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DTSTART;TZID=America/Detroit:20260225T120000
DTEND;TZID=America/Detroit:20260225T130000
DTSTAMP:20260620T205015
CREATED:20260116T194936Z
LAST-MODIFIED:20260130T183451Z
UID:10000848-1772020800-1772024400@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. 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\nEstimating potential-dependent physicochemical properties at metal–electrolyte interfaces using machine learning interatomic potentials\nMetal–electrolyte interfaces play a central role in electrocatalysis\, energy storage\, and environmental remediation. Understanding the structure and properties of these interfaces is therefore essential to designing efficient electrochemical systems. Density functional theory (DFT)-based molecular dynamics (MD) can accurately capture interfacial structure but is restricted to short timescales and small system sizes. To overcome these limitations\, we develop machine learning interatomic potentials (MLIPs) using the MACE architecture within an active learning workflow to model aqueous NaCl electrolytes in contact with Au\, Cu\, and Rh (111) electrodes. The resulting committee of MLIPs achieves DFT-level accuracy across 21 electrolyte–metal systems spanning a wide range of surface charge densities. MACE–MD simulations reproduce key interfacial properties obtained from ab initio MD\, including water density profiles\, water orientation\, and chemisorbed water coverage. \nOur simulations reveal a universal trend across all metals: the total coverage of water and ions decreases with increasing surface charge density or potential\, reaches a minimum at or slightly below the pzc\, and increases thereafter. Overall\, this work demonstrates that MLIPs based on the MACE architecture enable long-timescale\, first-principles-accurate simulations of metal electrolyte interfaces and provide detailed mechanistic insight into their potential-dependent physicochemical properties. \nAnkit Mathanker (Chemical Engineering and Scientific Computing)\nAnkit Mathanker is a Ph.D. researcher in Chemical Engineering in the Goldsmith Lab. His work leverages DFT\, AIMD\, and machine-learning interatomic potentials to understand and predict electrochemical interfacial phenomena relevant to catalysis and energy conversion. \n\nPredictive Modeling and Inverse Design of High-Entropy Semiconductor Alloys\nThe vast compositional space of high-entropy semiconductors offers unprecedented tunability but presents a significant challenge for traditional screening methods. This talk outlines a multi-tiered computational strategy designed to navigate this complexity\, applied specifically to ferroelectric high-entropy III-nitrides (AlGaInScY-N). We detail a comprehensive workflow that begins with high-throughput first-principles calculations to generate accurate stability and property datasets. We then demonstrate how this data fuels a dual-pronged AI approach\, which uses generative machine learning (symbolic regression) to discover interpretable governing equations for phase stability\, and black-box machine learning models to rapidly predict structural properties and band gaps beyond the training set. This synergistic framework not only accelerates materials discovery but also reveals the physical descriptors driving entropy stabilization and ferroelectric performance. \nYujie Liu (Materials Science and Engineering and Scientific Computing)\nYujie is a Ph.D. student from materials science and engineering. He is working on semiconductor materials design\, combineing high-throughput first-principles workflows with surrogate machine-learning models. \n\nRapid 3D Localization of Cavitation Events for Histotripsy Monitoring\nHistotripsy is a noninvasive ultrasound therapy that relies on controlled cavitation to mechanically fractionate tissue\, but accurately localizing cavitation events in real time remains a challenge\, particularly in the presence of acoustic aberrations and attenutation. This talk presents computational and experimental methods for rapid three-dimensional localization of inertial cavitation events using a large-aperture\, receive-capable focused ultrasound array. By combining narrowband signal processing with passive acoustic mapping techniques\, these methods enable high-accuracy cavitation localization at clinically relevant treatment rates. Experimental validation using optical imaging and rib-mimicking phantoms demonstrates the potential of these approaches for treatment monitoring and feedback control in therapeutic ultrasound. \nMikey Komaiha (Biomedical Engineering and Scientific Computing)\nMikey is a Ph.D. candidate in the Department of Biomedical Engineering at the University of Michigan and a member of the Histosonics research group. His research focuses on computational signal processing and experimental methods for cavitation localization and monitoring in therapeutic ultrasound applications. \n\nRegister to attend
URL:https://micde.umich.edu/event/workshop-seminar2025-2026-micde-ph-d-in-scientific-computing-student-seminars-3/
LOCATION:Room 4425\, Green Court Building
CATEGORIES:Aerospace Engineering,Chemical Engineering,Chemistry,Civil and Environmental Engineering,College Of Engineering,Computation,Computational Medicine,Computational Modeling,Computational Science,Computational Social Science,Data Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Behavior & Health Equity,In Person,Interdisciplinary,Machine Learning,Materials Science,Micde,Phd Seminar,Political Science,Prospective Graduate Students,Public Health,Research,Science,Scientific Computing,Sessions
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20260218T120000
DTEND;TZID=America/Detroit:20260218T130000
DTSTAMP:20260620T205015
CREATED:20260116T194934Z
LAST-MODIFIED:20260128T220234Z
