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-WR-CALNAME:Michigan Institute for Computational Discovery and Engineering 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: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:20251020T180000 DTEND;TZID=America/Detroit:20251020T190000 DTSTAMP:20260620T224034 CREATED:20251016T140912Z LAST-MODIFIED:20251016T142421Z UID:10000843-1760983200-1760986800@micde.umich.edu SUMMARY:Graduate Fellowships for Computational Science & Engineering information session DESCRIPTION:Join MICDE for an information session on graduate fellowships in computational science and engineering. Following an overview of existing opportunities\, a panel of recent fellowship recipients will answer questions.\n\nThe event is primarily intended for juniors\, seniors\, and first-year graduate students\, but is open to all. Refreshments will be provided; a Zoom option is also available.\n\nGraduate Fellowships for Computational Science & Engineering\nMonday\, October 20 at 6pm\n3150 Dow\nRegister to attend in-person or via Zoom\n\nFellowship programs represented will include: CSGF\, NSF\, NDSEG URL:https://micde.umich.edu/event/graduate-fellowships-for-computational-science-engineering-information-session/ CATEGORIES:Funding,Talk,Undergraduate,Undergraduate Students ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/10/Fellowships-info-session-Happening.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251017T143000 DTEND;TZID=America/Detroit:20251017T160000 DTSTAMP:20260620T224034 CREATED:20250926T143946Z LAST-MODIFIED:20250926T201326Z UID:10000834-1760711400-1760716800@micde.umich.edu SUMMARY:Graduate Opportunities in Computational and Data Science Information Session DESCRIPTION:The educational programs represented will be: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to U-M graduate students with an interest in scientific computing or data science. If you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu). \nPlease register to attend URL:https://micde.umich.edu/event/workshop-seminargraduate-opportunities-in-computational-and-data-science/ LOCATION:Weiser Hall\, Room 555\, 500 Church St.\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Info Session,Sessions ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-Happening@UMich-graphic.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20251007T114500 DTEND;TZID=America/Detroit:20251007T124500 DTSTAMP:20260620T224034 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:20251002T140000 DTEND;TZID=America/Detroit:20251002T150000 DTSTAMP:20260620T224034 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:20250926T114500 DTEND;TZID=America/Detroit:20250926T131500 DTSTAMP:20260620T224034 CREATED:20251001T161907Z LAST-MODIFIED:20251001T162036Z UID:10000842-1758887100-1758892500@micde.umich.edu SUMMARY:SC2 General Meeting F25 #1 – Scientific Computing Basics DESCRIPTION:Hi everyone! \nWelcome to our first general meeting of Fall 2025. We will be hosting a workshop on scientific computing basics\, including\, but not limited to: using the terminal\, writing scripts\, using git\, and compiling programs. Free lunch will be provided from Jeursalem Garden so please RSVP here! \n– SC2 Officers URL:https://micde.umich.edu/event/sc2-general-meeting-f25-1-scientific-computing-basics/ CATEGORIES:SC2,Workshops END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250924T150000 DTEND;TZID=America/Detroit:20250924T170000 DTSTAMP:20260620T224034 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:20250711T120000 DTEND;TZID=America/Detroit:20250711T130000 DTSTAMP:20260620T224034 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:20250711T120000 DTEND;TZID=America/Detroit:20250711T130000 DTSTAMP:20260620T224034 CREATED:20250708T154831Z LAST-MODIFIED:20250808T204538Z UID:10000826-1752235200-1752238800@micde.umich.edu SUMMARY:Frontiers in Scientific Machine Learning Seminar 17: SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning DESCRIPTION:Date: June 6\, 2025\, 12pm – 1pm\nThis is a hybrid event. To join via Zoom: Meeting ID: 978 2352 7756\, Passcode: Enter last year in format YYYY\nTo join in person: 2004 Lay Auto Lab. Refreshments will be available! \nAbstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However\, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation\, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions)\, a novel RL framework that models control actions as dynamic\, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system\, our approach enables interpretability through local stability analysis\, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation\, SALSA-RL provides a powerful tool for advancing the design\, analysis\, and theoretical understanding of RL systems.