BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Michigan Institute for Computational Discovery and Engineering - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://micde.umich.edu X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Detroit BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20230312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20231105T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241028T121500 DTEND;TZID=America/Detroit:20241028T131500 DTSTAMP:20260605T131533 CREATED:20240924T215158Z LAST-MODIFIED:20240926T204946Z UID:10000762-1730117700-1730121300@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. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-4/ LOCATION:Undergraduate Science Building – 1250 CATEGORIES:Micde,Phd Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241107T180000 DTEND;TZID=America/Detroit:20241107T190000 DTSTAMP:20260605T131533 CREATED:20241029T230120Z LAST-MODIFIED:20241030T170447Z UID:10000787-1731002400-1731006000@micde.umich.edu SUMMARY:Taking the Next Step: Graduate Studies in Computation/AI for Science and Engineering at U-M DESCRIPTION:PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master’s students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend. \nPlease register via the link: https://sessions.studentlife.umich.edu/p/track/12857 \nZoom option available after registering. URL:https://micde.umich.edu/event/taking-the-next-step-2024/ LOCATION:GG Brown Laboratory – 2147 CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Artificial Intelligence,Astronomy,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Science,Computational Social Science,computer science,computing,Earth And Environmental Sciences,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Epidemiology,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Michigan Engineering,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scientific Computing,Statistics,Talk,Undergraduate,Undergraduate Students,Virtual,Workshop ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Happening@UM.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241111T121500 DTEND;TZID=America/Detroit:20241111T131500 DTSTAMP:20260605T131533 CREATED:20240924T215158Z LAST-MODIFIED:20250107T180016Z UID:10000764-1731327300-1731330900@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars: Vishal Subramanian / Heting Fu 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. \n\nAccelerating Fock exact exchange calculations using Tucker Tensor techniques \nDensity Functional Theory (DFT) is widely used to predict the electronic structure and properties of a broad range of materials. Although exact in theory\, DFT simulations rely on exchange-correlation (Exc) functionals that are approximated in practice. The accuracy of DFT calculations is solely dependent on the accuracy of the Exc functionals. Hybrid exchange-correlation functionals are a class of functionals that have been shown to match experimental observations more closely compared to other Exc functionals. However\, the use of hybrid Exc functionals necessitates the computation of Fock exact exchange\, significantly increasing the computational cost. Furthermore\, the nature of Fock exact exchange demands a substantial increase in memory requirements and communication across processors. The latter is a serious issue as it affects the scalability of the code\, restricting routine simulations to a few tens of atoms. In this work\, we have developed a Tucker Tensor-based approach that significantly reduces the computational cost of Fock exact exchange calculations. We have incorporated an innovative communication pattern that reduces communication without significantly increasing peak memory usage. Consequently\, we have developed a robust\, efficient\, and scalable algorithm that achieves an order-of-magnitude speedup over the current state of the art. \nVishal Subramanian (Materials Science & Engineering and Scientific Computing) \nVishal Subramanian is a PhD candidate in the Materials Science and Engineering department. He is interested in harnessing the power of linear algebra and high-performance computing to develop robust\, and efficient algorithms that can compute material properties accurately. His work with Prof. Gavini’s group developing algorithms and scalable implementations for fast density functional theory (DFT) calculations on large-scale systems earned him the 2023 Gordon Bell Prize – the highest honor given in high-performance computing. \n\nTopology Optimization for Die Casting with Nonplanar Parting Surfaces \nThis talk presents a density-based topology optimization method for the simultaneous design of die-castable geometry\, die drawing directions\, and arbitrarily nonplanar parting surface. Viewing a die casted part as a two-component system consisting of the cavities of die halves\, an arbitrarily nonplanar parting surface is represented as the boundaries between adjacent partitioned domains similar to the joints in multi-component topology optimization (MTO). The draw direction of each die half is represented as a probability distribution to avoid premature convergence\, and the undercut of a part geometry in the draw direction is evaluated using the gradient of the density field. Several numerical examples are presented to demonstrate the advantages of the inclusion of nonplanar parting surfaces as optimization variables. \nHeting Fu (Mechanical Engineering and Scientific Computing) \nHeting Fu is a Ph.D. candidate under the guidance of Professor Kazuhiro Saitou in Mechanical Engineering. His research involves multi-component\, multi-material\, and multi-process topology optimization. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-6/ LOCATION:Undergraduate Science Building – 1250 CATEGORIES:Micde,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/09/Vishal-Subramanian-Heting-Fu.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241118T121500 DTEND;TZID=America/Detroit:20241118T131500 DTSTAMP:20260605T131533 CREATED:20240924T215200Z LAST-MODIFIED:20241101T183341Z UID:10000770-1731932100-1731935700@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. URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-12/ LOCATION:Undergraduate Science Building – 1250 CATEGORIES:Micde,Phd Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241202T121500 DTEND;TZID=America/Detroit:20241202T131500 DTSTAMP:20260605T131533 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 BEGIN:VEVENT DTSTART;TZID=America/Detroit:20241204T160000 DTEND;TZID=America/Detroit:20241204T170000 DTSTAMP:20260605T131533 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:20241209T121500 DTEND;TZID=America/Detroit:20241209T131500 DTSTAMP:20260605T131533 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 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250117T160000 DTEND;TZID=America/Detroit:20250117T170000 DTSTAMP:20260605T131533 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:20250121T150000 DTEND;TZID=America/Detroit:20250121T160000 DTSTAMP:20260605T131533 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:20250128T160000 DTEND;TZID=America/Detroit:20250128T170000 DTSTAMP:20260605T131533 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 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:20260605T131533 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:20250206T150000 DTEND;TZID=America/Detroit:20250206T160000 DTSTAMP:20260605T131533 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:20250211T120000 DTEND;TZID=America/Detroit:20250211T130000 DTSTAMP:20260605T131533 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:20250225T120000 DTEND;TZID=America/Detroit:20250225T130000 DTSTAMP:20260605T131533 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:20250311T120000 DTEND;TZID=America/Detroit:20250311T130000 DTSTAMP:20260605T131533 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:20250314T120000 DTEND;TZID=America/Detroit:20250314T130000 DTSTAMP:20260605T131533 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:20250318T120000 DTEND;TZID=America/Detroit:20250318T130000 DTSTAMP:20260605T131533 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:20250320T153000 DTEND;TZID=America/Detroit:20250320T163000 DTSTAMP:20260605T131533 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:20250401T120000 DTEND;TZID=America/Detroit:20250401T130000 DTSTAMP:20260605T131533 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 ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-01-Baxi-Chen.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250401T160000 DTEND;TZID=America/Detroit:20250401T170000 DTSTAMP:20260605T131533 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:20250403T160000 DTEND;TZID=America/Detroit:20250403T170000 DTSTAMP:20260605T131533 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:20250408T113000 DTEND;TZID=America/Detroit:20250408T130000 DTSTAMP:20260605T131533 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 ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-08-Bhola-Macias-Medellin-Ren-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250415T120000 DTEND;TZID=America/Detroit:20250415T133000 DTSTAMP:20260605T131533 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 ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-22-Zhang-Castor-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250422T120000 DTEND;TZID=America/Detroit:20250422T130000 DTSTAMP:20260605T131533 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 ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-22-Croft-Turner.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250515T090000 DTEND;TZID=America/Detroit:20250515T103000 DTSTAMP:20260605T131533 CREATED:20250501T205802Z LAST-MODIFIED:20250501T205802Z UID:10000820-1747299600-1747305000@micde.umich.edu 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 ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/05/MICDE-Seminar-Series-Speaker-Portraits-2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250711T120000 DTEND;TZID=America/Detroit:20250711T130000 DTSTAMP:20260605T131533 CREATED:20250709T192007Z LAST-MODIFIED:20260522T152604Z UID:10000827-1752235200-1752238800@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\nBridging Bonds and Bands: A Toolkit for Interpreting the Crystal Chemistry of Electronic Structure\nThis talk explores how we can better understand chemical bonding and electronic structure in materials using quantum mechanical calculations. I