UID:10000847-1771416000-1771419600@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. 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\nHidden Relics: The Past and Present Lives of Satellites Around MW-Mass Galaxies\nMergers are one of the most important drivers of galaxy evolution\, as present-day galaxies have been built up over time through hierarchical evolution. The main bodies of galaxies have a diversity of structural properties that can be highly influenced by mergers; unfortunately\, the response of a galaxy to a merger largely erases observational markers that allow us to infer the characteristics of the merger. But simulations have shown that material deposited into a galaxy through merger is retained by its stellar halo\, thereby leaving a “fossil record” we can trace. My PhD thesis takes a multi-faceted approach to uncover this historical record and learn what processes govern how galaxies form and evolve\, from massive Milky Way-like galaxies to their small\, ultra-faint companions. I have harnessed the power of resolved-star photometry and spectroscopy to 1) create the deepest stellar halo map of the nearby galaxy M94\, revealing that it underwent one of the quietest merger histories among galaxies of similar stellar mass\, 2) illuminate the structural diversity of faint satellite galaxies around M81\, improving ground-based characterizations and finding one of the most concentrated satellites we know of\, and 3) make the first-ever measurement of the kinematics of NGC 253’s stellar halo\, finding that it has slight prograde bulk motion and pioneering fiber-fed spectroscopy in a low S/N regime. With the techniques I have developed\, I am laying the foundation for doing resolved stellar population science with next-generation observing facilities such as the Rubin Observatory\, Roman Space Telescope\, and the ELT. \nKatya Gozman (Astronomy and Scientific Computing)\nKatya is a 6th year PhD student in the Astronomy Department working with Prof. Eric Bell. She uses ground- and space-based observations of resolved stars in the outskirts of nearby galaxies to understand their merger histories and satellite populations. \n\nFracture Criterion for Ultra-Low Cycle Fatigue Based on Measured Void Characteristics\nCommonly used ultra-low cycle fatigue (ULCF) fracture models rely on idealized void shapes and sizes. However\, the void shapes generated by real fracture processes are irregular\, forming non-uniform half-dimples and voids on the fracture surface. Therefore\, a gap remains in validating the link between simulated void behavior and fracture initiation with actual fracture surface data. To address this\, monotonic tensile and ULCF tests were performed on axisymmetric circumferential tensile (CNT) specimens with medium to high stress triaxiality\, and dimple-voids were examined using scanning electron microscope (SEM) fractographs. For the first time\, a correlation between simulated and actual void formation under ULCF was established\, leading to a new fracture criterion based on measured void features. \nMin-Chun Han (Civil and Environmental Engineering and Scientific Computing)\nMin-Chun is a Ph.D. candidate in Civil and Environmental Engineering. Her research focuses on the behavior of structures and structural materials under extreme loading and environmental conditions. \n\nA Holistic Performance-Based Framework for Assessing Coupled Building Envelope–Structural System Performance under Extreme Winds\nHigh-rise building envelopes are vulnerable to extreme winds\, requiring robust performance assessment to ensure safety. Existing models often assume linear or simplified post-elastic structural behavior\, overlooking strong nonlinearities that can arise near collapse. This study presents a performance-based computational framework combining high-fidelity nonlinear structural modeling with a progressive damage model for envelope assessment. Localized damage mechanisms\, including yielding\, buckling\, low-cycle fatigue\, and fracture\, are simulated\, and envelope vulnerability is quantified via component-level sequential fragility functions. Dynamic wind pressures are captured using wind tunnel-informed stochastic models\, while internal pressures at damage-induced openings are estimated via Bernoulli’s equation and mass conservation. A case study of a 45-story reinforced concrete building in New York City providing insights into the global probabilistic performance and the local coupled progression of envelope and structural damage under extreme wind events. \nJieling Jiang (Civil and Environmental Engineering and Scientific Computing)\nShe is currently a phd candidate in the civil engineering department\, working on developing next-generation probabilistic modeling frameworks for high-rise building systems under extreme natural hazards. Her research involves high fidelity simulation and stochastic simulation methods.  \n\n  \nRegister to attend
URL:https://micde.umich.edu/event/phd-seminar-20260218/
LOCATION:Room 4425\, Green Court Building
CATEGORIES:Aerospace Engineering,Chemical Engineering,Chemistry,Civil and Environmental Engineering,College Of Engineering,Computation,Computational Medicine,Computational Modeling,Computational Science,Computational Social Science,Data Science,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Behavior & Health Equity,In Person,Interdisciplinary,Machine Learning,Materials Science,Micde,Phd Seminar,Political Science,Prospective Graduate Students,Public Health,Research,Science,Scientific Computing,Sessions
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20260210T150000
DTEND;TZID=America/Detroit:20260210T160000
DTSTAMP:20260620T205015
CREATED:20260127T154702Z
LAST-MODIFIED:20260128T143051Z
UID:10000858-1770735600-1770739200@micde.umich.edu
SUMMARY:MICDE - NERS - MIPSE Joint Seminar: Brian Haines\, Los Alamos National Laboratory
