BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Michigan Institute for Computational Discovery and Engineering - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Michigan Institute for Computational Discovery and Engineering X-ORIGINAL-URL:https://micde.umich.edu X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/Detroit BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20220313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20221106T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20230312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20231105T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20240310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20241103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20250309T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20251102T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20260308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20261101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250711T120000 DTEND;TZID=America/Detroit:20250711T130000 DTSTAMP:20260613T140009 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:20250422T120000 DTEND;TZID=America/Detroit:20250422T130000 DTSTAMP:20260613T140009 CREATED:20250114T140812Z LAST-MODIFIED:20260522T152527Z UID:10000798-1745323200-1745326800@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nApplications of the phase-field method to polycrystalline materials\nPhase-field modeling is a common diffuse interface method for simulating microstructure evolution due to its ability to capture complex morphologies without the need for explicitly tracking phase interfaces. A typical application of the phase-field method is polycrystalline grain growth during annealing\, where grain boundaries migrate toward their centers of curvature. Recent studies have shown abnormally large grains can be grown in shape memory alloys during cyclic annealing due to additional driving forces generated during the growth and dissolution of second-phase precipitates. In this work\, we model grain growth via a phase-field model that considers stored energy generated during the cyclic heat treatments. Applications of the phase-field method to experimentally acquired grain microstructures will also be discussed. \nZach Croft\, Applied Physics\nZach is a PhD student in the Applied Physics program. He works in the field of computational materials science with an emphasis on phase-field modeling of polycrystalline evolution and solidification of alloys under Professor Katsuyo Thornton. \n\nUsing causal inference to estimate counterfactual disparity measures for access to weight management treatments\nType 2 Diabetes (T2D) is a prevalent condition with significant variation in outcomes based on race and ethnicity\, underscoring the need for more improved prevention practices. Because effective weight management is a key component of T2D prevention\, increasing access to evidence-based treatments for those most at-risk for developing T2D is imperative. Yet\, existing population health management approaches do not typically measure disparities in access to treatments or do so in ways that do not account for the increased risk experienced by certain patient populations. This talk will (1) describe how causal inference was used to calculate counterfactual estimates of disparities in referral to weight management treatments among a population of adults with obesity\, (2) compare counterfactual estimates generating from the standard approach vs. a risk-based approach \, and (3) share UM research\, computing\, and other resources that supports this research. \nCassie Turner\, Health Infrastructures and Learning Systems\nCassie has a joint appointment at Michigan Medicine and the Ann Arbor Veteran Affairs Health System\, where she contributes to health research and practice focused on improving metabolic health through leveraging analytics\, novel care models\, and learning health systems approaches. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-22-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-22-Croft-Turner.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250415T120000 DTEND;TZID=America/Detroit:20250415T133000 DTSTAMP:20260613T140009 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:20250408T113000 DTEND;TZID=America/Detroit:20250408T130000 DTSTAMP:20260613T140009 CREATED:20250114T141754Z LAST-MODIFIED:20260413T190501Z UID:10000796-1744111800-1744117200@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n  \n  \n\nProbabilistic Rounding Uncertainty Analysis for Floating-Point Statistical Models\nAdvancements in computer hardware now allow low- and mixed-precision arithmetic to improve efficiency\, especially on new architectures. It is thus critical that the rounding uncertainty be rigorously quantified alongside traditional sources of uncertainty including those from observations\, sampling\, and numerical discretization. Traditional deterministic rounding uncertainty analysis (DBEA) assumes that the absolute rounding errors equal the unit roundoff u\, considering the worst-case scenario. This work presents a novel probabilistic rounding uncertainty analysis called VIBEA. By treating rounding errors as i.i.d. random variables and leveraging concentration inequalities\, VIBEA provides high-confidence estimates for rounding uncertainty using higher-order rounding error statistics. The presented framework is valid for all problem sizes n\, unlike DBEA\, which requires nu<1. Further\, it can account for the potential cancellation of rounding errors\, resulting in rounding uncertainty estimates that grow slowly with n. We demonstrate that quantifying rounding uncertainty alongside traditional sources allows for a more efficient allocation of computational resources\, balancing efficiency with accuracy. This study takes a step towards a comprehensive mixed-precision approach to enhance model reliability and optimize resource allocation in predictive modeling. The talk will conclude with a vision for end-to-end\, formally verified numerics for scientific computing. \nSahil Bhola\, Aerospace Engineering and Scientific Computing\nSahil Bhola is a 4th-year Ph.D. candidate in Aerospace Engineering and Scientific Computing at the University of Michigan. He is a MICDE Fellow and a J.N. Tata Scholar\, advised by Prof. Karthik Duraisamy. He holds a master’s degree in Aerospace Engineering from the University of Michigan and a bachelor’s degree in Mechanical Engineering from Thapar University\, India. His research focuses on adaptive mixed-precision methods\, experimental design for potential energy surfaces\, and flow-based generative models for Bayesian inference. \n\nHomogenous Cities? How Conflict and Politics Shape the Urban Topography\nThe relationship between armed conflict\, politics\, and the urban built environment \nMartin Macias Medellin\, Political Science\nMartin Macias Medellin is interested in the dynamics of mass political dissent\, political and criminal violence\, and state-building processes. In his doctoral dissertation he studies how conflict affects the way in which cities are built and how the physical structures of urban areas affect the dynamics of armed conflict. \n\nMinimally Orthogonal Causal Effect Estimation\nCausal Machine Learning \nYiman Ren\, Business Economics\nYiman Ren is a final year PhD student in Business Economics and Scientific Computing at Ross School of Business. Her research focuses on financial economics and causal machine learning. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-8-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-08-Bhola-Macias-Medellin-Ren-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250401T120000 DTEND;TZID=America/Detroit:20250401T130000 DTSTAMP:20260613T140009 CREATED:20250114T141907Z LAST-MODIFIED:20260522T154208Z UID:10000795-1743508800-1743512400@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nRegister to attend\n\nMonitoring the fidelity of the LIGO detectors\nThe detection of gravitational waves depends on LIGO’s ability to discriminate authentic signals from instrumental noise. To improve this capability\, the LIGO Scientific Collaboration employs hardware injections\, controlled\, simulated signals introduced directly into the detectors. These injections validate the analysis pipelines and refine the calibration of the detector. This study focuses on continuous- wave signals from the initial phase of the fourth observing run (O4a)\, using simulated emissions from rapidly rotating neutron stars as benchmarks to assess sensitivity and data-processing efficiency. The analysis employs a template generation approach that uses complex conjugates to align observational data with theoretical signal templates and offers probabilistic validation of detected signals. An investigation explores the role of hardware injections in the refinement of software models and the maintenance of the timing and amplitude. By utilizing daily diagnostic plots for a diverse array of synthetic neutron star signals\, including both binary and isolated systems\, the detector’s responsiveness is evaluated over a broad frequency spectrum. The results emphasize the importance of hardware injections in sustaining calibration standards and affirming LIGO’s reliability in gravitational wave detection \nPreet Baxi\, Physics and Scientific Computing\nPreet Baxi is an innovative Data Scientist and Algorithm Developer with experience in scientific computing\, data pipeline optimization\, and business data analysis. Specializing in developing advanced algorithms and has worked extensively in gravitational wave data analysis\, contributing to cutting-edge research in astrophysics. Currently working in large language models (LLMs)\, focusing on their development and optimization. \n\nFast Summation for Geophysical Fluid Dynamics\nFast Summation refers to a family of techniques for the fast approximation of N-body sums. While traditionally fast summation has been applied to problems coming from astrophysics or electrodynamics\, many problems in geophysical fluid dynamics can be rewritten as the computation of a spherical convolution\, and when these integrals are discretized\, the resulting problem is a N-body problem. In this talk\, I discuss a novel spherical tree code/fast multipole method based on barycentric Lagrange interpolation\, as well as applications to problems coming from geophysical fluid dynamics\, including tidal modeling and the problem of computing Self Attraction and Loading in the ocean model MOM6. \nAnthony Chen\, Applied and Interdisciplinary Mathematics and Scientific Computing\nAnthony Chen is a 4th year in Applied and Interdisciplinary Mathematics working on fast summation for problems in geophysics. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-4-1-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-04-01-Baxi-Chen.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250318T120000 DTEND;TZID=America/Detroit:20250318T130000 DTSTAMP:20260613T140009 CREATED:20250114T142206Z LAST-MODIFIED:20250228T184043Z UID:10000794-1742299200-1742302800@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n  \n\nSolving High Reynolds Number Flows on Cartesian Cut-cell Meshes using a Jacobian-Free Newton–Krylov Method\nIn this work\, we developed a Newton–Krylov method for a second-order Cartesian cut cell Reynolds-averaged Navier–Stokes (RANS) solver\, Viscous Aerodynamic Cartesian Cut cells (VACC)\, with the one equation Spalart–Allmaras (SA) turbulence model. The Newton–Krylov method uses pseudo-transient continuation and a point Jacobi preconditioner to accelerate convergence. Then various wall functions were compared on a finite flat plate and 2D bump cases. The SA analytical wall function was used as a baseline. An ordinary differential equation (ODE) wall function and wall-modeled RANS (WMRANS) approach were also implemented. Although these methods all showed promise\, the interior viscous fluxes resulted in oscillatory pressures. These oscillations degraded the accuracy of all of the solutions. \nAlex Kleb\, Aerospace Engineering\nAlex Kleb is a fifth year PhD candidate in the CFD Group and MDO lab in the Aerospace Engineering department. \n\nGeometrically Nonlinear Methods for High-Fidelity MDO of Very-Flexible Aircraft\nOver the past decade\, advances in Multidisciplinary Design Optimization (MDO) have enabled the optimization of aircraft wings using high-fidelity simulations of their coupled aerodynamic and structural behavior. Using RANS CFD and detailed structural finite element wingbox models\, the aerodynamic shape and internal structural sizing of a wing can be optimized concurrently to tailor the wing’s aeroelastic behavior and optimally trade-off drag and structural mass. This capability makes MDO a key enabling technology for the next generation of efficient high-aspect-ratio transport aircraft. However\, as their aspect-ratios increase\, these wings increasingly exhibit geometrically nonlinear behavior that cannot be correctly modeled by typical linear structural analysis methods. This work demonstrates the first simultaneous optimization of a wing’s aerodynamic shape and structural sizing using high-fidelity geometrically nonlinear models. Our methods are implemented in the open-source finite element library\, TACS\, and include a geometrically nonlinear shell element formulation\, an efficient and robust nonlinear solver\, and a constitutive model for stiffened shells. We demonstrate the ability to couple these nonlinear structural analysis tools to a high-fidelity RANS CFD solver using a geometrically nonlinear load and displacement transfer scheme. Finally\, we use this capability to optimize a single-aisle commercial transport aircraft wing featuring 547 design variables and 1277 constraints. \nAlasdair Christison Gray\, Aerospace Engineering\nAlasdair Christison Gray is a 5th year PhD student in the Aerospace Engineering department’s MDO Lab. His research focuses on applying high performance computing to the large scale design optimization of aircraft wings. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-3-18-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-03-18-Kleb-Christison-Gray.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250311T120000 DTEND;TZID=America/Detroit:20250311T130000 DTSTAMP:20260613T140009 CREATED:20250225T214118Z LAST-MODIFIED:20250225T214448Z UID:10000810-1741694400-1741698000@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nUnraveling Rotator Cuff Tendon Tear Growth Mechanisms with Full-Volume Strains and Data-Driven Modeling\nIn this talk\, I will show how full-volume methods\, which can probe internal locations of a material\, enable the detection of regions with high shear strain concentration in intact and torn rotator cuff tendons. I will also explain my approach to use these full-volume datasets to develop a finite element model of this tendon using variational system identification\, and future work to obtain validated computational models that can predict tear growth. \nNathaly Villacis\, Mechanical Engineering and Scientific Computing\nNathaly is a fifth year Ph.D. candidate in Mechanical Engineering\, who works at the Soft Tissue Mechanics Lab\, supervised by Dr. Ellen Arruda. Her research involves the characterization of rotator cuff tendon tear growth with experimental and computational methods. She will work on machine learning models of the rotator cuff once she finishes her Ph.D. \n\nIncremental Tensor Decompositions for Machine Learning and Bayesian Inference\nWith recent advancements in large-scale parallel computing\, there is an increased interest in constructing high-fidelity digital twins of complex systems. Especially for systems that have limited physical experimentation possibilities\, high-fidelity simulations may provide the main source of information for constructing digital twins. However\, performing such simulations is computationally intensive and generates extreme amounts of data. The size of the generated simulation data makes it challenging to use the data in further analysis. As the spatial and temporal resolution of these simulations grow\, even storing the data may become a serious bottleneck. This talk proposes a solution to this multi-faceted problem through the use of low-rank tensor decompositions. Specifically\, we present incremental algorithms that provide computationally efficient ways of compressing data with accuracy guarantees. We showcase a diverse array of applications\, from 3D turbulent Navier-Stokes simulations to Minecraft gameplay videos\, demonstrating the versatility and power of these techniques. \nDoruk Aksoy\, Aerospace Engineering and Scientific Computing\nDoruk is a 5th year Ph.D. candidate in the Department of Aerospace Engineering in the Computational Autonomy group. His research focuses on developing incremental low-rank tensor decomposition algorithms to compress large-scale data for downstream machine learning applications. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-20250311/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/02/2025-03-11-Villacis-Aksoy.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250225T120000 DTEND;TZID=America/Detroit:20250225T130000 DTSTAMP:20260613T140009 CREATED:20250114T142545Z LAST-MODIFIED:20250221T144301Z UID:10000793-1740484800-1740488400@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:  \nThe MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. If you have any questions\, please email micde-phd@umich.edu. \nRegister to attend\n  \n\nNumerical simulation of the collapse of a cavitation bubble near a deformable solid surface\nThe exact mechanisms leading to the permanent deformation of solid surfaces\, following a cavitation event\, are still unclear. Specifically\, the relationship between the characteristics of a given cavitation bubble and the shape of the resulting pit is unknown. In this study\, we numerically investigate the collapse of a single cavitation bubble near a solid surface\, with the objective of characterizing how the pit shape (height and depth) changes with the bubble initial radius\, its distance from the solid and the initial pressure difference at the bubble interface. To this end\, we implement a diffuse interface method for the interaction of multiple compressible fluids and hyperelastic solids in an Eulerian frame of reference. This method numerically solves the evolution equations of mass\, momentum\, energy as well as volume fractions of each material and of the mixture. The model is closed by splitting the internal energy of each material into hydrodynamic and elastic contributions\, with appropriate equations of state. A set of evolution