first show how specific atomic bonds shape the electronic bands of materials like silicon\, using simplified models built from density functional theory (DFT). Then\, I introduce a new method called COGITO\, which creates a clear and flexible atomic picture of the wavefunctions in DFT. COGITO builds a set of atomic orbitals that accurately capture the full electronic structure and reveal where and how electrons are shared between atoms. This makes it possible to see and measure covalent bonds\, estimate bond energies\, and even understand magnetic interactions. I demonstrate how COGITO can explain why some crystal structures are more stable than others and how different DFT functionals change bonding—giving us a powerful new tool for interpreting and designing materials. \nEmily Oliphant\, Materials Science & Engineering and Scientific Computing\nEmily Oliphant is a 5th year PhD student working with Professor Wenhao Sun and Professor Emmanouil Kioupakis. She is working to obtain atom and bonding insight in density functional theory. \n\nAn immersed boundary method formulation for aortic dissection simulation\nAortic dissection is characterized by a disruption of the intima\, leading to delamination of the aortic wall and formation of a true lumen (TL) and a false lumen (FL)\, separated by an intimal flap or septum which moves cyclically due to pressure gradients between TL and FL. Aortic dissection can lead to complications\, including end-organ malperfusion and aortic rupture. The scarcity of clinical hemodynamic data\, such as pressure and flow in the TL and FL\, complicates aortic dissection research\, driving the use of computational simulations to study its flow dynamics and flap motion. Computational simulations can be used to study the aortic dissection dynamics and their relation to pressure gradient across TL and FL. Fluid–structure-interaction (FSI) methods have been used in aortic dissection simulations to investigate the impact of the intimal flap motion on hemodynamic parameters. While the Arbitrary Lagrangian–Eulerian (ALE) approach is widely used for the aortic dissection problems\, it faces challenges: frequent fluid mesh updates increase computational costs\, and mesh quality can degrade when the flap nears the aortic wall. Immersed Boundary Methods (IBM) offer an attractive alternative\, avoiding fluid remeshing and effectively capturing the dynamics of thin structures\, as demonstrated in heart valve simulations among other applications. In this work\, we developed an IBM algorithm within a Finite Element flow solver framework using unstructured grids and computationally efficient rotation-free shell formulation to simulate aortic dissection\, providing a practical approach to study its complex flow and structural behavior in patient-specific cases \nTaeouk Kim\, Biomedical Engineering and Scientific Computing\nTaeouk is a 5th year PhD student in the Biomedical Engineering department. He is working with Dr. Alberto Figueroa at the computational vascular biomechanics lab. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-20250711/ 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/2025/07/2025-07-11.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250924T150000 DTEND;TZID=America/Detroit:20250924T170000 DTSTAMP:20260605T131533 CREATED:20250822T192306Z LAST-MODIFIED:20260522T151523Z UID:10000828-1758726000-1758733200@micde.umich.edu SUMMARY:MICDE Nobel Prize Lectures DESCRIPTION:Speakers:\n\nCharles Brooks\, Warner-Lambert/Parke-Davis Professor of Chemistry\, Cyrus Levinthal Distinguished University Professor of Chemistry and Biophysics\, will talk about the 2024 Nobel Prizes in Chemistry.\nVeera Sundararaghavan\, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory\, will talk about the 2024 Nobel Prizes in Physics.\n\nNobel Prize Lectures\nThe 2024 Nobel Prizes in Physics and Chemistry spotlight the reciprocal influence between artificial intelligence and the natural sciences. This MICDE special event examines the science and scientists recognized for foundational advances in neural networks that underpin modern machine learning (Physics)\, and for AI-enabled breakthroughs in protein structure prediction and computational protein design (Chemistry). The lectures will be followed by a moderated panel and an open\, cross-disciplinary discussion. \nPanel Discussion:\nThe panel discussion\, followed by the lectures\, will address questions such as: What can AI do for science? How can it support existing ideas and create new ones? What can science do for AI? \nPanelists:\n\nJames Wells\, Professor of Physics\, University of Michigan\nIndika Rajapakse\, Professor of Computational Medicine and Bioinformatics\, and Professor of Mathematics\, University of Michigan\nCharles Brooks\, Warner-Lambert/Parke-Davis Professor of Chemistry\, Cyrus Levinthal Distinguished University Professor of Chemistry and Biophysics\nVeera Sundararaghavan\, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory\n\nModerator:\n\nKarthik Duraisamy\, Professor of Aerospace Engineering\, Mechanical Engineering and Nuclear Engineering and Radiological Sciences and Samir and Puja Kaul Director of the Michigan Institute for Computational Discovery and Engineering