DESCRIPTION:Bio: Brian M. Haines is a Senior Distinguished Scientist in the Eulerian Codes group in the X-Computational Physics division at Los Alamos National Laboratory. He is currently the lead for the Ignition Applications project\, which includes the THOR and BrassOwl experimental campaigns on the National Ignition Facility. Brian leads the effort to produce LANL xRAGE pre-shot predictions and post-shot analysis of high-yield implosion attempts on the National Ignition Facility. Brian led the decadal effort to develop the xRAGE radiation-hydrodynamics code into a state-of-the-art tool for modeling inertial confinement fusion (ICF) and high-energy density physics experiments and has pioneered the use of xRAGE to perform large-scale high-resolution full-physics three-dimensional simulations of ICF implosions to understand the impacts of hydrodynamic instabilities and engineering features. Prior to his current position\, Brian was a Metropolis postdoc in the Methods & Algorithms group from 2011-2013 and did various internships as a student with Argonne National Laboratory\, LANL\, the National Security Agency\, and the Institute for Defense Analyses’ Center for Communications Research. Brian received a Ph.D. in mathematics from Penn State University in 2011 and a B.A. in mathematics and physics from New York University in 2006. Brian has co-authored 100 peer-reviewed publications that have received over 3\,400 citations and has been awarded a Secretary’s Honor Award from DOE\, four distinguished performance awards from LANL\, five defense program awards of excellence from NNSA\, an ICF program award from Lawrence Livermore National Laboratory (LLNL)\, and a Director’s Science and Technology Award from LLNL. \n  \nRadiation-hydrodynamics Modeling & Application to Prediction of Inertial Confinement Fusion Experiments\nThe xRAGE radiation-hydrodynamics code is a state-of-the art simulation tool for modeling inertial confinement fusion experiments. xRAGE is one of only three radiation-hydrodynamics codes developed in the U.S. with sufficient physics to credibly model both capsule implosions as well as the high-Z cylindrical hohlraums used to convert laser energy into an X-ray drive for the capsule. xRAGE solves the equations for hydrodynamics and other physics in an Eulerian reference frame and features adaptive mesh refinement\, which makes it uniquely well-suited to accurately modeling capsule defects and engineering features that are important factors limiting capsule performance. In the first half of this talk\, we will discuss the physics modeling capabilities and algorithms available in xRAGE with an emphasis on those relevant to high-energy-density physics and inertial confinement fusion. In the second half of the talk\, we will discuss the successful application of xRAGE to provide pre-shot predictions for seventeen high-yield capsule implosions on the National Ignition Facility. This will include the modeling methodology\, how we establish prediction uncertainties\, and how we have learned from prediction failures to improve the methodology. Our predictions have exhibited a 67% success rate thus far\, which is much higher than other pre-shot predictions over the same set of experiments. \n  \n\n  \nThe MICDE 2025-26 Seminar Series is open to all. \nThis seminar is organized by the Michigan Institute for Computational Discovery & Engineering (MICDE)\, the Department of Nuclear Engineering & Radiological Sciences (NERS) and the Michigan Institute for Plasma Science and Engineering (MIPSE). \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/brian-haines-los-alamos-national-laboratory/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
CATEGORIES:College Of Engineering,Featured Events,Micde,Micde Seminar,MICDE Seminar Series,Nuclear Engineering and Radiological Sciences,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20260129T140000
DTEND;TZID=America/Detroit:20260129T150000
DTSTAMP:20260620T205015
CREATED:20251125T210910Z
LAST-MODIFIED:20260522T151806Z
UID:10000844-1769695200-1769698800@micde.umich.edu
SUMMARY:MICDE - Mechanical Engineering Seminar - Elif Ertekin\, University of Illinois Urbana-Champaign
DESCRIPTION:Bio: Elif Ertekin is an Andersen Faculty Scholar\, Associate Professor\, and Associate Head for Graduate Programs in the Mechanical Science and Engineering Department at the University of Illinois at Urbana-Champaign. She is a faculty affiliate of the National Center for Supercomputing Applications (NCSA) and the Materials Research Laboratory (MRL). Her research interests center on the theory and modeling of materials\, with an emphasis on probabilistic and stochastic methods. She focuses on developing a microscopic understanding of atomic and electronic scale processes in materials\, with applications areas in thermal transport\, energy conversion\, and defect chemistry. She received BS degrees in Mathematics and in Engineering Science and Mechanics from Penn State\, a PhD in Materials Science and Engineering from UC Berkeley\, and she carried out post-doctoral work at the Berkeley Nanoscience and Nanoengineering Institute and the Massachusetts Institute of Technology. She is an Associate Editor for the Journal of Applied Physics and a Divisional Associate Editor for\nPhysical Review Letters. \nPhysical Mechanisms or Learned Patterns? Reconciling First-Principles Models with Machine Learning for Predictive Materials\nPredictive materials simulation has long been rooted in first-principles descriptions of physical mechanisms\, grounded in quantum mechanics but limited by tractable length scales\, sampling challenges\, and the accuracy-cost tradeoff. Today\, machine-learning methods seek to transform materials science by revealing patterns in data extending beyond conventional modeling. My talk will explore how these two paradigms\, mechanistic simulation and data-driven learning\, can act synergistically to accelerate materials discovery and understanding. I will begin by outlining what first-principles simulations can currently achieve and where their limitations arise\, using examples from our work in thermoelectrics\, wide-band-gap semiconductors\, ion-transport materials\, and structural alloys. Building on this foundation\, I will show how machine-learning approaches\, when designed with materials-specific considerations such as symmetries and invariances\, can enhance traditional methods. Examples include symmetry-aware generative models for inorganic crystalline solids and machine-learning solutions to the many-body electronic-structure problem that rival high-accuracy quantum methods. Together\, these examples highlight how integrating mechanisms and patterns can help advance predictive materials simulations.