\nBio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics\, geophysical modeling\, nuclear fusion\, and beyond. He is an Early Career Awardee of the Army Research Office. URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-seminar-17-salsa-rl-stability-analysis-in-the-latent-space-of-actions-for-reinforcement-learning/ LOCATION:Walter E Lay Auto Lab – 2004 CATEGORIES:Deep Learning,FSML,Interdisciplinary,Machine Learning,North Campus,Scientific Computing,Sciml END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250711T120000 DTEND;TZID=America/Detroit:20250711T130000 DTSTAMP:20260620T224034 CREATED:20250708T153951Z LAST-MODIFIED:20250708T153951Z UID:10000825-1752235200-1752238800@micde.umich.edu SUMMARY:Frontiers in Scientific Machine Learning Seminar - Romit Maulik (Penn State University): SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning DESCRIPTION:Zoom link \nBio: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics\, geophysical modeling\, nuclear fusion\, and beyond. He is an Early Career Awardee of the Army Research Office. \nSALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning\nAbstract: Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However\, real-world control systems–especially those requiring precise and reliable performance–often demand interpretability in the sense of a-priori assessments of agent behavior to identify safe or failure-prone interactions with environments. To address this limitation\, we propose SALSA-RL (Stability Analysis in the Latent Space of Actions)\, a novel RL framework that models control actions as dynamic\, time-dependent variables evolving within a latent space. By employing a pre-trained encoder-decoder and a state-dependent linear system\, our approach enables interpretability through local stability analysis\, where instantaneous growth in action-norms can be predicted before their execution. We demonstrate that SALSA-RL can be deployed in a non-invasive manner for assessing the local stability of actions from pretrained RL agents without compromising on performance across diverse benchmark environments. By enabling a more interpretable analysis of action generation\, SALSA-RL provides a powerful tool for advancing the design\, analysis\, and theoretical understanding of RL systems. URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-romit-maulik-penn-state-university/ LOCATION:2004 Lay Auto Lab CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/07/Romit-Maulik-PennState-University.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250620T120000 DTEND;TZID=America/Detroit:20250620T130000 DTSTAMP:20260620T224034 CREATED:20250619T132221Z LAST-MODIFIED:20250620T145718Z UID:10000823-1750420800-1750424400@micde.umich.edu SUMMARY:Frontiers in Scientific Machine Learning Seminar - Pan Du (University of Notre Dame): Conditional neural field latent diffusion model for generating spatiotemporal turbulence DESCRIPTION:Zoom link \nBio: Pan Du received his bachelor’s degree in Thermal Engineering from Tsinghua University and completed his master’s in Mechanical Engineering at Washington University in St. Louis. He is currently a Ph.D. candidate in Aerospace and Mechanical Engineering at the University of Notre Dame under the guidance of Prof. Jian-Xun Wang. Pan’s research spans multiple disciplines\, including scientific machine learning\, Bayesian inference\, uncertainty quantification\, geometric deep learning\, and computational fluid mechanics. \nConditional neural field latent diffusion model for generating spatiotemporal turbulence\nAbstract: Pan Du will present the CoNFiLD model\, a novel generative framework for simulating complex turbulent flows in 3D irregular domains. While traditional eddy-resolved simulations are accurate\, their high computational cost limits usability. CoNFiLD addresses this by integrating neural field encoding with latent diffusion\, enabling efficient\, probabilistic modeling of spatiotemporal dynamics. It supports a wide range of tasks—such as flow super-resolution\, sparse reconstruction\, and data restoration—via Bayesian conditional sampling\, all without retraining. Results across diverse turbulent scenarios highlight its potential for advancing data-driven turbulence modeling. URL:https://micde.umich.edu/event/frontiers-in-scientific-machine-learning-seminar-pan-du-university-of-notre-dame/ LOCATION:GG Brown Laboratory – 1642 CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/Pan-Du-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250606T120000 DTEND;TZID=America/Detroit:20250606T130000 DTSTAMP:20260620T224034 CREATED:20250602T103951Z LAST-MODIFIED:20250604T181957Z UID:10000822-1749211200-1749214800@micde.umich.edu SUMMARY:Frontiers in Scientific Machine Learning Seminar - Ashwin Renganathan (Penn State): Sample-efficient and Principled Decision-making with Expensive Stochastic Oracles DESCRIPTION:Zoom link \nBio: Ashwin Renganathan is an assistant professor of aerospace engineering at Penn State and holds a joint appointment with the Penn State Institute of Computational and Data Sciences (ICDS). He directs the Computational complex engineered Systems Design Laboratory (CSDL) at Penn State. He is broadly interested in developing novel and scalable computational techniques for surrogate modeling\, uncertainty quantification\, and numerical optimization\, with a focus on aerospace applications. He earned his Ph.D. in aerospace engineering from Georgia Tech and previously completed a postdoctoral appointment in applied mathematics at the Argonne National Laboratory. \nSample-efficient and Principled Decision-making with Expensive Stochastic Oracles\nAbstract: Modern day engineering decision-making involves one or more computer simulation oracles of an engineered system which can be queried on-demand to learn the system response to control input. Querying simulation oracles\, also called “computer experiments”\, incur a non-trivial computational cost\, which increases with the level of fidelity in the underlying models. For instance\, a realistic computational aerodynamic simulation of an aircraft can cost several thousands of CPU hours to compute—anything more than a few dozens of such simulations is prohibitive. Therefore\, a central goal of engineering decision-making is to optimally design computer experiments\, to maximize the value of information extracted at minimal computational effort.