate Modeling Framework for Digital Twins of Nuclear Energy Systems\nNuclear Power Plants (NPPs) are complex systems that can benefit from Digital Twin (DT) technologies to reduce operational costs and increase plant reliability. A system surrogate model is developed to predict quantities and responses associated with diverse physical and computational assets. The proposed hybrid surrogate modeling framework is applied to a Pebble-Bed Fluoride-salt-cooled High-temperature Reactor (PB-FHR)\, with a two-loop reactor configuration. The surrogate’s hybrid design combines the accuracy of physical models and computational efficiency of data-driven models to achieve speed and predictive robustness. This surrogate model is adaptable through assimilation with online measurements\, which is highlighted in a proposed DT framework design. \nJasmin Lim (Aerospace Engineering and Scientific Computing)\nJasmin is a 5th PhD student in the department of aerospace engineering in the Computational Aerosciences Laboratory under the advisement of Karthik Duraisamy. Her research is focused on developing data-driven methods for digital twin applications; which includes surrogate modeling\, data assimilation\, and system framework design.  \n\nRegister to attend
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-2-11-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-11-Xu-Lim.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250206T150000
DTEND;TZID=America/Detroit:20250206T160000
DTSTAMP:20260621T004914
CREATED:20241011T222159Z
LAST-MODIFIED:20260522T151605Z
UID:10000781-1738854000-1738857600@micde.umich.edu
SUMMARY:MICDE-IOE Seminar: Jong-Shi Pang\, Professor\, University of Southern California
DESCRIPTION:Bio: Elected a member of the National Academy of Engineering in February 2021 and appointed a Distinguished Professor in April 2023\, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position\, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A. Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007. He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003\, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas\, and an Assistant and then an Associate Professor in the Graduate School of Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full-time) and 2002 (part-time)\, he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences. \nHeaviside Composite Optimization\, a new paradigm of optimization\nAbstract: This talk introduces the topic of Heaviside composite optimization and briefly covers its many facets: breadth in modeling\, roles in old and new applications\, theory of optimizers and stationary solutions\, bridge with discrete optimization\, and the progressive integer programming method. By definition\, a univariate Heaviside function is the (discontinuous) indicator of an interval. By its name\, a Heaviside composite function is the composition of a Heaviside function with a continuous multivariate function that may be nonconvex and nondifferentiable. While very natural in modeling many physical phenomena\, a Heaviside composite optimization problem\, possibly with Heaviside composite functional constraints\, has never been formally studied. Our work aims to fill this void with a comprehensive research program covering the applications\, theory\, and algorithms for this novel class of very challenging optimization problems. \nThis research has benefitted from previous collaboration with Ying Cui (UC Berkeley)\, Yue Fan (CUHK-SZ)\, Shaoning Han (NUS)\, Junyi Liu (Tsinghua)\, and Xinyao Zhang (USC)\, and is presently being organized in a monograph co-authored with Junyi Lui.
URL:https://micde.umich.edu/event/jong-shi-pang/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Jong-Shi-Pang.png
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250204T120000
DTEND;TZID=America/Detroit:20250204T130000
DTSTAMP:20260621T004914
CREATED:20250114T145459Z
LAST-MODIFIED:20260522T154253Z
UID:10000791-1738670400-1738674000@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAerodynamic Shape Optimization with Curved Mesh Adaptation\nIn this talk we present a method for performing curved mesh adaptation during aerodynamic shape optimization with high-order computational fluid dynamics (CFD). High-order methods are promising because they offer increased accuracy for a given mesh. Mesh adaptation further improves the efficiency of high-order methods. These high-order methods require curved meshes to properly capture the simulated geometry and a mesh adaptation process that can generate curved meshes. Adapting these curved meshes needs to be robust as any failures will require human intervention inside the automated optimization loop. We first will present HOEP\, a novel and highly robust method for adapting highly-anisotropic curved meshes. Then we will present our adaptation strategy that balances computational cost with accuracy and show results for transonic airfoil optimization. \nAlexander Coppeans\, Aerospace Engineering and Scientific Computing\nAlexander Coppeans is a 5th year PhD Student in Aerospace Engineering and Scientific Computing. His research focuses on high-order adaptive methods for CFD based aerodynamic shape optimization. \nRegister to attend
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-02-04-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-04-Coppeans.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250131T120000
DTEND;TZID=America/Detroit:20250131T130000
DTSTAMP:20260621T004914
CREATED:20250127T173918Z
LAST-MODIFIED:20250127T193042Z
UID:10000799-1738324800-1738328400@micde.umich.edu
SUMMARY:FSML Lecture Series - Panos Stinis: When big neural networks are not enough: physics\, multi-fidelity and kernels