\ \n\nThe MICDE 2025-26 Seminar Series is open to all. \nThis seminar is organized by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Mechanical Engineering. Prof. Ertekin will be hosted by Prof. Chenhui Shao\, Associate Professor of Mechanical Engineering. \nThis is an in-person event. This seminar will not be recorded! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-elif-ertekin-uiuc/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
CATEGORIES:College Of Engineering,Featured Events,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251209T114500
DTEND;TZID=America/Detroit:20251209T124500
DTSTAMP:20260620T205015
CREATED:20250926T143954Z
LAST-MODIFIED:20251208T171351Z
UID:10000841-1765280700-1765284300@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  \n\nImproving Slater Orbital Integration Accuracy through Prolate Spheroidal Coordinates\nThe core of electronic structure calculations is the integration of forces exerted on and by\nelectrons and nuclei in a system. Some of these interactions have forms which manifest in such a way that makes integration challenging depending on the choice of basis (specifically Slater Type Orbitals (STOs)). This difficulty lies in the fact that not all integrals have a known analytically integrable form when Slater functions are used as a basis. The Prolate Spheroidal coordinate system has only been applied to diatomic systems\, but offers an advantage in numerical integration accuracy over more generally applicable schemes. A third center is added in the PS coordinate grid in this work\, where we will note the challenges and steps taken to handle a third center. It is important to note that the addition of a third center is sufficient to solve all integrals required by the Hamiltonian under the Resolution of the Identity(RI) approximation. Analysis was performed using metrics which test the scheme directly (error values for integral matrix elements) and indirectly(applying integrals to Hartree-Fock(HF) and post-HF methods to get observables). The methods ability to accurately calculate 2-center properties allows for the use of larger basis sets which were previously unserviceable. \nAlexander Stark (Chemistry and Scientific Computing)\nThis is Alexander Stark\, he is in the Zimmerman group in the chemistry department\, his research involves refining different levels of wave-function theory as to improve the accuracy of predictions. \n\nMultiscale Modeling of Radical and Vibrational Pathways in Plasma-Assisted Ammonia Synthesis on Fe(110) and Ni(111)\nLow-temperature plasma (LTP)-assisted ammonia synthesis is a promising alternative to the Haber-Bosch process for decentralized\, renewable energy-driven production. Progress has been limited by an incomplete mechanistic understanding\, particularly the debated roles of vibrationally excited N2(g)\,ν and plasma-generated N · /H · radicals\, which may explain the unexpected insensitivity of catalyst performance across metals. We apply first-principles multiscale modeling—combining density functional theory (DFT) calculations and a packed-bed reactor microkinetic model—to disentangle these contributions to LTP-assisted NH3(g) synthesis over Fe(110) and Ni(111) catalysts. The model incorporates an experimentally derived vibrationally excited N2(g)\,ν distribution from a radiofrequency (RF) plasma source and accounts for their vibrational surface quenching. The model predicts that vibrational excitation enhances the dissociation of N2(g)\,ν on Ni but its impact on Fe is limited. Quenching of vibrationally excited N2(g)\,ν\ndue to collisions with the reactor walls and the catalyst surface does not significantly affect ammonia yields on either catalyst\, with less an an order of magnitude increase. In contrast\, Eley-Rideal reactions involving N · and H · radicals dominate ammonia formation\, bypassing the conventional rate-controlling steps of thermal catalysis on Fe and Ni materials. This mechanistic picture explains the experimentally observed insensitivity of ammonia production rates to metal catalyst identity and highlights the central role of radical chemistry in plasma-assisted ammonia synthesis. \nOluwatosin Ohiro (Chemical Engineering and Scientific Computing)\nOluwatosin earned his primary degree in petroleum and gas engineering and worked for several years as a reservoir engineer and oil asset planner. He is currently pursuing his PhD in the Chemical Engineering Department under the supervision of Prof. Bryan Goldsmith. His research focuses on the interface of computational materials science and heterogeneous catalysis. \n\nQuantifying the state of inflammation in invasive lobular breast cancer using a one-class logistic regression algorithm\nAfter invasive ductal cancer (IDC)\, invasive lobular cancer (ILC) is the second most diagnosed type of breast cancer. Given complexities with detection\, patients with ILC may be diagnosed at an advanced stage of disease\, presenting larger tumors and a higher metastasis incidence when compared to