\nIn this talk\, we will address problems anchored in\, what we coin\, the “decision-making triad” which includes: surrogate modeling\, uncertainty quantification (UQ)\, and numerical optimization/control. Specifically\, using variants of a probabilistic surrogate model and a Bayesian decision theoretic framework\, we will show that problems in the decision-making triad can be solved in a principled\, theoretically sound and\, yet (computational) cost-effective manner. We will show demonstrations on applications in computational aerodynamics. URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-seminar-15-ashwin-renganathan/ LOCATION:GG Brown Laboratory – 1642 CATEGORIES:Ai In Science And Engineering,Computational Modeling,Engineering,FSML,Graduate School,Interdisciplinary,North Campus,Research,Sciml,Statistics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/06/MICDE-Seminar-Series-Speaker-Portraits-4.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250523T120000 DTEND;TZID=America/Detroit:20250523T130000 DTSTAMP:20260620T224034 CREATED:20250522T231313Z LAST-MODIFIED:20250522T231313Z UID:10000821-1748001600-1748005200@micde.umich.edu SUMMARY:FSML Lecture Series - Smita Krishnaswamy (Yale University): Dynamics Models of Cellular and Neuronal Interactions DESCRIPTION:Zoom link \nBio: Smita Krishnaswamy is an Associate Professor in the departments of Computer Science (SEAS) and Genetics (YSM). She is part of the programs in Applied Mathematics\, Computational Biology & Bioinformatics and Interdisciplinary Neuroscience. She is also affiliated with the Yale Institute for the foundations of data science\, Wu-Tsai Institute\, Yale Cancer Center. Her lab works on fundamental deep learning and machine learning developments for representing and learning from big data. Her techniques incorporate mathematical priors from graph spectral theory\, manifold learning\, signal processing\, and topology into machine learning and deep learning frameworks\, in order to denoise and model the underlying systems faithfully for predictive insight. Currently her methods are being widely used for data denoising\, visualization\, generative modeling\, dynamics. modeling\, comparative analysis and domain transfer in datasets arising from stem cell biology\, cancer\, immunology and structural biology (among others). \nPrior to joining Yale\, she completed her postdoctoral training at Columbia University in the systems biology department where she focused on learning computational models of cellular signaling from single-cell mass cytometry data. She obtained her Ph.D. from EECS department at University of Michigan where her research focused on algorithms for automated synthesis and probabilistic verification of nanoscale logic circuits. Following her time in Michigan\, she spent 2 years at IBM’s TJ Watson Research Center as a researcher in the systems division where she worked on automated bug finding and error correction in logic. Her work over the years has won several awards including the NSF CAREER Award\, Sloan Faculty Fellowship\, and Blavatnik fund for Innovation. \nDynamics Models of Cellular and Neuronal Interactions URL:https://micde.umich.edu/event/fsml-lecture-series-smita-krishnaswamy/ LOCATION:GG Brown Laboratory – 1642 CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,big data,College Of Engineering,data,FSML,Machine Learning,North Campus,Statistics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/05/MICDE-Seminar-Series-Speaker-Portraits-3.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250515T090000 DTEND;TZID=America/Detroit:20250515T103000 DTSTAMP:20260620T224034 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:20250425T120000 DTEND;TZID=America/Detroit:20250425T130000 DTSTAMP:20260620T224034 CREATED:20250421T155355Z LAST-MODIFIED:20250522T231416Z UID:10000819-1745582400-1745586000@micde.umich.edu SUMMARY:FSML Lecture Series - Julie Bessac (National Renewable Energy Laboratory): Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution DESCRIPTION:Zoom link \nBio: Julie Bessac received her Ph.D. degree in 2014 in Applied Mathematics from the University of Rennes 1\, France. Between 2014 and 2023\, she was a post-doctoral appointee and a research scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. She joined National Renewable Energy Laboratory in 2023 as a computational statistician. She is an adjunct professor at the Department of Statistics at Virginia Tech. Her research focuses on statistical and machine learning methods for modeling\, forecasting and uncertainty quantification for diverse applications: geophysical processes and their applications to energy systems\, computer science and nuclear physics. \nSummary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution\nAbstract: In the first part of this talk\, we will discuss summary statistics of physics-based model outputs and their correction with observational data. Physics-based models capture broad-scale dynamics across various spatial and temporal scales\, they often face challenges such as modeling biases\, high computational costs\, along with large outputs that are challenging to manipulate. On the other hand\, observations capture localized variability but are typically sparse. This talk presents an innovative approach to address these challenges by utilizing summary statistics from physics-based model outputs and enhancing them with observational information via neural networks.