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DTSTART;TZID=America/Detroit:20250311T120000
DTEND;TZID=America/Detroit:20250311T130000
DTSTAMP:20260604T074240
CREATED:20250225T214118Z
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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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20260604T074240
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:20250318T120000
DTEND;TZID=America/Detroit:20250318T130000
DTSTAMP:20260604T074240
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250320T153000
DTEND;TZID=America/Detroit:20250320T163000
DTSTAMP:20260604T074240
CREATED:20241011T222200Z
LAST-MODIFIED:20250120T170935Z
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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
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