IDC. It is increasingly appreciated that the immune system plays a crucial role in both primary tumor and metastatic progression and is a complex balance of both innate and adaptive immune interactions. Critically\, the success of modern immunotherapies\, such as immune checkpoint blockade\, depends not only on the T cells on which they directly act\, but also the complicated and often contradictory influence of innate myeloid cells on the lymphoid compartment. Innate myeloid cells in the tumor microenvironment (TME) have the potential to be both pro- and anti-cancer and often present in a spectrum within the TME. The dynamic nature of these immune components makes understanding and interpreting the state of the immune system in the TME very difficult. Simple methods\, like quantifying tumor infiltrating lymphocytes (TILs) or tumor-associated macrophages (TAMs) do not account for the function of these cells\, which may be pro- or anti-tumor. We investigated the role of the immune system in the tumor microenvironment (TME) of ILC by developing a machine learning-based inflammation score (IS) that can quantify the complex state of the immune system within a primary tumor on a numerical scale from pro- to anti-inflammatory. We correlate the IS with overall survival and disease-free survival to set prognostic thresholds for immune dysregulation. \nKate Griffin (Biomedical Engineering and Scientific Computing)\nKate is a PhD Candidate in Biomedical Engineering in the Shea Lab. Her research involves engineering nanoparticles to reverse immunosuppression in metastatic breast cancer\, and using computational methods to understand immune dysregulation in the metastatic niche.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-7/
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251118T114500
DTEND;TZID=America/Detroit:20251118T124500
DTSTAMP:20260620T205015
CREATED:20250926T143953Z
LAST-MODIFIED:20251023T021817Z
UID:10000840-1763466300-1763469900@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\nTailored Ultrashort Pulse Bursts in a Gain-Managed Nonlinear Fiber Amplifier for Coherent 50fs Pulse Stacking at mJ Energies\nWe show a method of scaling gain-managed nonlinear amplifiers (GMNA) to mJ energies using feedback-driven scaling of pulse bursts that can be time-combined into a single 50fs output pulse using coherent pulse stacking.  \nLauren Cooper (Electrical Engineering and Scientific Computing)\nLauren Cooper is working on coherent pulse stacking of gain-managed nonlinear amplified pulse bursts for high power applications. She is being advised by Professor Almantas Galvanauskas in the Electrical Engineering department at the University of Michigan. \n\nLeveraging multipole models to measure rotation in time-dependent potentials\nMultipole expansion models are efficient and flexible methods by which to encode aspherical and time-dependent fluctuations in 3D functions of galactic densities and potentials. Historically these techniques have been used primary to perform orbit integration and N-body simulations. However\, it is becoming increasingly clear that the expansion series coefficients encode useful physical information that may be used to discover novel dynamics. In this talk\, I will outline my recent work using multipole expansion coefficient series\, including methods I have developed for measuring rotation in the quadrupole component and the discoveries multipole expansion has facilitated. \nNeil Ash (Astronomy and Scientific Computing)\nNeil is a 5th year graduate student in the Astronomy Department working with Professor Monica Valluri. His research interests include hydrodynamical simulations of cosmic structure formation and galactic dynamics\, with a special focus on the dark matter haloes and their interactions with the baryonic (stellar) galactic component. \n\nTracing Refractory Material in the Inner 10 AU of Protoplanetary Disks\nPlanets form in protoplanetary disks by building their cores from rocky/refractory material that drifts inward toward the central star\, establishing this material as the fundamental building blocks of all planets. Identifying the physical processes that regulate rocky material within the inner 10 AU during disk evolution is essential for understanding the formation of the observed diversity of planetary systems\, particularly for all rocky planets. In my PhD dissertation\, I study the content of rocky material in the inner regions of protoplanetary disks. I utilize spectroscopic observations across the entire electromagnetic spectrum\, using both ground-based and space telescopes\, to disclose how much rocky material reaches the inner disk and what its composition is. I have found (1) evidence for refractory depletion in the inner gas disk\, 2) connections between age and dust-trapping/planet-forming mechanisms with higher depletion values\, and 3) estimates of the impact of sublimation temperature and dust drifts on the composition of rocky material in the inner disk. Overall\, my work probes dust trapping and dust drift theories. \nMarbely Micolta (Astronomy and Scientific Computing)\nI’m a fifth-year Ph.D. student in Astronomy\, working with Prof. Nuria Calvet. I’m from Venezuela. My research aims to constrain the physical and chemical processes that regulate rocky (refractory) material\, the building blocks of planets\, in the inner 10AU of protoplanetary disks. I have developed a broad expertise in disk characterization\, using observations across the electromagnetic spectrum\, both from the ground and space telescopes.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-6/
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251111T114500
DTEND;TZID=America/Detroit:20251111T124500
DTSTAMP:20260620T205015
CREATED:20250926T143952Z
LAST-MODIFIED:20251105T194338Z