\nIn the second part of the talk\, we will present neural networks with closed-form probabilistic loss that applied to super-resolution of surface wind speed. We will illustrate that the use of a closed-form probabilistic loss provides the neural network with a sampling capability and a spatial covariance for super-resolved wind fields.\nThese are joint work with Atlanta Chakraborty (NREL)\, Harrison Goldwyn (NREL)\, Daniel Getter (USC)\, Johann Rudi (Virginia Tech) and Mitchell Krock (University of Missouri). URL:https://micde.umich.edu/event/fsml-lecture-13-julie-bessac/ LOCATION:GG Brown Laboratory – 2636 CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,big data,College Of Engineering,data,FSML,Machine Learning,North Campus,Statistics ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/04/MICDE-Seminar-Series-Speaker-Portraits-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250422T120000 DTEND;TZID=America/Detroit:20250422T130000 DTSTAMP:20260620T224034 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:20250415T120000 DTEND;TZID=America/Detroit:20250415T133000 DTSTAMP:20260620T224034 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:20250409T190000 DTEND;TZID=America/Detroit:20250409T200000 DTSTAMP:20260620T224034 CREATED:20250407T165916Z LAST-MODIFIED:20250407T165916Z UID:10000818-1744225200-1744228800@micde.umich.edu SUMMARY:The Orren C. Mohler Prize Lecture DESCRIPTION:Volker Springel\, Professor\, Max Planck Institute for Astrophysics & Ludwig Maximilian University of Munich\, Germany \n“Simulated Universes: Origin and Fate of Our Milky Way”\nGalaxies contain hundreds of billions of stars and display a wide variety of shapes and sizes. But these cosmic lighthouses are just markers for even much vaster structures lying underneath. In fact\, astrophysicists are convinced that the vast majority of the energy and matter content of the Universe does not consist of ordinary matter\, but is dominated by enigmatic dark matter and dark energy components. Supercomputer simulations play a crucial role in testing this seemingly daring cosmological hypothesis. The astonishing performance of today’s supercomputers makes it possible to link the relatively simple initial conditions left by the Big Bang directly with the complex\, developed state of the present universe and thus trace the life of galaxies in detail. They show how a cosmic network of dark matter is created over 13 billion years\, at the intersections of which structures of various sizes form\, from small dwarf galaxies to enormous galaxy clusters. The supercomputers also make predictions about the specific formation history of the Milky Way\, and how it should develop in the future. At the same time\, the simulations can also help us to explain extreme phenomena such as the effect of supermassive black holes on the cosmic evolution of galaxies. \nSponsored by the Department of Astronomy URL:https://micde.umich.edu/event/the-orren-c-mohler-prize-lecture/ LOCATION:Forum Hall\, Palmer Commons\, 100 Washtenaw Ave\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Astronomy,Computation,Computational Modeling,Computational Science,computing ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2025/04/event_133141_original-1.jpeg 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:20250408T113000 DTEND;TZID=America/Detroit:20250408T130000 DTSTAMP:20260620T224034 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:20250403T160000 DTEND;TZID=America/Detroit:20250403T170000 DTSTAMP:20260620T224034 CREATED:20250308T043518Z LAST-MODIFIED:20250310T172122Z UID:10000813-1743696000-1743699600@micde.umich.edu SUMMARY:Scientific Computing in the Biological and Health Sciences information session DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Biology\, Kinesiology\, Medicine\, Pharmacy\, Public Health\, or any other biological or health-related science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-biological-and-health-sciences/ LOCATION:Weiser Hall – 170 CATEGORIES:Basic Science,Biology,Biomedical Engineering,Biosciences,Computation,Computational Modeling,Computational Science,Computational Social Science,computing,Ecology And Evolutionary Biology,Epidemiology,Evolutionary Biology,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,In Person,Interdisciplinary,Kinesiology,Life Science,Machine Learning,Medicine,Micde,Natural Sciences,Neuroscience,Pharmacy,Prospective Graduate Students,Psychology,Public Health,Rackham,Research,Science,Scientific Computing,Virtual ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Biological-and-Health-Sciences.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250401T160000 DTEND;TZID=America/Detroit:20250401T170000 DTSTAMP:20260620T224034 CREATED:20250308T043515Z LAST-MODIFIED:20250310T172100Z UID:10000812-1743523200-1743526800@micde.umich.edu SUMMARY:Scientific Computing in the Physical Sciences information session DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Applied Physics\, Astronomy\, Biophysics\, Chemistry\, Earth and Environmental Sciences\, Math\, Physics\, or any other physical science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-physical-sciences/ LOCATION:Weiser Hall – 170 CATEGORIES:Applied Physics,Astronomy,Biophysics,Chemistry,Computation,Computational Modeling,Computational Science,computing,Earth And Environmental Sciences,Environment,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Life Science,Machine Learning,Mathematics,Micde,Natural Sciences,Physics,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Virtual ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/2025-04-Scientific-Computing-in-the-Physical-Sciences.