equations of local cobasis\, with a plastic source term\, are used to compute the elastic Finger tensor\, which is needed to obtain the elastic energy and the deviatoric stress. We additionally provide improvements to the numerical method to preserve interface conditions. The proposed method allows to elucidate some of the mechanisms of cavitation pitting. \nBaudouin Fonkwa Kamga\, Mechanical Engineering and Scientific Computing\nBaudouin is a 4th year PhD student in the department of Mechanical Engineering\, under the supervision of Eric Johnsen. His research combines the theoretical study of cavitation in viscoelastic medium and the development of numerical methods for multimaterial compressible flows. \n\nScalable foundation model training for computational pathology\nScalable and efficient foundation model training is critical for advancing computational pathology. In this talk\, we present a two-stage self-supervised pipeline for whole slide image (WSI) analysis. First\, HiDisc leverages the inherent patient–slide–patch hierarchy to learn robust visual representations efficiently without relying on heavy data augmentation\, outperforming existing methods in cancer diagnosis and genetic mutation prediction. Building on these high-quality patch-level features\, our second stage\, Slide Pre-trained Transformers (SPT)\, treats WSI patches as tokens and integrates data transformation strategies from both language and vision models to capture the rich morphological diversity of gigapixel images. Together\, these methods offer a scalable\, efficient framework for training foundation models that drive robust performance across a range of diagnostic tasks. \nXinhai Hou\, Bioinformatics and Scientific Computing\nXinhai Hou is a PhD candidate in the department of computational medicine and bioinformatics. His research focuses on self-supervised learning\, computer vision\, and multimodal machine learning\, with a particular emphasis on real-world applications such as AI in healthcare and medicine. URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-2-25-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-25-Fonkwa-Kamga-Hou-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250211T120000 DTEND;TZID=America/Detroit:20250211T130000 DTSTAMP:20260613T140009 CREATED:20250114T150617Z LAST-MODIFIED:20260522T152322Z UID:10000792-1739275200-1739278800@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAdaptive Deep Learning-Powered Multi-fidelity Stratified Sampling for Efficient Failure Analysis of Nonlinear Dynamic Systems\nCurrent stochastic simulation-based frameworks that leverage variance reduction techniques still require a substantial number of model evaluations to estimate small failure probabilities associated with rare events. In the context of high-fidelity modeling environments\, these frameworks can become computationally challenging\, especially when dealing with complex nonlinear systems. Despite the potential of providing remarkable computational efficiency\, low-fidelity models may yield bias if used directly. To address this challenge\, this work introduces a multi-fidelity framework within the setting of stratified sampling\, termed Multi-Fidelity Stratified Sampling (MFSS)\, for efficient estimation of failure probabilities given various limit states of interest. In this approach\, the strata-wise failure probabilities\, associated with a carefully selected stratification variable\, are estimated by multi-fidelity Monte Carlo. To minimize the computational budget\, the high-fidelity data used in the stratified multi-fidelity estimator is also employed as training data for developing a deep learning-based metamodel\, which then serves as a low-fidelity model. To derive the trade-off between the approximation quality and computational demand associated with the metamodel\, an adaptive strategy is proposed to seek the minimal training data that ensures a desired correlation between the high- and low-fidelity models. Through application to a full-scale high-rise steel building subject to stochastic wind excitation\, the proposed scheme is demonstrated to be capable of accurately reproducing exceedance probability curves of nonlinear responses of interest with significant computational gains\, compared to variance reduction techniques relying solely on high-fidelity models. \nLiuyun Xu (Civil Engineering and Scientific Computing)\nLiuyun Xu is a fourth-year Ph.D. candidate in Civil Engineering and Scientific Computing at the University of Michigan. Her research lies in enhancing the resilience and adaptation of civil infrastructures against climate-related hazards by leveraging AI/ML\, scientific computing and data science. \n\nA Hybrid Surrogate Modeling Framework for Digital Twins of Nuclear Energy Systems\nNuclear Power Plants (NPPs) are complex systems that can benefit from Digital Twin (DT) technologies to reduce operational costs and increase plant reliability. A system surrogate model is developed to predict quantities and responses associated with diverse physical and computational assets. The proposed hybrid surrogate modeling framework is applied to a Pebble-Bed Fluoride-salt-cooled High-temperature Reactor (PB-FHR)\, with a two-loop reactor configuration. The surrogate’s hybrid design combines the accuracy of physical models and computational efficiency of data-driven models to achieve speed and predictive robustness. This surrogate model is adaptable through assimilation with online measurements\, which is highlighted in a proposed DT framework design. \nJasmin Lim (Aerospace Engineering and Scientific Computing)\nJasmin is a 5th PhD student in the department of aerospace engineering in the Computational Aerosciences Laboratory under the advisement of Karthik Duraisamy. Her research is focused on developing data-driven methods for digital twin applications; which includes surrogate modeling\, data assimilation\, and system framework design. \n\nRegister to attend URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-2-11-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-11-Xu-Lim.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20250204T120000 DTEND;TZID=America/Detroit:20250204T130000 DTSTAMP:20260613T140009 CREATED:20250114T145459Z LAST-MODIFIED:20260522T154253Z UID:10000791-1738670400-1738674000@micde.umich.edu SUMMARY:Ph.D. in Scientific Computing Student Seminars DESCRIPTION:The MICDE PhD Student Seminar Series showcases the research of students in the Ph.D. in Scientific Computing. These events are open to the public\, but we request that all who plan to attend register in advance. \nIf you have any questions\, please email micde-phd@umich.edu. \nRegister to attend \n\nAerodynamic Shape Optimization with Curved Mesh Adaptation\nIn this talk we present a method for performing curved mesh adaptation during aerodynamic shape optimization with high-order computational fluid dynamics (CFD). High-order methods are promising because they offer increased accuracy for a given mesh. Mesh adaptation further improves the efficiency of high-order methods. These high-order methods require curved meshes to properly capture the simulated geometry and a mesh adaptation process that can generate curved meshes. Adapting these curved meshes needs to be robust as any failures will require human intervention inside the automated optimization loop. We first will present HOEP\, a novel and highly robust method for adapting highly-anisotropic curved meshes. Then we will present our adaptation strategy that balances computational cost with accuracy and show results for transonic airfoil optimization. \nAlexander Coppeans\, Aerospace Engineering and Scientific Computing\nAlexander Coppeans is a 5th year PhD Student in Aerospace Engineering and Scientific Computing. His research focuses on high-order adaptive methods for CFD based aerodynamic shape optimization. \nRegister to attend URL:https://micde.umich.edu/event/ph-d-in-scientific-computing-student-seminars-02-04-2025/ LOCATION:4th floor conference room\, Green Ct.\, 3520 Green Ct.\, Ann Arbor\, MI\, 48105\, United States CATEGORIES:Micde,MICDE PhD Seminar Series,Phd Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2025/01/2025-02-04-Coppeans.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230316T163000 DTEND;TZID=America/Detroit:20230316T170000 DTSTAMP:20260613T140009 CREATED:20230123T090003Z LAST-MODIFIED:20230809T181913Z UID:10000601-1678984200-1678986000@micde.umich.edu SUMMARY:PhD Seminar: Xintao Yan DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nXintao Yan\, PhD Candidate\, Civil and Environmental Engineering and Scientific Computing\nXintao Yan is currently a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Michigan\, Ann Arbor\, advised by Professor Henry Liu. He received his bachelor’s degree from the Department of Automotive Engineering at Tsinghua University\, China in 2018. His research interests are mainly about the safety of connected and automated vehicles\, including naturalistic driving behavior modeling and automated driving system evaluation. \nSimulating Naturalistic Driving Environment for Autonomous Vehicles\nSimulation provides a controllable\, efficient\, and low-cost venue for both developing and testing autonomous vehicles (AV). But for simulation to be an effective tool\, statistical realism of the simulated driving environment is a must. In this talk\, we will introduce methods to simulate naturalistic driving environment for AV testing purposes. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-xintao-yan/ LOCATION:Venue TBA\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Yan.png GEO:42.3053253;-83.6694169 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230316T160000 DTEND;TZID=America/Detroit:20230316T163000 DTSTAMP:20260613T140009 CREATED:20230123T090003Z LAST-MODIFIED:20230809T182009Z UID:10000597-1678982400-1678984200@micde.umich.edu SUMMARY:PhD Seminar: Xingmin Wang DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nXingmin Wang\, PhD Candidate\, Civil and Environmental Engineering and Scientific Computing\nXingmin Wang is currently a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Michigan\, Ann Arbor\, advised by Professor Henry Liu. He obtained his bachelor’s degree in the school of vehicle and mobility from Tsinghua University\, in 2018. His research interests include traffic state estimation and traffic network optimization with connected and automated vehicles. \nTraffic signal optimization with connected vehicle trajectories\nTraffic signal retiming is one of the most cost-effective methods for reducing congestion and energy consumption in urban areas based on the existing road infrastructure. However\, high installation and maintenance costs of vehicle detectors have prevented the widespread implementation of adaptive traffic control systems (ATSC). Therefore\, most intersections are still controlled by fixed-time traffic signals which are not updated regularly due to the lack of traffic monitoring capabilities. In the past few years\, vehicle trajectory data has become increasingly available and offers many advantages over detectors and other infrastructure-based sensors for traffic monitoring; but using such data for automatic traffic signal diagnosis and optimization at scalable implementable levels is relatively unexplored. To fill this gap\, this work proposes Optimizing Traffic Signals as a Service (OSaaS)\, an integrated traffic signal re-timing system that uses vehicle trajectories as the main input. OSaaS addresses many of the current challenges relating to signal retiming with trajectory data such as incomplete observation due to limited penetration rates. The system builds a queueing model that reconstructs the overall average traffic state\, calibrated from performance measurements directly obtained from vehicle trajectories. The calibrated queueing model then predicts and evaluates network performance under different traffic signal parameters to provide diagnostics and direct traffic signal retiming guidance. In April 2022\, a citywide field test of OSaaS was conducted in Birmingham\, Michigan\, with 34 signalized intersections. This resulted in decreases in both the delay and number of stops by up to 20% and 30%\, respectively. OSaaS provides a more scalable\, sustainable\, resilient\, and efficient solution to traffic signal retiming without requiring any additional infrastructure through the exclusive utilization of currently available trajectory data. As a result\, it presents the possibility of upgrading all existing fixed-time traffic signals to dynamic systems with periodical parameter updates\, something that is not currently possible without significant investments in infrastructure-based traffic flow sensors. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-xingmin-wang/ LOCATION:Venue TBA\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Wang-1.png GEO:42.3053253;-83.6694169 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230309T163000 DTEND;TZID=America/Detroit:20230309T170000 DTSTAMP:20260613T140009 CREATED:20230209T090003Z LAST-MODIFIED:20230809T184654Z UID:10000600-1678379400-1678381200@micde.umich.edu SUMMARY:PhD Seminar: Jiahao Shi DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nJiahao Shi\, PhD Candidate\, Industrial and Operations Engineering and Scientific Computing\nHe is from industrial and Operations Engineering department and is working on constrained stochastic optimization. \nAccelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction\nWe propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically\, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions\, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points\, for both constant and adaptive step size strategies. Finally\, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-jiahao-shi/ LOCATION:Venue TBA\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Shi.png GEO:42.3053253;-83.6694169 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230309T160000 DTEND;TZID=America/Detroit:20230309T163000 DTSTAMP:20260613T140009 CREATED:20230209T090003Z LAST-MODIFIED:20260417T164122Z UID:10000596-1678377600-1678379400@micde.umich.edu SUMMARY:PhD Seminar: Kashvi Srivastava DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nKashvi Srivastava\, PhD Candidate\, Applied and Interdisciplinary Mathematics and Scientific Computing\nKashvi Srivastava is a PhD candidate in Applied and Interdisciplinary Mathematics. Her research interests lie in the applications of nonlinear dynamics in chemical kinetics. She works on analytical and computational modeling of chemical reactions using tools from perturbation theory and bifurcation theory. \nDeterministic and Stochastic Modeling of Dynamical Systems in Chemical Kinetics\nChemical reactions are ubiquitous in nature in the form of biological and physical processes. We use nonlinear ordinary differential equations to mathematically model these processes in the deterministic regime. If a given process occurs at disparate time-scales\, we can further reduce the number of equations to obtain a quasi-steady-state approximation of the system. In this talk\, we consider a significant mechanism in chemical kinetics called the Michaelis–Menten reaction and its different quasi-steady-state reductions. We focus on the challenges faced in applying classical reduction theory on the system and the conditions under which its reductions are valid in the stochastic regime. We make use of a stochastic simulation algorithm called the Gillespie algorithm to demonstrate the accuracy of the reduced systems and to disprove a commonly-accepted qualifier for the validity of the stochastic approximation. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-kashvi-srivastava/ LOCATION:3530 Rackham\, 915 E. Washington St.\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Srivastava.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230223T163000 DTEND;TZID=America/Detroit:20230223T170000 DTSTAMP:20260613T140009 CREATED:20230123T090003Z LAST-MODIFIED:20230809T190922Z UID:10000591-1677169800-1677171600@micde.umich.edu SUMMARY:PhD Seminar: Shirlyn Wang DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nShirlyn Wang\, PhD Candidate\, Applied & Interdisciplinary Mathematics and Scientific Computing\nShirlyn Wang is a Ph.D candidate in Applied and Interdisciplinary Mathematics. Her research interest is in mathematical oncology\, the study of cancer initiation\, progression\, and therapy through data-driven mathematical models and simulations. \nModeling CTL-mediated Tumor Cell Death Mechanisms and the Activity of Immune Checkpoints in Immunotherapy\nImmunotherapy has dramatically transformed the cancer treatment landscape. Of the variety of types of immunotherapies available\, immune checkpoint inhibitors (ICIs)\, which block inhibitory signals from tumor cells and reinvigorate killing activities of immune cells\, have gained the spotlight. Although ICIs have shown promising results for some patients\, the low response rates in many cancers highlight the challenges of using immune checkpoint blockade as an effective treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms. The first process is fast-acting and perforin/granzyme-mediated\, and the second is a slower\, Fas ligand (FasL)-driven killing mechanism. There is also evidence suggesting that the preferred killing mechanism by CTLs depends on the antigenicity of tumor cells. To determine the key factors affecting responses to checkpoint blockade therapy\, we constructed an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PDL1 immune checkpoint. Specifically\, we analyzed which aspects of the tumor-immune landscape affect the response to ICIs with endpoints of tumor size and composition in the short and long term. By generating a virtual cohort with heterogeneous tumor and immune attributes\, we also simulated the therapeutic outcomes of immune checkpoint blockade in a largely diverse population. In this way\, we identified key tumor and immune characteristics that are associated with tumor elimination\, dormancy and escape. This talk will also shed light on which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-shirlyn-wang/ LOCATION:Rackham Building\, Earl Lewis Room\, 3rd Floor East\, 915 E. Washington St.\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar GEO:42.2807892;-83.7381556 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Rackham Building Earl Lewis Room 3rd Floor East 915 E. Washington St. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=915 E. Washington St.:geo:-83.7381556,42.2807892 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230223T160000 DTEND;TZID=America/Detroit:20230223T163000 DTSTAMP:20260613T140009 CREATED:20230123T090003Z LAST-MODIFIED:20230809T191036Z UID:10000599-1677168000-1677169800@micde.umich.edu SUMMARY:PhD Seminar: Pei-Hsun Huang DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nPei-Hsun Huang\, PhD Candidate\, Nuclear Engineering & Radiological Sciences and Scientific Computing\nPei-Hsun is a PhD student in Nuclear Engineering & Radiological Sciences working with Professor Annalisa Manera in the Experimental and Computational Multiphase Flow Laboratory. His project involves high-temperature two-phase heat pipe technologies. \nSimulation for the Design of Sodium Heat Pipes Bundle Test Facility for the Application of Microreactors\nThe 20 MW Special Purpose Reactor (SPR) is a heat pipe cooled microreactor that designed for electricity production in remote locations where reliable power grids are not always available. The key to SPR is the alkali metal heat pipes\, which offer entirely passive operation capacity with high mobility. Prior to deployment\, safety analysis with postulated accident scenarios is required for the licensing of SPR. To this regard\, a sufficiently accurate model is crucial to predict the behavior of heat pipes\, and high-resolution data is needed for the safety analysis of SPR. However\, the current existing heat pipe models are either oversimplified or unpractical expensive in view of the difficulty of the simulation with the wick structure and two-phase flow in the heat pipe. Therefore\, high fidelity experimental data is required for model verification in the high temperature heat pipe bundle system. The Michigan Sodium Heat Pipe bundle test facility which serves as a scale-down test facility using ten sodium heat pipes with a triangular array\, was utilized to verify the model for the licensing of SPR. In the talk\, the feasibility analysis using Computer Aided Engineering and Computational Fluid Dynamics for the design of the test facility was addressed. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-pei-hsun-huang-2/ LOCATION:Rackham Building\, Earl Lewis Room\, 3rd Floor East\, 915 E. Washington St.\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Huang.png GEO:42.2807892;-83.7381556 X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Rackham Building Earl Lewis Room 3rd Floor East 915 E. Washington St. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=915 E. Washington St.:geo:-83.7381556,42.2807892 END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230126T163000 DTEND;TZID=America/Detroit:20230126T170000 DTSTAMP:20260613T140009 CREATED:20230104T090003Z LAST-MODIFIED:20230809T191218Z UID:10000595-1674750600-1674752400@micde.umich.edu SUMMARY:PhD Seminar: Gurmeet Singh DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nGurmeet Singh\, PhD Candidate\, Aerospace Engineering and Scientific Computing\nGurmeet is a Ph.D. candidate in the Department of Aerospace Engineering. His research interests lie in the field of computational solid mechanics focusing on constitutive behavior of materials. He works in Prof. Veera Sundararaghavan’s research group\, and his PhD dissertation focuses on the multiscale modeling of vitrimers and semi-crystalline polymers. \nUnderstanding thermomechanical behavior of vitrimers using molecular dynamics simulations\nVitrimers are a special class of polymers that undergo dynamic cross-linking under thermal stimuli. Their ability to exchange covalent bonds can be harnessed to mitigate damage in a composite or to achieve recyclable composites. This work addresses the primary challenge of modeling dynamic cross-linking reactions in vitrimers during thermomechanical loading. Dynamic bond exchange reaction probability change during heating and its effect on dilatometric and mechanical response are simulated in large scale molecular dynamics (MD) simulations. Healing of damage under thermal cycling is computed with mechanical properties predicted before and after self–healing. \nSubsequently\, the model is used to simulate the creep response of the vitrimer. The results show that the vitrimers demonstrate a secondary creep response on contrary to pure epoxy. The MD simulations are able to probe the interplay between chemical reactions and the loading that results in the healing of the vitrimer under creep. The important feature that explains the difference between epoxies and vitrimers is the orientation of the crosslink bonds with respect to the loading direction. Furthermore\, it is found that the free volume that arises from tensile loads is reduced in vitrimers through dynamic bond rearrangement. The bond orientation\, however\, is preferentially chosen to be normal to the loading axis which ends up decreasing the stiffness along the loading axis\, leading to higher strain as compared to epoxies. Over longer timescales\, the increased strain leads to faster damage localization in tertiary creep where the largest void grows to a critical volume beyond which healing is no longer possible. Thus\, chemistry changes or additives that can prevent the initial realignment of dynamic bonds can be an effective strategy to mitigate creep in vitrimers. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-gurmeet-singh-2/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Singh.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230126T160000 DTEND;TZID=America/Detroit:20230126T163000 DTSTAMP:20260613T140009 CREATED:20230104T090003Z LAST-MODIFIED:20230809T191357Z UID:10000588-1674748800-1674750600@micde.umich.edu SUMMARY:PhD Seminar: Alex Hrabski DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nAlex Hrabski\, PhD Candidate\, Naval Architecture & Marine Engineering and Scientific Computing\nAlex is a PhD candidate in the department of Naval Architecture and Marine Engineering\, working in Yulin Pan’s Flow Physics and Engineering Lab to study nonlinear waves. His research seeks to leverage modern computational capabilities to explore wave turbulence theory and the physics that it seeks to describe. \nInvestigations of Wave Turbulence in Bounded Domains\nNonlinear wave systems are ubiquitous in nature\, and when many incoherent dispersive waves interact\, there is the potential for wave turbulence. Just as in hydrodynamic turbulence (HDT)\, systems in wave turbulence exhibit inter-scale energy cascades\, power-law inertial-range spectra\, and even intermittency. Unlike in HDT\, however\, a natural analytical closure for field statistics has been developed: spectral evolution in wave turbulence can be expressed as a Boltzmann-like kinetic equation. In this talk\, we will numerically probe the interplay of nonlinear strength and domain size (critical quantities to the analytical closure) in determining the behaviors of wave turbulence in a model system. Our numerical experiments demonstrate that (a) domain aspect ratio plays a key role in spectral evolution when nonlinearity is weak\, (b) that near-resonant interactions are important for the observation of kinetic behavior\, and (c) evaluations of the energy cascade can be used to investigate the wave turbulence closure. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-alex-hrabski/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2023-Winter-Hrabski.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20230112T160000 DTEND;TZID=America/Detroit:20230112T163000 DTSTAMP:20260613T140009 CREATED:20211021T140003Z LAST-MODIFIED:20230809T191524Z UID:10000550-1673539200-1673541000@micde.umich.edu SUMMARY:PhD Seminar: Ismael Mendoza DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nIsmael Mendoza\, PhD Candidate\, Physics and Scientific Computing\nIsmael is a 4th year Physics PhD student working in the area of cosmology. His research focuses on developing novel statistical and machine learning methods to analyze astronomical images from state-of-the-art telescopes. \nGitHub \nMachine Learning in Cosmology\nIn the upcoming decades\, we will have the opportunity to solve some of the biggest questions about our universe by taking advantage of the huge amounts of data produced by upcoming state-of-the-art cosmological experiments. In order to harness the full statistical power of this data\, we will need to develop scalable and accurate algorithms that can extract its maximal information. Recent advances in Machine Learning have demonstrated its ability to overcome the computational bottlenecks of traditional statistical techniques and even achieve better performance when analyzing cosmology data. In this talk\, I will give a brief overview of the open problems in cosmology\, motivate how Machine Learning (ML) could help us answer these by enabling novel analyses of upcoming cosmological surveys\, and give a specific application of ML enabling probabilistic detection and measurement of galaxy images. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-ismael-mendoza-2/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/Mendoza.