UID:10000839-1762861500-1762865100@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\nPy-Conformational-Sampling: Towards Predicting Stereoselectivity\nStereoselective reactions are an integral part of organic synthesis due to the abundance of chiral centers in natural products and drug molecules. The design of these reactions remains challenging due to specific substrate requirements\, delicate reaction conditions and more importantly\, multiple competing product-forming transition states (TSs). These TSs often arise from a range of conformers present within the reactant complex. Thus\, predicting stereoselectivity requires detailed insights into favorable interactions amidst the conformational ensemble. This work introduces Py-Conformational-Sampling (PyCoSa) as a methodical approach to sample transition-metal-catalyzed stereoselective reactions. This technique\, when devoted to atroposelective Suzuki-Miyaura coupling to generate axially chiral biaryl products\, shows a variety of mechanistic possibilities through which C(sp2)–C(sp2) bond formation takes place. \nSoumik Das (Chemistry and Scientific Computing)\nSoumik is currently pursuing Ph.D. in Chemistry and Scientific Computing under the supervision of Dr. Paul Zimmerman. His research interests involve developing and applying automated and predictive computational tools using quantum chemistry for reaction design and discovery. Among other things\, he’s also a recipient of MICDE Graduate Fellowship for the academic year 2023-2024 and presented his research in MICDE conference SciFM ’24. \n\nDensity Functional Theory Simulations of Icosahedral Quasicrystals\nQuasicrystals (QCs) are fascinating materials with their long-range aperiodicity and forbidden rotational symmetry\, which opened a new type of classification in crystallography and attracted much attention to its potential applications to condensed matter\, statistical and solid-state physics. The characterization and identification of QCs after the first discovery is widely undertaken\, but thermodynamic stability and kinetics of nucleation are ongoing questions to answer the synthesizability and design novel structures. The quantum mechanical simulation including the density functional theory (DFT) is a widely used method for atomic-scale simulation\, however\, aperiodicity of QC structure makes it challenging to apply a computational model for periodic boundary frameworks. In this present work\, atomistic simulation of Tsai-type ScZn and YbCd icosahedral quasicrystals (iQCs)\, which is one of recently discovered iQCs types\, were performed using density functional theory – finite element (DFT-FE) method to study the thermodynamic stability\, role of surface energy to the stability\, and driving force of QC formation. The size-dependent and mixed-thermodynamic-and-kinetic phase diagram from quantitative theoretical calculations can provide fundamental insights into the origin of QC formation. \nWoohyeon Baek (Materials Science and Engineering and Scientific Computing)\nWoohyeon Baek is a PhD student in Materials Science and Engineering and Scientific Computing under the supervision of Dr. Wenhao Sun. He is working on the thermodynamics and kinetics of non-traditional materials formation from computational simulations including quasicrystals\, minerals\, functional materials\, and organic crystals. \n\nData-Driven Development of Constitutive Equations for Thixotropic Waxy Oil Rheology for Flow Assurance Using Symbolic Regression and PINNs\nWaxy crude oils crystallize below the wax appearance temperature\, forming networks that make rheology strongly dependent on temperature and prior shear history\, complicating pipeline restart operations. We develop a compact\, predictive modeling framework that combines data-driven and mechanistic approaches\, with all methods using differential scanning calorimetry crystallinity measurements to encode temperature effects. Symbolic regression (PySR) trained on two temperatures accurately predicts steady-state flow curves at remaining temperatures. A Fractal Isotropic-Kinematic Hardening (FIKH) model\, fitted at two temperatures for steady response\, predicts steady behavior at other temperatures; for transients\, parameters identified at 5°C reproduce rejuvenation and recovery dynamics at additional temperatures. We introduce LFP-IKH (Liquid Free-Path IKH)\, a novel approach that defines the structural state as liquid-network connectivity bounded by crystallinity. When calibrated only on steady-state data\, LFP-IKH predicts both steady and transient responses across all temperatures without refitting. This yields a mechanism-based framework that requires no parameter adjustment across temperature ranges\, making it suitable for flow-assurance prediction and restart design applications. \nSamuel Ogunwale (Chemical Engineering and Scientific Computing)\nSamuel Ogunwale is a sixth-year PhD student in Chemical Engineering working in the Larson group. His research focuses on developing predictive models for complex fluid systems\, combining mechanistic understanding with experimental validation to address industrial flow assurance challenges.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-5/
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/2025/09/2025-11-11-Das-Baek-Ogunwale.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251104T114500
DTEND;TZID=America/Detroit:20251104T124500
DTSTAMP:20260620T205015
CREATED:20250926T143951Z
LAST-MODIFIED:20251009T184957Z