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250401T120000 DTEND;TZID=America/Detroit:20250401T130000 DTSTAMP:20260620T224035 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:20250328T120000 DTEND;TZID=America/Detroit:20250328T130000 DTSTAMP:20260620T224035 CREATED:20250324T152136Z LAST-MODIFIED:20250324T152136Z UID:10000816-1743163200-1743166800@micde.umich.edu SUMMARY:FSML Lecture Series - Lianghao Cao (Caltech): Derivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion DESCRIPTION:Zoom link \nBio: Dr. Lianghao Cao is a Postdoctoral Scholar Research Associate from the Department of Computing and Mathematical Sciences at the California Institute of Technology. He obtained a B.S. in Engineering Mechanics from the University of Illinois at Urbana-Champaign and a Ph.D. in Computational Science\, Engineering\, and Mathematics from The University of Texas at Austin. His research blends mechanistic modeling\, uncertainty quantification\, and scientific machine learning to understand\, enhance\, and control the quality\, validity\, and reliability of simulation-based predictions of complex physical systems. \nDerivative-Informed Operator Learning with Applications to Cost-Efficient Bayesian Inversion\nAbstract: This talk focuses on a derivative-informed supervised learning method for efficiently building machine learning surrogates of high-fidelity computational models\, particularly those governed by parametric partial differential equations. Unlike the conventional supervised learning method that treats the model as a black box\, our approach leverages additional model sensitivity information\, extracted via solving forward or adjoint sensitivity equations. This sensitivity information is integrated into the surrogate’s architecture and training process based on rigorous error analysis. We refer to such a surrogate construction as DINO (derivative-informed neural operator). \nDINO offers two key advantages over conventional surrogate construction. First\, it significantly improves the cost-accuracy trade-off for a wide range of models\, often by one to two orders of magnitude. Second\, it directly controls the surrogate Jacobian (Fréchet derivative) errors\, thus enhancing performance in surrogate-driven outer-loop problems that use gradient- and Hessian-based optimization algorithms. We demonstrate DINO’s capability to accelerate infinite-dimensional Bayesian inversion. First\, we show that geometric MCMC driven by DINO achieves a 2–9x speed up in asymptotically exact posterior sampling. Second\, we introduce LazyDINO\, a DINO-driven measure transport method for amortized Bayesian inversion\, which is one to two orders of magnitude more cost-efficient than competing methods.\nThis talk is based on joint work with Michael Brennan\, Joshua Chen\, Omar Ghattas\, Youssef Marzouk\, and Thomas O’Leary-Roseberry. URL:https://micde.umich.edu/event/fsml-lecture-series-lianghao-cao-caltech-derivative-informed-operator-learning-with-applications-to-cost-efficient-bayesian-inversion/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Lianghao-Cao-Caltech.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250320T153000 DTEND;TZID=America/Detroit:20250320T163000 DTSTAMP:20260620T224035 CREATED:20241011T222200Z LAST-MODIFIED:20250120T170935Z UID:10000782-1742484600-1742488200@micde.umich.edu SUMMARY:MICDE-EECS Seminar: Mikhail Belkin\, Professor\, University of California San Diego DESCRIPTION:Bio: Mikhail Belkin is a Professor at Halicioglu Data Science Institute and Computer Science and Engineering Department at UCSD and an Amazon Scholar. Prior to that he was a Professor at the Department of Computer Science and Engineering and the Department of Statistics at the Ohio State University. He received his Ph.D. from the Department of Mathematics at the University of Chicago (advised by Partha Niyogi). His research interests are broadly in theory and applications of machine learning\, deep learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps\, Graph Regularization and Manifold Regularization algorithms\, which brought ideas from classical differential geometry and spectral graph theory to data science. His more recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. The empirical evidence necessitated revisiting some of the classical concepts in statistics and optimization\, including the basic notion of over-fitting. One of his key findings has been the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. His recent work focusses on understanding feature learning and over-parameterization in deep learning. Mikhail Belkin is an ACM Fellow and a recipient of a NSF Career Award and a number of best paper and other awards. He had served on the editorial boards of IEEE Proceedings on Pattern Analysis Machine Intelligence and the Journal of the Machine Learning Research. He is the editor-in-chief of SIAM Journal on Mathematics of Data Science (SIMODS). \nEmergence and grokking in “simple” architectures\nAbstract: In recent years\, transformers have become a dominant machine learning methodology.