DESCRIPTION:Zoom link \nBio: Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems\, multiscale modeling\, uncertainty quantification\, and machine learning. He studied aeronautical engineering at the Technical University of Athens\, Greece. He earned his PhD in applied mathematics in 2003\, from Columbia University in New York and began his career as a postdoctoral fellow at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research. In 2008\, he became a faculty member at the Mathematics Department at the University of Minnesota. He moved to the Pacific Northwest National Laboratory in 2014\, where he is currently leading the Computational Mathematics group. \nWhen big neural networks are not enough: physics\, multi-fidelity and kernels\nAbstract: Modern machine learning has shown remarkable promise in multiple applications. However\, brute force use of neural networks\, even when they have huge numbers of trainable parameters\, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics\, exploiting multi-fidelity knowledge\, and the kernel representation of neural networks can lead to a significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas. \n 
URL:https://micde.umich.edu/event/fsml-lecture-series-panos-stinis-when-big-neural-networks-are-not-enough-physics-multi-fidelity-and-kernels/
LOCATION:2004 Lay Auto Lab
CATEGORIES:Engineering,FSML,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/Panos-Stinis.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250128T160000
DTEND;TZID=America/Detroit:20250128T170000
DTSTAMP:20260621T004914
CREATED:20241011T222157Z
LAST-MODIFIED:20260522T151639Z
UID:10000780-1738080000-1738083600@micde.umich.edu
SUMMARY:MICDE Seminar: Joshua Dolence\, Research Scientist\, Los Alamos National Lab
DESCRIPTION:Bio: Josh Dolence is a scientist in the Computational Physics & Methods Group at Los Alamos National Laboratory and a founding member of the LANL Michigan SPARC\, a permanent LANL presence at U-M in Ann Arbor. Before joining LANL\, he received a PhD in Astronomy from UIUC in 2011 and spent three years in Astrophysical Sciences at Princeton University where he worked in computational astrophysics\, studying topics like black hole accretion and supernovae. More recently\, he leads the Methods for Multiscale\, Multiphysics Accelerated Prediction project for LANL’s Advanced Simulation and Computing Program\, focusing efforts on enabling unprecedented fidelity and scale in modeling complex systems like high energy density physics experiments. \nParthenon: a flexible framework for rapid development of performance portable multiphysics codes\nAbstract: In many areas of computational science\, developing new\, state-of-the-art capabilities has become a high-cost\, risky proposition. The complexity and diversity of models\, methods\, algorithms\, and machines often lead to fundamental challenges in designing and building codes that enable advances in science and engineering. In fields like high energy density physics and astrophysics\, multiphysics simulations leveraging adaptive meshes\, particles\, and a variety of numerical methods are foundational to progress but difficult to realize performantly on ever-evolving high-performance computing platforms. In this talk\, I will present the Parthenon framework\, an open-source code base that aims to facilitate the development of highly adaptive\, multiphysics codes that are fast\, scalable\, and capable of leveraging modern platforms with both CPUs and GPUs. I will describe the basic principles behind its design and some of its most enabling features and highlight the ~10 downstream codes it already supports. \n 
URL:https://micde.umich.edu/event/joshua-dolence/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Joshua-Dolence-LANL.png
GEO:42.2914823;-83.7138452
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250121T150000
DTEND;TZID=America/Detroit:20250121T160000
DTSTAMP:20260621T004914
CREATED:20250106T213237Z
LAST-MODIFIED:20250128T161655Z
UID:10000790-1737471600-1737475200@micde.umich.edu
SUMMARY:MICDE-CSE Seminar: Andrew Appel\, Professor\, Princeton University
DESCRIPTION:Bio: Andrew Appel is Eugene Higgins Professor Computer Science\, and served from 2009-2015 as Chair of Princeton’s CS department. His research is in software verification\, computer security\, programming languages and compilers\, and technology policy. He received his A.B. summa cum laude in physics from Princeton in 1981\, and his Ph.D. in computer science from Carnegie Mellon University in 1985. Professor Appel has been editor in chief of ACM Transactions on Programming Languages and Systems and is a fellow of the ACM (Association for Computing Machinery). He has worked on fast N-body algorithms (1980s)\, Standard ML of New Jersey (1990s)\, Foundational Proof-Carrying Code (2000s)\, and the Verified Software Toolchain (2010-present). \nFormally Verified Numerical Methods\nAbstract: Formal machine-checked program verification uses mechanized logical tools to connect low-level programs to the specifications of the algorithms they are supposed to implement. The same program verification tools can work in many application domains. But it’s not enough just to implement an algorithm; the program is fully “correct” only if the algorithm (provably) computes an answer to the problem or question of interest. Proofs of algorithm correctness rely on the mathematics of the application domains\, and each domain has its own mathematics.\nIn recent years we have applied this method to numerical methods (algorithms for scientific computing) and numerical analysis (reasoning about the accuracy of those methods)\, with machine-checked proofs formally connected to low-level program-correctness proofs. I will discuss the results of the numerical integration of differential equations and the solving of linear systems. Some of these results are joint work with Ariel Kellison and David Bindel (Cornell)\, Mohit Tekriwal and Jean-Baptiste Jeannin (Michigan).