UID:10000838-1762256700-1762260300@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\nEmbodying mechano-intelligence in mechanical metastructures for in-memory phononic learning\nMechano-intelligence (MI)—intelligence embodied within the mechanical domain of materials and structures—promises autonomous systems with higher effectiveness\, efficiency\, and resilience. Rather than outsourcing information processing entirely to electronics\, MI envisions materials that store\, process\, and adapt to environmental inputs through intrinsic mechanical responses\, reducing latency and energy while improving robustness in extreme and cyber-contested conditions. Realizing MI requires three elements: a memory module to retain knowledge from inputs\, a computing module to interpret and act on information\, and a physical communication interface linking storage and computation. In this talk\, I will introduce a new approach to realizing MI in and through a reconfigurable phononic metastructures via the concept of in-memory phononic learning\, where mechanical states are programmed to encode and store information and the elastic-wave physics is harnessed to carry out computation and decision—a framework that unifies the full information chain in the mechanical domain and provides efficient\, physically interpretable processing by using elastic waves as the natural communication and processing medium.  \nYuning Zhang (Mechanical Engineering and Scientific Computing)\nYuning is a Ph.D. candidate in Mechanical Engineering under Prof. Kon-Well Wang. His research focuses on wave propagation in phononic metastructures\, and the development of physical computing and mechanical intelligence.  \n\nGlobal Probabilistic Geomagnetic Perturbation Forecasting \nAccurately predicting the horizontal component of the ground magnetic field perturbation (dBH)\, as a proxy for Geomagnetically Induced Currents (GICs)\, is crucial for estimating the impact of geomagnetic storms and remains a topic under active investigation. The current operational Geospace model is computationally expensive for fine-grid global simulations\, while existing machine learning methods consistently tend to underestimate dBH. Additionally\, these models either lack uncertainty quantification (UQ)\, which is either overlooked or treated as secondary. In this work\, as part of the NextGen SWMF project funded by NSF\, we develop a data-driven\, grid-free global model using deep Gaussian process (DGP)\, a Bayesian non-parametric approach that forecasts the dBH for the full surface of Earth with calibrated uncertainty. The model uses solar wind measurements and the Dst index as input\, and it is trained based on ground magnetometer station data provided by SuperMAG over the period 1995-2022. The model’s predictions are evaluated based on the Heidke skill score (HSS) for a total of 23 storms in 2015. We further test the model on the 2024 Gannon superstorm. The results demonstrate that our model outperforms the state-of-the-art model\, with predictions exhibiting high accuracy in mid-latitudes and high-latitude regions in the northern hemisphere. \nHongfan Chen (Mechanical Engineering and Scientific Computing)\nHongfan Chen is a fourth-year PhD student in Mechanical Engineering and the Michigan Institute for Computational Discovery and Engineering (MICDE) Scientific Computing program. His research develops computational methods for uncertainty quantification (UQ) and machine learning (ML) in complex scientific and engineering systems\, with emphases on data assimilation (DA)\, knowledge-guided machine learning\, and optimal experimental design (OED).  \n\n 
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-seminar-series-4/
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251028T114500
DTEND;TZID=America/Detroit:20251028T124500
DTSTAMP:20260620T205015
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251024T143000
DTEND;TZID=America/Detroit:20251024T160000
DTSTAMP:20260620T205015
CREATED:20250926T143948Z
LAST-MODIFIED:20250926T201218Z
UID:10000836-1761316200-1761321600@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/graduate-opportunities-in-computational-and-data-science-2025-north/
LOCATION:1670 Bob and Betty Beyster Building\, 2260 Hayward Street\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Sessions
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/09/2025-Happening@UMich-graphic.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:20251020T180000
DTEND;TZID=America/Detroit:20251020T190000
DTSTAMP:20260620T205015
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251017T143000
DTEND;TZID=America/Detroit:20251017T160000
DTSTAMP:20260620T205015
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251007T114500
DTEND;TZID=America/Detroit:20251007T124500
DTSTAMP:20260620T205015
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20251002T140000
DTEND;TZID=America/Detroit:20251002T150000
DTSTAMP:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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:20260620T205015
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250425T120000
DTEND;TZID=America/Detroit:20250425T130000
DTSTAMP:20260620T205015
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250422T120000
DTEND;TZID=America/Detroit:20250422T130000
DTSTAMP:20260620T205015
CREATED:20250114T140812Z
LAST-MODIFIED:20260522T152527Z
UID:10000798-1745323200-1745326800@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nApplications of the phase-field method to polycrystalline materials\nPhase-field modeling is a common diffuse interface method for simulating microstructure evolution due to its ability to capture complex morphologies without the need for explicitly tracking phase interfaces. A typical application of the phase-field method is polycrystalline grain growth during annealing\, where grain boundaries migrate toward their centers of curvature. Recent studies have shown abnormally large grains can be grown in shape memory alloys during cyclic annealing due to additional driving forces generated during the growth and dissolution of second-phase precipitates. In this work\, we model grain growth via a phase-field model that considers stored energy generated during the cyclic heat treatments. Applications of the phase-field method