\nA key element of transformer architectures is a standard neural network (MLP). I argue that MLPs alone already exhibit many remarkable behaviors observed in modern LLMs\, including emergent phenomena. Furthermore\, despite large amounts of work\, we are still far from understanding how 2-layer MLPs learn relatively simple problems\, such as “grokking” modular arithmetic. I will discuss recent progress and argue that feature-learning kernel machines (Recursive Feature Machines) isolate some key computational aspects of modern neural architectures and are preferable to MLPs as a model for analysis of emergent phenomena. URL:https://micde.umich.edu/event/mikhail-belkin/ LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Micde,Micde Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Mikhail-Belkin-2.png GEO:42.292322;-83.713272 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250318T120000 DTEND;TZID=America/Detroit:20250318T130000 DTSTAMP:20260620T224035 CREATED:20250114T142206Z LAST-MODIFIED:20250228T184043Z UID:10000794-1742299200-1742302800@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n  \n\nSolving High Reynolds Number Flows on Cartesian Cut-cell Meshes using a Jacobian-Free Newton–Krylov Method\nIn this work\, we developed a Newton–Krylov method for a second-order Cartesian cut cell Reynolds-averaged Navier–Stokes (RANS) solver\, Viscous Aerodynamic Cartesian Cut cells (VACC)\, with the one equation Spalart–Allmaras (SA) turbulence model. The Newton–Krylov method uses pseudo-transient continuation and a point Jacobi preconditioner to accelerate convergence. Then various wall functions were compared on a finite flat plate and 2D bump cases. The SA analytical wall function was used as a baseline. An ordinary differential equation (ODE) wall function and wall-modeled RANS (WMRANS) approach were also implemented. Although these methods all showed promise\, the interior viscous fluxes resulted in oscillatory pressures. These oscillations degraded the accuracy of all of the solutions. \nAlex Kleb\, Aerospace Engineering\nAlex Kleb is a fifth year PhD candidate in the CFD Group and MDO lab in the Aerospace Engineering department. \n\nGeometrically Nonlinear Methods for High-Fidelity MDO of Very-Flexible Aircraft\nOver the past decade\, advances in Multidisciplinary Design Optimization (MDO) have enabled the optimization of aircraft wings using high-fidelity simulations of their coupled aerodynamic and structural behavior. Using RANS CFD and detailed structural finite element wingbox models\, the aerodynamic shape and internal structural sizing of a wing can be optimized concurrently to tailor the wing’s aeroelastic behavior and optimally trade-off drag and structural mass. This capability makes MDO a key enabling technology for the next generation of efficient high-aspect-ratio transport aircraft. However\, as their aspect-ratios increase\, these wings increasingly exhibit geometrically nonlinear behavior that cannot be correctly modeled by typical linear structural analysis methods. This work demonstrates the first simultaneous optimization of a wing’s aerodynamic shape and structural sizing using high-fidelity geometrically nonlinear models. Our methods are implemented in the open-source finite element library\, TACS\, and include a geometrically nonlinear shell element formulation\, an efficient and robust nonlinear solver\, and a constitutive model for stiffened shells. We demonstrate the ability to couple these nonlinear structural analysis tools to a high-fidelity RANS CFD solver using a geometrically nonlinear load and displacement transfer scheme. Finally\, we use this capability to optimize a single-aisle commercial transport aircraft wing featuring 547 design variables and 1277 constraints. \nAlasdair Christison Gray\, Aerospace Engineering\nAlasdair Christison Gray is a 5th year PhD student in the Aerospace Engineering department’s MDO Lab. His research focuses on applying high performance computing to the large scale design optimization of aircraft wings. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-3-18-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-03-18-Kleb-Christison-Gray.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250314T120000 DTEND;TZID=America/Detroit:20250314T130000 DTSTAMP:20260620T224035 CREATED:20250311T164812Z LAST-MODIFIED:20250311T164812Z UID:10000815-1741953600-1741957200@micde.umich.edu SUMMARY:Workshop / Seminar:Frontiers in Scientific Machine Learning (FSML) Seminar: Alexander Tong (Post-doctoral Fellow\, Mila - Quebec AI Institute) DESCRIPTION:Abstract:\nGenerative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery.\nBio:\nAlexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins. URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-fsml-seminar-alexander-tong-post-doctoral-fellow-mila-quebec-ai-institute/ LOCATION:GG Brown Laboratory – 1642 CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,Micde END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250314T120000 DTEND;TZID=America/Detroit:20250314T130000 DTSTAMP:20260620T224035 CREATED:20250311T130516Z LAST-MODIFIED:20250311T130831Z UID:10000814-1741953600-1741957200@micde.umich.edu SUMMARY:FSML Lecture Series - Alexander Tong (Mila - Quebec AI Institute): Flow matching in cell trajectories and protein design DESCRIPTION:Zoom link \nBio: Alexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins. \nFlow matching in cell trajectories and protein design\nAbstract: Generative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery. URL:https://micde.umich.edu/event/fsml-lecture-series-alexander-tong-mila/ LOCATION:1642 GGBL\, 2350 HAYWARD ST\, Ann Arbor\, 48109\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/03/Alexander-Tong.