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DTSTART;TZID=America/Detroit:20260218T120000
DTEND;TZID=America/Detroit:20260218T130000
DTSTAMP:20260603T185400
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
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2026/01/2-18-Gozman-Han-Jiang.png
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DTSTART;TZID=America/Detroit:20260225T120000
DTEND;TZID=America/Detroit:20260225T130000
DTSTAMP:20260603T185400
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
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2026/01/2-25-Mathanker-Liu-Komaiha-1.png
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