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20221201T160000 DTEND;TZID=America/Detroit:20221201T170000 DTSTAMP:20260613T140009 CREATED:20211021T140003Z LAST-MODIFIED:20260522T141801Z UID:10000549-1669910400-1669914000@micde.umich.edu SUMMARY:PhD Seminar: Meichen Liu DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speakers:\nMeichen Liu\, PhD Candidate\, Earth and Environmental Science and Scientific Computing\nShe works with Professor Jeroen Ritsema in the department of Earth and Environmental Sciences. Her research involves characterizing earthquake sources as well as imaging structures in deep Earth. \nInfluence of shear wave velocity heterogeneity on SH-wave reverberation imaging of the mantle transition zone\nWe use the top-side shear wave reflection Ssds as a probe for mapping the depths of the mantle transition zone (MTZ) beneath the contiguous US. Using a common-reflection point (CRP) mapping approach\, we observe that the MTZ are about 40–50 km deeper beneath the western United States than the central-eastern United States if based on the 1-D Earth wave velocity model (Preliminary Reference Earth Model). However\, the east-to-west deepening of the MTZ disappears in the CRP image if we account for 3-D shear wave velocity variations in the mantle according to global tomography. In addition\, from spectral-element method synthetics\, we find that ray theory overpredicts the traveltime delays of the reverberations. Undulations of the MTZ are underestimated when their wavelengths are smaller than the Fresnel zones of the wave reverberations in the MTZ. Therefore\, modelling of layering in the upper mantle must be based on 3-D reference structures and accurate calculations of reverberation traveltimes. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-meichen-liu/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20221117T160000 DTEND;TZID=America/Detroit:20221117T163000 DTSTAMP:20260613T140009 CREATED:20211021T140003Z LAST-MODIFIED:20230809T191957Z UID:10000548-1668700800-1668702600@micde.umich.edu SUMMARY:PhD Seminar: Khoi Dang DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speaker:\nKhoi Dang\, PhD Candidate\, Chemistry and Scientific Computing\nKhoi is a 5th year graduate student in the Chemistry Department currently developing electronic structure theory methods in the Zimmerman Group. \nParallel Heat-bath Configuration Interaction\nThe heat-bath configuration interaction (HBCI) method is a deterministic wave function method that approaches the full CI limit at greatly reduced cost. HBCI consists of two parts: the generation of a variational wave function\, followed by a perturbative correction. This work introduces a parallel implementation that is highly scalable and overcomes the memory bottleneck of perturbation theory. The implementation demonstrates 83% parallel efficiency for the perturbative step on 32 nodes. \n\n  \nThis event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-khoi-dang/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/02/2022-Fall-Dang.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20221103T160000 DTEND;TZID=America/Detroit:20221103T170000 DTSTAMP:20260613T140009 CREATED:20211021T140003Z LAST-MODIFIED:20230809T192106Z UID:10000537-1667491200-1667494800@micde.umich.edu SUMMARY:PhD Seminar: Srihari Sundar and Vishwas Goel DESCRIPTION:The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.  \nFeatured Speakers:\nSrihari Sundar\, PhD Candidate\, Aerospace Engineering and Scientific Computing\nHari’s research interests include decarbonization of the power sector\, climate impacts\, computational modeling\, and sustainable transformation. His current research in the center for sustainable systems is focused on predicting changes in the energy system — meteorology interaction with a transition to widespread renewable energy generation. He aspires to use this to inform long term planning of reliable power systems under a changing climate while ensuring a just transition. \nLinkedIn   Twitter \nMeteorological Drivers of Resource Adequacy Failures During the Transition to a Decarbonized Power System\nIncreasing meteorological extremes and renewable penetrations could challenge resource adequacy (RA) in the electric power system\, as demonstrated by recent blackouts in California and Texas. We quantify meteorological drivers of RA in the Western U.S. power system\, and examine how these drivers change with increasing renewable penetrations. Our analysis integrates an optimization-based capacity expansion model\, stochastic RA model\, and neural-network-based self-organizing maps. We find that RA failures are driven by high pressure circulation patterns which produce positive surface temperature anomalies and negative solar radiation and wind speed anomalies. Further\, with increasing renewable penetration we find that the probability of failure attributed to patterns associated with heat waves over the region increases. \n\nVishwas Goel\, PhD Candidate\, Materials Science and Engineering and Scientific Computing\nVishwas is a Ph.D. candidate in the Department of Materials Science and Engineering. His research is primarily focused on simulating electrochemical phenomena on multiple scales. \nLinkedIn \nSimulating microgalvanic corrosion in Mg alloys using PRISMS-PF\nMagnesium and its alloys are the lightest structural metallic materials known\, and therefore\, hold vast potential for reducing the weight for various transportation modes such as airplanes\, cars\, buses\, etc. Although the alloying of Mg with elements such as Al\, Mn\, and rare earth (RE) elements is known to improve the mechanical properties of Mg\, the process is often detrimental to the corrosion performance of Mg. This increase in the corrosion rate occurs because of the micro-galvanic couple that forms between the Mg-rich phase\, which acts as an anode\, and the alloying-element-rich phase\, which acts as a cathode. \nUsing both experiments and modeling\, it has been reported that the rate of micro-galvanic corrosion in the Mg-alloys depends on the alloying element and microstructure. However\, a deeper understanding is required for quantifying the effect of microstructure characteristics such as the fraction of the two phases\, spacing between the two phases\, the geometry of the two phases\, etc.\, on the corrosion rate. This understanding is crucial for designing Mg-alloys with optimal mechanical properties and high corrosion resistance. \nTo bridge this gap in our understanding\, we perform the continuum-scale phase-field modeling of different microstructures observed in Mg-alloys. Furthermore\, we complement the modeling work with theoretical analysis\, where we develop analytical relations for studying the effect of various material and microstructural parameters on the characteristic corrosion length scale. The results from both these efforts will be summarized in our presentation. \n\n  \nThis event is part of MICDE’s Fall 2022 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu \n  URL:https://micde.umich.edu/event/phd-seminar-srihari-sundar-and-vishwas-goel/ LOCATION:Weiser Hall\, 6th Floor\, 619\, 500 Church Street\, Ann Arbor\, MI\, 48109\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series,Seminar END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210325T160000 DTEND;TZID=America/Detroit:20210325T170000 DTSTAMP:20260613T140009 CREATED:20230905T171443Z LAST-MODIFIED:20260612T015311Z UID:10000463-1616688000-1616691600@micde.umich.edu SUMMARY:PhD Seminar: Chanese Forte and Hyeon Joo DESCRIPTION:CHANESE FORTE\, GRADUATE STUDENT\, ENVIRONMENTAL HEALTH SCIENCES & SCIENTIFIC COMPUTING \nBio: Chanese is a Dual PhD student pursuing a degree in the Environmental Health Sciences and Scientific Computing. Chanese’s research interests lie in chemical exposure in agriculture workers and cellular alteration. \nASCERTAINING PESTICIDE EXPOSURE AND BIOACTIVITY USING OPEN SOURCE DATA: Pesticides are known to be harmful chemicals to human health\, however\, they are still heavily used in agriculture. Using large publicly available datasets\, this study aims to quantify pesticide exposure levels of the US general population in comparison to farmworkers. The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional study representative of the US population. NHANES was used to quantify pesticide exposure among US farmworkers and the general population who responded to NHANES. It compares and analyzes\, using regression\, the US pesticide exposure levels to the bioactivity of these same pesticides within the human body. By comparing population-level data with toxicological assay data in future projects\, we hope to create a more overarching idea of how pesticides may be affecting the body and the human population level. \nHYEON JOO\, GRADUATE STUDENT\, HEALTH INFRASTRUCTURES AND LEARNING SYSTEMS & SCIENTIFIC COMPUTING \nBio: Hyeon Joo is a second year PhD student in the Health Infrastructures and Learning Systems program of the Department of Health Learning Systems (Michigan Medical School). He completed his MS in Computer Science and Engineering\, and Master of Health Informatics from the University of Michigan\, Ann Arbor. His research focuses on developing and implementing computational data-driven algorithms\, systems or tools to help users identify gaps and make informed decisions. He loves working in the field of health care as a data scientist and a software engineer. \nEARLY PREDICTION OF HEART FAILURE USING ATTENTION MODELS USING EHR DATA: Heart Failure (HF) is a severe and progressive chronic condition affecting over 5.8 million patients with a 5-year mortality rate of 45-60% in the United States. Despite significant efforts and advanced HF management\, diagnosing HF in the early stages remains challenging due to its