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250311T120000 DTEND;TZID=America/Detroit:20250311T130000 DTSTAMP:20260620T224035 CREATED:20250225T214118Z LAST-MODIFIED:20250225T214448Z UID:10000810-1741694400-1741698000@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nUnraveling Rotator Cuff Tendon Tear Growth Mechanisms with Full-Volume Strains and Data-Driven Modeling\nIn this talk\, I will show how full-volume methods\, which can probe internal locations of a material\, enable the detection of regions with high shear strain concentration in intact and torn rotator cuff tendons. I will also explain my approach to use these full-volume datasets to develop a finite element model of this tendon using variational system identification\, and future work to obtain validated computational models that can predict tear growth. \nNathaly Villacis\, Mechanical Engineering and Scientific Computing\nNathaly is a fifth year Ph.D. candidate in Mechanical Engineering\, who works at the Soft Tissue Mechanics Lab\, supervised by Dr. Ellen Arruda. Her research involves the characterization of rotator cuff tendon tear growth with experimental and computational methods. She will work on machine learning models of the rotator cuff once she finishes her Ph.D. \n\nIncremental Tensor Decompositions for Machine Learning and Bayesian Inference\nWith recent advancements in large-scale parallel computing\, there is an increased interest in constructing high-fidelity digital twins of complex systems. Especially for systems that have limited physical experimentation possibilities\, high-fidelity simulations may provide the main source of information for constructing digital twins. However\, performing such simulations is computationally intensive and generates extreme amounts of data. The size of the generated simulation data makes it challenging to use the data in further analysis. As the spatial and temporal resolution of these simulations grow\, even storing the data may become a serious bottleneck. This talk proposes a solution to this multi-faceted problem through the use of low-rank tensor decompositions. Specifically\, we present incremental algorithms that provide computationally efficient ways of compressing data with accuracy guarantees. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques. \nDoruk Aksoy\, Aerospace Engineering and Scientific Computing\nDoruk is a 5th year Ph.D. candidate in the Department of Aerospace Engineering in the Computational Autonomy group. His research focuses on developing incremental low-rank tensor decomposition algorithms to compress large-scale data for downstream machine learning applications. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-20250311/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/2025-03-11-Villacis-Aksoy.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250228T120000 DTEND;TZID=America/Detroit:20250228T130000 DTSTAMP:20260620T224035 CREATED:20250225T171723Z LAST-MODIFIED:20250227T225237Z UID:10000809-1740744000-1740747600@micde.umich.edu SUMMARY:FSML Lecture Series - Doruk Aksoy: From Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition DESCRIPTION:Zoom link \nBio:Doruk Aksoy is a 5th year PhD candidate in Aerospace Engineering and Scientific computing at the University of Michigan\, working under the supervision of Prof. Alex Gorodetsky. Prior to joining UM\, he studied Mechanical Engineering at Bogazici University in Istanbul Turkey. During his PhD\, he worked on developing incremental tensor decomposition algorithms to accelerate scientific machine learning through data reduction. \nFrom Turbulent Flows to Video Games: Managing Large-Scale Data with Tensor Decomposition\nAbstract: The rapid advancement of large-scale parallel computing created a surge of interest in developing high-fidelity digital twins for complex systems. However\, the computational demands for training these models are immense\, requiring vast amounts of data. As the spatial and temporal resolution of simulations increases\, even data storage becomes a critical bottleneck. This talk presents how low-rank tensor decomposition methods can be used to exploit the structure in large-scale data. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques. URL:https://micde.umich.edu/event/fsml-lecture-series-doruk-aksoy/ LOCATION:2636 GGBA\, 2350 Hayward St\, Ann Arbor\, MI\, United States CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/09/Aksoy-Doruk.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250225T120000 DTEND;TZID=America/Detroit:20250225T130000 DTSTAMP:20260620T224035 CREATED:20250114T142545Z LAST-MODIFIED:20250221T144301Z UID:10000793-1740484800-1740488400@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nNumerical simulation of the collapse of a cavitation bubble near a deformable solid surface\nThe exact mechanisms leading to the permanent deformation of solid surfaces\, following a cavitation event\, are still unclear. Specifically\, the relationship between the characteristics of a given cavitation bubble and the shape of the resulting pit is unknown. In this study\, we numerically investigate the collapse of a single cavitation bubble near a solid surface\, with the objective of characterizing how the pit shape (height and depth) changes with the bubble initial radius\, its distance from the solid and the initial pressure difference at the bubble interface. To this end\, we implement a diffuse interface method for the interaction of multiple compressible fluids and hyperelastic solids in an Eulerian frame of reference. This method numerically solves the evolution equations of