URL:https://micde.umich.edu/event/nobel-prize-lecture/ LOCATION:Forum Hall\, Palmer Commons\, 100 Washtenaw Ave\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Chemistry,College Of Engineering,Computation,Computational Modeling,Computational Science,computing,Engineering,Featured Events,Free,Generative Ai,Graduate,Graduate and Professional Students,Graduate Students,Lecture,Machine Learning,Micde,Micde Seminar,Physics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/08/Stockholm-3.png GEO:42.2807039;-83.7338523 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Forum Hall Palmer Commons 100 Washtenaw Ave Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=100 Washtenaw Ave:geo:-83.7338523,42.2807039 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251002T140000 DTEND;TZID=America/Detroit:20251002T150000 DTSTAMP:20260605T131533 CREATED:20250909T223056Z LAST-MODIFIED:20251003T210107Z UID:10000830-1759413600-1759417200@micde.umich.edu SUMMARY:MICDE - MSE Seminar: Michael Herbst\, Swiss Federal Institute of Technology in Lausanne DESCRIPTION:Bio: Michael Herbst obtained a PhD in Theoretical Chemistry from Heidelberg University in 2018\, after which he moved on to two postdoctoral research stays in Applied Mathematics with Éric Cancès (École des Ponts\, France) and Benjamin Stamm (RWTH Aachen\, Germany). Since March 2023\, he has been a tenure-track assistant professor in the Institute of Mathematics and the Institute of Materials at EPFL. His current research spans broadly in the field of materials simulations concerning numerical error control and uncertainty quantification of first-principle simulations\, as well as the propagation of such errors during inverse materials design or when training machine learning models. \nAlgorithmic differentiation (AD) for plane-wave DFT\nAbstract: Reliable algorithmic differentiation techniques offer great promise for the inverse design of materials and functionals\, as well as the propagating uncertainties from functionals to DFT quantities of interest. Over the past years\, considerable effort has been spent on equipping the density-functional toolkit (DFTK\, https://dftk.org) with algorithmic differentiation capabilities. Prof. Herbst will present some of the required algorithmic developments\, e.g. to efficiently compute such DFT derivatives in numerically challenging metallic systems. Furthermore\, he will highlight the conceptual difficulties associated with applying AD to plane-wave DFT and discuss our recent results\, which demonstrate the current state of AD in DFTK for error estimation\, inverse design\, and implementing new functionality. \nRead more URL:https://micde.umich.edu/event/micde-seminar-michael-herbst/ LOCATION:1670 Bob and Betty Beyster Building\, 2260 Hayward Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Micde,Micde Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/Michael-F.-Herbst.png GEO:42.2930138;-83.716372 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1670 Bob and Betty Beyster Building 2260 Hayward Street Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=2260 Hayward Street:geo:-83.716372,42.2930138 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251007T114500 DTEND;TZID=America/Detroit:20251007T124500 DTSTAMP:20260605T131533 CREATED:20250926T143945Z LAST-MODIFIED:20251008T041229Z UID:10000833-1759837500-1759841100@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nBridging Wavefunctions and Density Functionals: Unlocking Accurate Data for Functional Development\nDensity Functional Theory (DFT) is one of the most widely used electronic structure methods in chemistry\, physics\, and materials science\, striking a balance between accuracy and computational efficiency. However\, its accuracy is fundamentally limited by the choice of the exchange-correlation (XC) functional\, which remains an approximation in all practical applications. A key shortcoming of existing functionals is their failure to reproduce critical features of the exact XC potential\, such as the asymptotic -1/r decay and the step at integer electron transitions—features essential for correctly describing ionization energies\, band gaps\, and dissociation limits. In this work\, we take a data-driven approach to improving DFT by generating XC potentials from full configuration interaction (FCI) calculations. Using a large Slater basis\, we systematically recover key features of the exact XC potential across atomic systems and analyze their behavior. Additionally\, we compute exchange-correlation energy densities via an aufbau path integral\, ensuring consistency with total XC energy values from FCI. These highly accurate DFT quantities establish a benchmark for diagnosing errors in existing functionals and guiding the development of new approximations that incorporate wavefunction-level accuracy while retaining DFT’s efficiency. \nVaibhav Khanna (Chemistry and Scientific Computing)\nVaibhav Khanna is a Ph.D. candidate in Chemistry and Scientific Computing at the University of Michigan\, where he works under the supervision of Prof. Paul Zimmerman. His research focuses on developing improved density functionals that bridge the gap between highly accurate but computationally expensive wavefunction methods