URL:https://micde.umich.edu/event/micde-cse-seminar-andrew-appel-professor-princeton-university/
LOCATION:BBB 3725\, 2260 Hayward St.\, Ann Arbor\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/Andrew-Apple.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250117T160000
DTEND;TZID=America/Detroit:20250117T170000
DTSTAMP:20260621T004914
CREATED:20241224T044635Z
LAST-MODIFIED:20260522T182843Z
UID:10000789-1737129600-1737133200@micde.umich.edu
SUMMARY:MICDE - NERS Seminar: Teresa Bailey\, Lawrence Livermore National Laboratory
DESCRIPTION:Bio: Teresa S. Bailey is the Associate Program Director of Computational Physics in LLNL’s Weapon Simulation and Computing program. She oversees the development of multiple multiphysics simulation tools across a wide range of applications. These codes span a broad range of physics\, chemistry\, and engineering application space. As required\, the codes are production-quality software products that are portable and computationally efficient on DOE’s most advanced HPC systems. \nBailey has been an LLNL employee since 2008. She began her career as a code physicist before moving into technical leadership roles as the Deterministic Transport project leader and the Nuclear Science program group leader. Bailey earned her B.S. in Nuclear Engineering from Oregon State University in 2002. She received the DOE Computational Science Graduate Fellowship to support her graduate work and earn her Ph.D. in Nuclear Engineering from Texas A&M in 2008. \nComputational Science and High-Performance Computing at Lawrence Livermore National Laboratory
URL:https://micde.umich.edu/event/workshop-seminarmicde-ners-seminar-teresa-bailey-lawrence-livermore-national-laboratory/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:College Of Engineering,Computational Science,Micde,Micde Seminar,Michigan Engineering,Nuclear Engineering and Radiological Sciences,Physics,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/12/Teresa-Bailey-2.png
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241209T121500
DTEND;TZID=America/Detroit:20241209T131500
DTSTAMP:20260621T004914
CREATED:20240924T215159Z
LAST-MODIFIED:20241115T183858Z
UID:10000767-1733746500-1733750100@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:Mahmoud Komaiha (Biomedical Engineering) will give a talk on BME and Scientific Computing. \nZhucong Xi (Materials Science & Engineering) will give a talk on Multiscale Simulations of Solute Clustering in Aluminum Alloys. \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-8/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/09/2024-12-9-Updated.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241206T120000
DTEND;TZID=America/Detroit:20241206T130000
DTSTAMP:20260621T004914
CREATED:20241011T181202Z
LAST-MODIFIED:20241204T144214Z
UID:10000779-1733486400-1733490000@micde.umich.edu
SUMMARY:FSML Lecture Series - Anoushka Bhutani: Foundation Model for Molecular Design
DESCRIPTION:Zoom link \nBio: Anoushka is a third-year PhD student in Prof. Venkat Viswanathan’s group at the University of Michigan. Her research interests include machine learning for materials design and electrochemical battery modeling. \nFoundation Model for Molecular Design\nAbstract: The paradigm of molecular machine learning for material screening has accelerated material development cycles\, improved efficiency\, and reduced costs. However\, current state-of-the-art molecular property prediction models still require labeled training data generated using wet-lab experiments or Density Functional Theory (DFT) calculations. Their utility is limited by the scarcity and heterogeneity of labeled materials datasets. Foundation models (FMs) offer a solution to this: these models use self-supervised pre-training strategies to leverage unlabeled datasets and learn representations of data that can be applied to downstream tasks. Large unlabeled datasets of billions of synthesizable molecules are readily available. Prior attempts to train FMs for molecular property prediction demonstrate promise; however\, equivariant geometric models trained using supervised learning are still more accurate. This can be attributed to the fact that foundation models are extremely expensive to train and can be difficult to interpret; they require huge computing budgets\, complex distributed computing techniques\, and extensive hyperparameter searches. Our work addresses these challenges on three fronts: (1) we have prototyped a scalable workflow for distributed training of molecular foundation models (2) we have trained large foundation models using this workflow which demonstrates state-of-the-art molecular property prediction capabilities across several benchmarks\, and (3) we have applied model interpretability strategies such as the attention visualization to shed insight on molecular structure relationships learn by the transformer. \n 