mass\, momentum\, energy as well as volume fractions of each material and of the mixture. The model is closed by splitting the internal energy of each material into hydrodynamic and elastic contributions\, with appropriate equations of state. A set of evolution equations of local cobasis\, with a plastic source term\, are used to compute the elastic Finger tensor\, which is needed to obtain the elastic energy and the deviatoric stress. We additionally provide improvements to the numerical method to preserve interface conditions. The proposed method allows to elucidate some of the mechanisms of cavitation pitting. \nBaudouin Fonkwa Kamga\, Mechanical Engineering and Scientific Computing\nBaudouin is a 4th year PhD student in the department of Mechanical Engineering\, under the supervision of Eric Johnsen. His research combines the theoretical study of cavitation in viscoelastic medium and the development of numerical methods for multimaterial compressible flows. \n\nScalable foundation model training for computational pathology\nScalable and efficient foundation model training is critical for advancing computational pathology. In this talk\, we present a two-stage self-supervised pipeline for whole slide image (WSI) analysis. First\, HiDisc leverages the inherent patient–slide–patch hierarchy to learn robust visual representations efficiently without relying on heavy data augmentation\, outperforming existing methods in cancer diagnosis and genetic mutation prediction. Building on these high-quality patch-level features\, our second stage\, Slide Pre-trained Transformers (SPT)\, treats WSI patches as tokens and integrates data transformation strategies from both language and vision models to capture the rich morphological diversity of gigapixel images. Together\, these methods offer a scalable\, efficient framework for training foundation models that drive robust performance across a range of diagnostic tasks. \nXinhai Hou\, Bioinformatics and Scientific Computing\nXinhai Hou is a PhD candidate in the department of computational medicine and bioinformatics. His research focuses on self-supervised learning\, computer vision\, and multimodal machine learning\, with a particular emphasis on real-world applications such as AI in healthcare and medicine. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-2-25-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-25-Fonkwa-Kamga-Hou-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250214T093000 DTEND;TZID=America/Detroit:20250214T103000 DTSTAMP:20260620T224035 CREATED:20250212T173700Z LAST-MODIFIED:20250212T173805Z UID:10000808-1739525400-1739529000@micde.umich.edu SUMMARY:FSML Lecture Series - Ricardo Vinuesa: Identifying coherent structures and controlling turbulent flows through deep learning DESCRIPTION:Zoom link \nBio: Dr. Ricardo Vinuesa is joining the Department of Aerospace Engineering at the University of Michigan in the Fall of 2025. He is currently an Associate Professor at the Department of Engineering Mechanics\, KTH Royal Institute of Technology in Stockholm. He studied Mechanical Engineering at the Polytechnic University of Valencia (Spain)\, and he received his PhD in Mechanical and Aerospace Engineering from the Illinois Institute of Technology in Chicago. His research combines numerical simulations and data-driven methods to understand\, control and predict complex wall-bounded turbulent flows\, such as the boundary layers developing around wings and urban environments. Dr. Vinuesa has received\, among others\, an ERC Consolidator Grant\, the TSFP Kasagi Award\, the MST Emerging Leaders Award\, the Goran Gustafsson Award for Young Researchers\, the IIT Outstanding Young Alumnus Award\, the SARES Young Researcher Award and he leads several large Horizon Europe projects. He is also a member of the Young Academy of Science of Spain. \nIdentifying coherent structures and controlling turbulent flows through deep learning\nAbstract: In this work we first use explainable deep learning based on Shapley explanations to identify the most important regions for predicting the future states of a turbulent channel flow. The explainability framework (based on gradient SHAP) is applied to each grid point in the domain\, and through percolation analysis we identify coherent flow regions of high importance. These regions have around 70% overlap with the intense Reynolds-stress (Q) events in two-dimensional vertical planes. Interestingly\, these importance-based structures have high overlap with classical turbulence structures (Q events\, streaks and vortex clusters) in different wall-normal locations\, suggesting that this new framework provides a more comprehensive way to study turbulence. We also discuss the application of deep reinforcement learning (DRL) to discover active-flow-control strategies for turbulent flows\, including turbulent channels\, three-dimensional cylinders and turbulent separation bubbles. In all the cases\, the discovered DRL-based strategies significantly outperform classical flow-control approaches. We conclude that DRL has tremendous potential for drag reduction in a wide range of complex turbulent-flow configurations. \n  URL:https://micde.umich.edu/event/fsml-lecture-series-ricardo-vinuesa-identifying-coherent-structures-and-controlling-turbulent-flows-through-deep-learning/ LOCATION:2210 Lurie Engineering Center\, 1221 Beal Ave\, Ann Arbor\, MI\, 48105 CATEGORIES:Engineering,FSML,Science ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/Ricardo-Vinuesa.png END:VEVENT END:VCALENDAR