and the efficiency of the popular Density Functional Theory (DFT). By incorporating wavefunction-level accuracy\, his work aims to significantly improve the predictive power of DFT\, a widely used computational method in chemistry\, physics\, and materials science. \n\nTurbulence transport and size segregation of shock-driven multiphase flows\nThe phenomena of a shock-wave interacting with a particle suspension is observed in applications such as pulse detonation engines\, volcanic eruptions\, coal dust explosions and plume-surface interactions during spacecraft landings. Compressibility effects during these interactions give rise to complicated dynamics in the suspensions. While there has been a lot of effort and progress in modeling incompressible flows\, much less work has been done in modeling the microscale physics in turbulent flows at finite Mach numbers. Particle-resolved numerical simulations of shock passing through monodisperse suspensions are used to guide the development of subgrid-scale models for turbulence transport. Turbulent kinetic energy (TKE) is found to contribute to a significant portion of the resolved kinetic energy. A two-equation model is proposed and implemented within a hyperbolic Eulerian-based two-fluid model. The model is found to be accurate across a wide range of volume fractions and Mach numbers. Additionally\, to analyse particle dispersion and segregation in bidisperse suspensions with extreme diameter size ratios\, a hybrid numerical framework is developed\, combining an immersed boundary method for large particles with Lagrangian particle tracking of small particles. \nArchana Sridhar (Aerospace Engineering and Scientific Computing)\nArchana is a 5th year PhD student in the Aerospace Engineering department. She is a MICDE Fellow working with Dr. Jesse Capecelatro. Her focus is on computational fluid dynamics of multiphase compressible flows. \n\n  URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-10-07-Khanna-Sridhar.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251028T114500 DTEND;TZID=America/Detroit:20251028T124500 DTSTAMP:20260605T131533 CREATED:20250926T143950Z LAST-MODIFIED:20251027T214532Z UID:10000837-1761651900-1761655500@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Seminar Series DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. Lunch will be served. These events are open to the public\, but we request that all who plan to attend register in advance. Planned sessions will be canceled if no one signs up to present\, and registered attendees will be notified. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAutomated removal of artifactual false positive High Frequency Oscillations in intracranial EEG\nHigh frequency oscillations (HFOs) are a promising biomarker of the epileptogenic zone. Automated HFO detectors alleviate manual labeling but false positives\, artifacts\, remain. Clinicians recognize artifacts readily while viewing the EEG at standard resolution across channels\, and observing artifacts at the times of HFO events leads to a loss of trust in the detections. In this work\, we collect a new gold standard of HFO labeling using clinician expertise\, train several machine learning algorithms\, and develop an artifact filter compatible with any HFO detector to distinguish between true and false positives. \nAshley Tan (Mechanical Engineering and Scientific Computing)\nHer research involves developing engineering tools to control epilepsy. She is currently developing machine learning methods for artifact detection of a potential biomarker and investigating the effects of electrical brain stimulation on pathological activity. \n\nEmergence of three-dimensional structures from vortex pair instabilities in shocked interfacial flows\nThe Crow instability is a vortex-line instability that leads to the three-dimensional growth of perturbations in counter-rotating vortices\, with pinch-off leading to the generation of vortex rings at late time. Classically\, two incompressible\, inviscid vortices are studied in this context; in the present work\, we use numerical simulations to demonstrate that the cores which are generated from the compressible multi-material Richtmyer-Meshkov instability are subject to the Crow instability. Thus\, the onset of the Crow instability from the Richtmyer-Meshkov-induced cores can act as a mechanism for transitioning a nominally two-dimensional Richtmyer-Meshkov flow to three dimensions. \nWilliam White (Mechanical Engineering and Scientific Computing)\nWilliam is a PhD student in the Scientific Computing and Flow Physics Lab working on high-order numerical methods for compressible interfacial flows\, as well as interfacial and vortex-line hydrodynamic instabilities. \n\n  URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-3/ LOCATION:North Quad – 2185 CATEGORIES:Astronomy,Chemical Engineering,Chemistry,College Of Engineering,Computational Science,computing,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Mechanical Engineering,Micde,Michigan Engineering,Networking,Phd Seminar,Political Science,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Seminar,Talk ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-10-28-Tan-White.png END:VEVENT END:VCALENDAR