URL:https://micde.umich.edu/event/workshop-seminaranoushka-bhutani-foundation-model-for-molecular-design/
LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States
CATEGORIES:Computational Science,Engineering,FSML,Graduate School,Graduate Students,Michigan Engineering,Rackham,Research,Science
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Copy-of-MICDE-2022-2023-Fellowship-Portraits.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241204T160000
DTEND;TZID=America/Detroit:20241204T170000
DTSTAMP:20260621T004914
CREATED:20241126T144049Z
LAST-MODIFIED:20241210T173845Z
UID:10000788-1733328000-1733331600@micde.umich.edu
SUMMARY:MICDE-Aerospace Engineering Seminar: Jan Janssen\, Scientist\, Max Planck Institute
DESCRIPTION:Bio:\nDr. Jan Janssen is the group leader for materials informatics at the Max Planck Institute for Sustainable Materials in Düsseldorf\, Germany. Previously\, he was a Director’s Postdoctoral Fellow at Los Alamos National Laboratory\, where he designed materials for fusion reactors as part of the Exascale Computing Project. In addition\, he leads the development of the open-source pyiron software package\, is a maintainer of over 900 open-source software packages for the conda-forge community and an active contributor to open-source projects on Github. \nTitle:\nHow to use machine learning in the discovery and design of materials for the future? \nAbstract:\nDesigning materials for a sustainable future requires rethinking traditional materials design\, which is centered on optimizing and fine-tuning already known alloying compositions. In a mathematical sense this can be identified as a local or global optimization in the multi-dimensional alloy phase space. To sample the whole periodic table\, already a three-component alloy with 20 temperature steps and 10 concentration steps requires a million experiments\, making it prohibitive for purely experimental approaches.\nTo address this challenge\, simulation approaches and\, more recently\, machine learning models are applied to screen the periodic table. The pyiron workflow framework developed at the Max-Planck-Institute for sustainable materials predicts new materials using ab-intio thermodynamics. Starting from the interaction of electrons\, it predicts macroscopic material properties like heat capacity\, thermal expansion\, and phase stability. Recently\, the pyiron workflow framework was extended with a large language model (LLM) interface named LangSim.\nThis raises the question: Can a LLM replace a scientist? Or how does the thought process of a scientist differ from the statistical approach of the LLM? Can the LLM make us better scientists? We benchmark the capabilities of current LLMs to design new materials using atomistic simulation. The presentation introduces ab-initio thermodynamics\, covers the importance of simulation workflows to efficiently predict sustainable materials and highlights how LLMs accelerate their discovery.
URL:https://micde.umich.edu/event/workshop-seminarmicde-aerospace-engineering-seminar-jan-janssen-how-to-use-machine-learning-in-the-discovery-and-design-of-materials-for-the-future/
LOCATION:Cooley Building – 906
CATEGORIES:Aerospace Engineering,Micde,Micde Seminar,Michigan Engineering
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/11/Jan-Janssen.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241203T170000
DTEND;TZID=America/Detroit:20241203T180000
DTSTAMP:20260621T004914
CREATED:20240824T040137Z
LAST-MODIFIED:20240824T040137Z
UID:10000744-1733245200-1733248800@micde.umich.edu
SUMMARY:Scientific Computing Student Club General Meeting
DESCRIPTION:Join Us at the Scientific Computing Club’s General Meeting! Don’t miss out on a chance to contribute your ideas and help shape the future of our club. Let’s connect\, collaborate\, and create something amazing together! \nWhere: TBD \nWhen: December 3rd\, 2024\, Tuesday\, 5:00 – 6:00 PM \nMeeting Agenda: TBD
URL:https://micde.umich.edu/event/scientific-computing-student-club-general-meeting-8/
CATEGORIES:SC2,Workshops
ATTACH;FMTTYPE=image/webp:https://micde.umich.edu/wp-content/uploads/2024/07/DALL·E-2024-07-02-21.58.15-A-minimalist-poster-design-for-a-Scientific-Computing-Student-Club-general-meeting.-The-poster-should-feature-a-sleek-modern-aesthetic-with-a-stron.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241202T121500
DTEND;TZID=America/Detroit:20241202T131500
DTSTAMP:20260621T004914
CREATED:20240924T215200Z
LAST-MODIFIED:20241115T184037Z
UID:10000768-1733141700-1733145300@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:Shuai Che from the Nuclear Engineering & Radiological Sciences department will be giving a talk on Thermal-sturctural analysis of printed circuit heat exchangers and supporting structures for molten salt test facility. \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-10/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/09/2024-12-2-Updated.png
END:VEVENT
END:VCALENDAR