to experimentally acquired grain microstructures will also be discussed. \nZach Croft\, Applied Physics\nZach is a PhD student in the Applied Physics program. He works in the field of computational materials science with an emphasis on phase-field modeling of polycrystalline evolution and solidification of alloys under Professor Katsuyo Thornton. \n\nUsing causal inference to estimate counterfactual disparity measures for access to weight management treatments\nType 2 Diabetes (T2D) is a prevalent condition with significant variation in outcomes based on race and ethnicity\, underscoring the need for more improved prevention practices. Because effective weight management is a key component of T2D prevention\, increasing access to evidence-based treatments for those most at-risk for developing T2D is imperative. Yet\, existing population health management approaches do not typically measure disparities in access to treatments or do so in ways that do not account for the increased risk experienced by certain patient populations. This talk will (1) describe how causal inference was used to calculate counterfactual estimates of disparities in referral to weight management treatments among a population of adults with obesity\, (2) compare counterfactual estimates generating from the standard approach vs. a risk-based approach \, and (3) share UM research\, computing\, and other resources that supports this research. \nCassie Turner\, Health Infrastructures and Learning Systems\nCassie has a joint appointment at Michigan Medicine and the Ann Arbor Veteran Affairs Health System\, where she contributes to health research and practice focused on improving metabolic health through leveraging analytics\, novel care models\, and learning health systems approaches.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-22-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250415T120000
DTEND;TZID=America/Detroit:20250415T133000
DTSTAMP:20260620T205015
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:20260620T205015
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:20260620T205015
CREATED:20250114T141754Z
LAST-MODIFIED:20260413T190501Z
UID:10000796-1744111800-1744117200@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n  \n  \n\nProbabilistic Rounding Uncertainty Analysis for Floating-Point Statistical Models\nAdvancements in computer hardware now allow low- and mixed-precision arithmetic to improve efficiency\, especially on new architectures. It is thus critical that the rounding uncertainty be rigorously quantified alongside traditional sources of uncertainty including those from observations\, sampling\, and numerical discretization. Traditional deterministic rounding uncertainty analysis (DBEA) assumes that the absolute rounding errors equal the unit roundoff u\, considering the worst-case scenario. This work presents a novel probabilistic rounding uncertainty analysis called VIBEA. By treating rounding errors as i.i.d. random variables and leveraging concentration inequalities\, VIBEA provides high-confidence estimates for rounding uncertainty using higher-order rounding error statistics. The presented framework is valid for all problem sizes n\, unlike DBEA\, which requires nu<1. Further\, it can account for the potential cancellation of rounding errors\, resulting in rounding uncertainty estimates that grow slowly with n. We demonstrate that quantifying rounding uncertainty alongside traditional sources allows for a more efficient allocation of computational resources\, balancing efficiency with accuracy. This study takes a step towards a comprehensive mixed-precision approach to enhance model reliability and optimize resource allocation in predictive modeling. The talk will conclude with a vision for end-to-end\, formally verified numerics for scientific computing. \nSahil Bhola\, Aerospace Engineering and Scientific Computing\nSahil Bhola is a 4th-year Ph.D. candidate in Aerospace Engineering and Scientific Computing at the University of Michigan. He is a MICDE Fellow and a J.N. Tata Scholar\, advised by Prof. Karthik Duraisamy. He holds a master’s degree in Aerospace Engineering from the University of Michigan and a bachelor’s degree in Mechanical Engineering from Thapar University\, India. His research focuses on adaptive mixed-precision methods\, experimental design for potential energy surfaces\, and flow-based generative models for Bayesian inference. \n\nHomogenous Cities? How Conflict and Politics Shape the Urban Topography\nThe relationship between armed conflict\, politics\, and the urban built environment \nMartin Macias Medellin\, Political Science\nMartin Macias Medellin is interested in the dynamics of mass political dissent\, political and criminal violence\, and state-building processes. In his doctoral dissertation he studies how conflict affects the way in which cities are built and how the physical structures of urban areas affect the dynamics of armed conflict. \n\nMinimally Orthogonal Causal Effect Estimation\nCausal Machine Learning \nYiman Ren\, Business Economics\nYiman Ren is a final year PhD student in Business Economics and Scientific Computing at Ross School of Business. Her research focuses on financial economics and causal machine learning.
URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-8-2025/
LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar
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DTSTART;TZID=America/Detroit:20250403T160000
DTEND;TZID=America/Detroit:20250403T170000
DTSTAMP:20260620T205015
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
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DTSTART;TZID=America/Detroit:20250401T160000
DTEND;TZID=America/Detroit:20250401T170000
DTSTAMP:20260620T205015
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
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