syndromic nature and non-specific disease presentation. In this seminar\, I will present a single attention recurrent network and a hierarchical attention convolutional neural networks to detect the early stage of HF at a tertiary hospital. I will also describe various methods of feature selection to reduce the computation time and improve the performance of the models. Lastly\, I will present the challenges of adopting models in clinical practice which leads to my next research steps. \n\nRegister via Zoom to immediately receive login information. Note: You may register and join after the event has started. \nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/phd-seminar-chanese-forte-and-hyeon-joo/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210318T160000 DTEND;TZID=America/Detroit:20210318T170000 DTSTAMP:20260613T140009 CREATED:20230905T171300Z LAST-MODIFIED:20260612T022828Z UID:10000461-1616083200-1616086800@micde.umich.edu SUMMARY:PhD Seminar: Vishwas Goel and Benjamin Yang DESCRIPTION:VISHWAS GOEL\, GRADUATE STUDENT\, MATERIALS SCIENCE AND ENGINEERING & SCIENTIFIC COMPUTING \nBio:  Vishwas is a third year Ph.D. student in the Thornton group\, Department of Materials Science and Engineering. His research involves the simulations of the continuum level or microstructure level electrochemical dynamics of energy conversion/storage devices such as batteries\, fuel cells\, etc. \nSIMULATION OF EIS IN SOFC CATHODES USING SMOOTHED BOUNDARY METHOD:  Electrochemical impedance spectroscopy is the most commonly used technique for the in-situ characterization of solid oxide fuel cells (SOFC). In this presentation\, I will discuss about a method for simulating the impedance behavior of a mixed conducting SOFC cathode with an experimentally determined microstructure. I will also share the key insights that we generated through our work. \nBENJAMIN YANG\, GRADUATE STUDENT\, BIOMEDICAL ENGINEERING & SCIENTIFIC COMPUTING \nBio:  Ben is a 4th year PhD student in Dr. Carlos Aguilar’s Lab. His research explores the molecular mechanisms that regulate cellular fate plasticity using microfluidics\, cell-cell fusion\, and single-cell sequencing techniques. \nDECONSTRUCTING METASTATIC REGULATORS USING INTERSPECIES HETEROKARYONS:  Tumor metastasis\, the spread of cancer cells to sites beyond the primary tumor\, is the primary contributor to morbidity in cancer patients. While each step of the metastatic cascade is well characterized\, the molecular mechanisms responsible for initiating the cascade remain unclear\, inhibiting the efficacy of therapeutic modalities. We revisit a century-old hypothesis that changes in metastatic potential are conferred to tumor cells through fusion with neighboring stromal cells by fusing human breast cancer cells with brain-resident mouse microglia and astrocytes. Our main objectives are to assess how aberrant fusion between malignant cells and stromal cells overrides transcriptional safeguards against metastatic progression and to explore how fusion modifies the mechanical phenotype of tumor hybrids. Achieving these goals will advance our understanding of the biological significance of fusion events in metastasis and delineate markers that can serve as therapeutic targets. \n\nRegister via Zoom to immediately receive login information. Note: You may register and join after the event has started. \nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/phd-seminar-vishwas-goel-and-benjamin-yang/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210311T160000 DTEND;TZID=America/Detroit:20210311T170000 DTSTAMP:20260613T140009 CREATED:20230905T171300Z LAST-MODIFIED:20260612T015435Z UID:10000459-1615478400-1615482000@micde.umich.edu SUMMARY:PhD Seminar: Anna Redgrave and Agnit Mukhopadhyay DESCRIPTION:ANNA REDGRAVE\, GRADUATE STUDENT\, ECOLOGY AND EVOLUTIONARY BIOLOGY & SCIENTIFIC COMPUTING \nBio: Anna Redgrave began her science career as an undergrad\, master’s student\, and lab technician studying developmental biology in zebrafish. She became fascinated by how complicated developmental systems are\, and joined the Wittkopp lab at U-M for her PhD to investigate one mechanism of complicating developmental systems: gene duplication. \nREGULATORY DIVERGENCE OF DUPLICATED GENES: Gene duplication has long been studied as a mechanism of evolution at the genetic level. Duplicated genes introduce redundant protein-coding sequence\, allowing duplicates to acquire novel functions while preserving existing functions. Gene duplication\, however\, also provides a substrate for non-protein coding\, regulatory sequence evolution. Genes are duplicated with varying levels of their native regulatory sequence intact. This prompts the question: how does the degree to which duplication preserves native regulatory sequence affect future evolutionary paths? Here\, I investigate this question by comparing the expression profiles of duplicate genes across many environments in two diverging species of yeast. \nAGNIT MUKHOPADHYAY\, GRADUATE STUDENT\, CLIMATE AND SPACE SCIENCES AND ENGINEERING & SCIENTIFIC COMPUTING \nBio: Agnit is a NASA Earth & Space Sciences Fellow at the Climate and Space Sciences and Engineering department at the University of Michigan\, with a background in Aerospace Engineering. He is co-advised by Drs. Michael Liemohn and Daniel Welling to quantify the nonlinear coupling between the Earth’s atmosphere and it’s near-plasma environment. He loves working with numerical models to assess and predict the impact of extreme natural events on life and technology. \nQUANTIFYING THE IMPACT OF THE AURORA ON SPACE WEATHER: Conjuring a captivating vista of a colourful nightsky\, the aurora borealis (Northern Lights) and australis (Southern Lights) are a byproduct of upper atmospheric ionization by charged particles (plasma) of solar origin. The near-constant drizzling of auroral plasma particles from outer space are excellent drivers of space weather activity caused by solar disruptions like flares and coronal mass ejections that can adversely affect man-made technology like GPS satellites\, electrical power grids and oil pipelines. Using a combination of physics-based models\, data regression tools\, in-situ satellite and ground-based telemetry\, we figure out what forms and drives the aurora\, how these drivers modify the aurora’s electro-chemical atmospheric modification\, and how this system could be predicted during extreme natural events. \n  \n\nRegister via Zoom to immediately receive login information. Note: You may register and join after the event has started. \nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/phd-seminar-anna-redgrave-and-agnit-mukhopadhyay/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210304T160000 DTEND;TZID=America/Detroit:20210304T170000 DTSTAMP:20260613T140009 CREATED:20230905T171259Z LAST-MODIFIED:20260403T173300Z UID:10000458-1614873600-1614877200@micde.umich.edu SUMMARY:PhD Seminar: K G & Ryan Sandberg DESCRIPTION:K G\, PSYCHOLOGY & SCIENTIFIC COMPUTING \nBio: K is a 4th year PhD candidate in Psychology and Scientific Computing. He has a Bachelors and a Masters degree in Biomedical Engineering and a Masters in Psychology. He works in the multisensory perception lab with Dr. David Brang and studies how multisensory integration occurs in the human brain and their mechanisms. \nEFFECTS OF VISUAL SPEECH ON AUDITORY SPEECH PERCEPTION: For quite some time now\, the notion of different regions in the brain being highly interconnected instead of being segregated into modules has been widely discussed. There are numerous studies that provide evidence for such an effect where distinct regions in the brain responsible for different functionalities work together to create a unified sense of reality. A case in point would be audio-visual integration\, where a person’s auditory stimuli/input is modulated by visual stimuli. One such example is the McGurk effect where the auditory component of one sound\, paired with the visual component of another sound leads to the perception of a third sound. How does this effect happen and what are the ways in which the brain handles integration of these different senses? My research explores questions such as whether the brain integrates information from two different senses in a third\, unrelated region of the brain or whether the sense of integration is just an illusion created by the modulatory effect of one sense on another. In this talk\, I would provide evidence indicating a modulatory effect of visual stimuli on auditory speech perception. Results from complimentary data obtained using two different imaging modalities including intracranial electrocortocographic recordings and functional magnetic resonance imaging would be discussed. \n  \nRYAN SANDBERG\, GRADUATE STUDENT\, APPLIED AND INTERDISCIPLINARY MATHEMATICS & SCIENTIFIC COMPUTING \nBio: I work with Robert Krasny in math and Alec Thomas in NERS on numerical methods in plasma physics\, incorporating tree codes and particle methods in plasma simulation. I also study plasma-based electron and photon acceleration. \nFARRSIGHT: A FORWARD ADAPTIVELY REFINED AND REGULARIZED SEMI-LAGRANGIAN INTEGRAL GPU- AND HEIRARCHICAL TREECODE-ACCELERATED METHOD FOR THE VLASOV-POISSON SYSTEM: We present a new forward semi-Lagrangian particle method for the Vlasov-Poisson (VP) system. Recently developed methods for the VP system include deformable particles and high-order or discontinuous-Galerkin Eulerian methods. In contrast to these\, we do not use any operator splitting and obtain the electric field by summing regularized pairwise particle interactions using a GPU-accelerated tree-code. We remesh and use adaptive mesh refinement to maintain an efficient representation of phase space. We benchmark on several standard test cases including Landau damping and the two-stream instability. We also compare the multi-threaded and single-GPU performance of the method. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nRegister via Zoom to immediately receive login details for this event. Note: You may register and join after the event has started. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/phd-seminar-kg-ryan-sandberg/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210225T160000 DTEND;TZID=America/Detroit:20210225T170000 DTSTAMP:20260613T140009 CREATED:20230905T171259Z LAST-MODIFIED:20260612T020849Z UID:10000453-1614268800-1614272400@micde.umich.edu SUMMARY:Ph.D. Seminar: Anil Yildirim & Jiale Tan DESCRIPTION:ANIL YILDIRIM\, GRADUATE STUDENT\, AEROSPACE ENGINEERING & SCIENTIFIC COMPUTING \nBio: Anil Yildirim is a PhD candidate in Aerospace Engineering and Scientific Computing. His research focuses on the development and application of robust computational tools in the context of multidisciplinary design optimization for aircraft configurations. \nROBUST AND HIGH-PERFORMANCE TOOLS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION: The development of future sustainable aircraft heavily relies on the design and integration of advanced propulsion systems. However\, the design of these systems are challenging due to the tightly coupled interactions between the aerodynamic and the propulsion disciplines. My research focuses on enabling these advanced technologies using aeropropulsive design optimization\, in which the aerodynamic and propulsion system designs are optimized in a coupled manner. In this process\, I use multiple robust and high-performance computational tools including the computational fluid dynamics (CFD) solver we have been developing in the MDO Lab at the University of Michigan. In this talk\, I will cover some recent advancements in the field of CFD-based aeropropulsive design optimization and the computational methodologies we have been using for this work. \n  \nJIALE TAN\, GRADUATE STUDENT\, EPIDEMIOLOGY & SCIENTIFIC COMPUTING \nBio: Jiale is a second year Phd student working with Prof. Rafael Meza in Epidemiology. His interest is to apply computational skills to public health challenges so that he can develop and apply modeling techniques for infectious and noninfectious diseases\, including for viral infections like HIV and HCV\, and eventually use them for modeling non-communicable diseases that disproportionately affect global health like cancer. \nMARKOV MULTISTATE TRANSITION MODEL ON ELECTRONIC NICOTINE DELIVERY SYSTEMS AND TRADITIONAL CIGARETTES: Electronic nicotine delivery systems (ENDS) have dramatically changed the landscape of tobacco products patterns in the USA since 2011. The impact of ENDS use on traditional cigarettes smoking remains a topic of considerable debate. A Markov multistate transition model was used to estimate transition rates (Hazard rate) between ENDS and cigarette use states (25 use states); never user\, non-current experimental user\, non-current regular user\, current experimental user\, and current regular user for each product. A 25×25 transition matrix was generated from this model. Parallel computations using 150 processors was used to estimate the transition rates. The Population Assessment of Tobacco and Health study\, which includes longitudinal data from 11\,475 youth of ages 12 to 24 years from 2013-2018 was used to calibrate the model. The hazard estimates show the patterns of ENDS and cigarette use experimentation and transition to regular use. Next steps will assess the impact of different sociodemographic covariates (age\, sex\, race\, education\, household income) on the estimated transition rates. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis webinar was not recorded for public distribution. \nQuestions? Email MICDE-events@umich.edu \n\n  URL:https://micde.umich.edu/event/ph-d-seminar-anil-yildirim-jiale-tan/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,hpc-events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210218T160000 DTEND;TZID=America/Detroit:20210218T170000 DTSTAMP:20260613T140009 CREATED:20230905T171258Z LAST-MODIFIED:20260612T022042Z UID:10000452-1613664000-1613667600@micde.umich.edu SUMMARY:Ph.D Seminar: Matthew Duschenes & Yi Zhu DESCRIPTION:MATTHEW DUSCHENES\, GRADUATE STUDENT\, APPLIED PHYSICS & SCIENTIFIC COMPUTING \nBio: I am in my third year of the Applied Physics & Scientific Computing Ph.D. programs\, after completing a master’s in theoretical physics in my home country of Canada. As a member of Dr. Krishna Garikipati’s Computational Physics group\, I am currently working on data driven modelling and am collaborating with several groups on applying these graph theoretic approaches to various systems of interest. \nGRAPH THEORETIC APPROACHES FOR PHYSICAL SYSTEMS: Numerical analyses of physical systems are conventionally performed using direct numerical simulations\, that have proven highly successful\, yielding high fidelity solutions to very high dimensional problems\, such as boundary value problems with upwards of tens of millions of degrees of freedom. However\, there is always a balance to be met between the desire for higher accuracy and additional physics to be modeled\, and the complexity\, interpret-ability and ease of representation of such solutions. To aid in this dilemma\, I will be introducing a novel graph theoretic approach\, allowing for lower dimensional\, reduced order models to be produced\, given small amounts of high fidelity data. In this talk I will explain how such an approach allows for an intuitive representation of the states of a systems\, and how it is possible to use a non-local calculus\, allowing for rigorous operators and equations to be defined on the graph. I will then be discussing some implementation details\, and convey the generality\, validity\, and future applications of this framework through some example results from collaborations. \nYI ZHU\, GRADUATE STUDENT\, CIVIL AND ENVIRONMENTAL ENGINEERING & SCIENTIFIC COMPUTING \nBio: Yi is a 3rd year PhD candidate in Civil and Environmental Engineering & Scientific Computation. His research focuses on simulation\, design\, and fabrication of active origami systems for engineering devices\, and is particularly focused on micro-scale shape morphing systems inspired by origami. \nSIMULATION AND DESIGN OF MICRO-ORIGAMI SYSTEMS: In this talk\, we will introduce some recent advancement in the simulation and the design of micro-origami systems. We will discuss the micro-origami structures we fabricated and the rapid simulation framework we developed to capture the behaviors of these active origami. We will focus on the simulation framework and demonstrate how we can capture the thermo-mechanically coupled folding behavior and contacts between origami panels effectively and rapidly. Finally\, we will introduce some ongoing work on extracting origami design principle with interpretable machine learning\, which demonstrates how we can use the simulation framework to create better origami design. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/ph-d-seminar-matthew-duschenes-yi-zhu/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/Detroit:20210211T160000 DTEND;TZID=America/Detroit:20210211T163000 DTSTAMP:20260613T140009 CREATED:20230905T171258Z LAST-MODIFIED:20230905T171258Z UID:10000451-1613059200-1613061000@micde.umich.edu SUMMARY:Ph.D Seminar: Saibal De\, Applied and Interdisciplinary Mathematics & Scientific Computing DESCRIPTION:Bio: Saibal De is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics. His research involves using high-performance computing and novel algorithms for accelerating physics-based simulation frameworks\, and developing faithful reduced-order models of black-box high-fidelity simulations. \nTENSOR METHODS FOR DATA COMPRESSION: With the advancement of computing software and hardware\, physics-based simulations have gained notoriety in many scientific and industrial applications due to their highly accurate prediction capabilities. However\, in addition to being computationally expensive\, even a single of these high-fidelity simulations produce massive amounts of data. Storing and processing all these data thus requires novel approaches. In this talk\, I will present how we can use tensor factorization methods for compressing scientific data\, leading to dramatic savings in disk-space usage. Towards the end of the talk\, I’ll also touch upon how we can potentially construct reduced-order models out of these compressed datasets. \n\nThis event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu URL:https://micde.umich.edu/event/ph-d-seminar-saibal-de-applied-and-interdisciplinary-mathematics-scientific-computing/ LOCATION:Zoom Event\, MI\, United States CATEGORIES:Featured Events,MICDE PhD Seminar Series ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/Headshot-Saibal-De.jpg END:VEVENT END:VCALENDAR