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X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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DTSTART;TZID=America/Detroit:20230309T160000
DTEND;TZID=America/Detroit:20230309T163000
DTSTAMP:20260618T142736
CREATED:20230209T090003Z
LAST-MODIFIED:20260417T164122Z
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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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230309T163000
DTEND;TZID=America/Detroit:20230309T170000
DTSTAMP:20260618T142736
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230316T160000
DTEND;TZID=America/Detroit:20230316T163000
DTSTAMP:20260618T142736
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:20230316T163000
DTEND;TZID=America/Detroit:20230316T170000
DTSTAMP:20260618T142736
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
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