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X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240419T110000
DTEND;TZID=America/Detroit:20240419T120000
DTSTAMP:20260605T115526
CREATED:20240412T011911Z
LAST-MODIFIED:20240412T140752Z
UID:10000680-1713524400-1713528000@micde.umich.edu
SUMMARY:MICDE Fellowships Information Session
DESCRIPTION:Applications for the $4\,500 2024-2025 MICDE Graduate Fellowships will open May 1\, 2024. Please join this Zoom session to learn more about the fellowships and how to submit a great application! \nThese fellowships are sponsored by the Michigan Institute for Computational Discovery & Engineering. For more information\, see https://live-umor-micde.pantheonsite.io/academic-programs/fellowships/.
URL:https://micde.umich.edu/event/livestream-virtualmicde-fellowships-information-session/
LOCATION:Zoom Event
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Astronomy,big data,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Modeling,Computational Science,Computational Social Science,computer science,Data Science,Earth And Environmental Sciences,Ecology,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Engineering Academic Calendar,Epidemiology,Evolutionary Biology,Fellowship,Free,Funding,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,Health Data,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Life Science,Lsaresearch,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Natural Language Processing,Natural Sciences,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,parallel computing,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scholarship,Science,Scientific Computing,Sciml,Sociology,Statistics,Virtual,Webcast,Workshops
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/04/Fellowships-2024-info-session.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240708T090000
DTEND;TZID=America/Detroit:20240726T160000
DTSTAMP:20260605T115526
CREATED:20240611T033416Z
LAST-MODIFIED:20240611T125750Z
UID:10000684-1720429200-1722009600@micde.umich.edu
SUMMARY:SciFM24 Summer School
DESCRIPTION:The SciFM Summer School runs from July 8 to July 26. The first instance is going to be run in parallel at the University of Michigan (UM) and at Argonne National Laboratory (ANL). The focus will be on developing technical plans for specific instances for SciFMs\, a hackathon to jumpstart the construction of SciFMs\, as an incubator for ideas\, a training ground for researchers and students\, and further a vision for a national SciFM ecosystem.
URL:https://micde.umich.edu/event/workshop-seminarscifm24-summer-school/
CATEGORIES:Aerospace Engineering,College Of Engineering,Computational Science,Engineering,Micde,Scientific Computing,Sciml,Workshops
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/06/Untitled-design-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240925T150000
DTEND;TZID=America/Detroit:20240925T170000
DTSTAMP:20260605T115526
CREATED:20240910T182804Z
LAST-MODIFIED:20240910T183153Z
UID:10000748-1727276400-1727283600@micde.umich.edu
SUMMARY:MICDE Fellow Welcome Event
DESCRIPTION:MICDE is organizing a welcome event for its new fellows. During this event\, we will share with you the guidelines and process for utilizing your fellowship award funds\, as well as the resources and benefits you now have access to as a part of the MICDE community of fellows. Additionally\, you will receive a professionally-taken headshot free of charge (please dress accordingly). You will also have some time to introduce yourselves and get to know one another. Sandwiches\, cookies\, and beverages will be provided. \nRSVP HERE \n2024-25 MICDE fellow cohort only!
URL:https://micde.umich.edu/event/micde-fellow-welcome-event/
LOCATION:430S Green Ct Building\, 3520 Green Ct\, Ann Arbor\, MI\, 48105\, United States
CATEGORIES:Micde
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/08/MICDE-Fellow-Welcome-Event.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20240930T150000
DTEND;TZID=America/Detroit:20240930T160000
DTSTAMP:20260605T115526
CREATED:20240920T130342Z
LAST-MODIFIED:20240920T130342Z
UID:10000750-1727708400-1727712000@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/micde-midas-graduate-information-session/
LOCATION:1100 North University Building – 1528
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241004T120000
DTEND;TZID=America/Detroit:20241004T130000
DTSTAMP:20260605T115526
CREATED:20241002T220654Z
LAST-MODIFIED:20241104T140255Z
UID:10000774-1728043200-1728046800@micde.umich.edu
SUMMARY:FSML Lecture Series: Tokenization for Chemistry by Alex Wadell\, University of Michigan
DESCRIPTION:Alex Wadell is a PhD candidate at the University of Michigan Department of Mechanical Engineering. \nTokenization for Chemistry\nMolecular Foundation Models are emerging as a powerful tool for molecular design\, material science\, and cheminformatics. By leveraging the transformer architecture\, these models attempt to learn the language of chemistry and discover robust molecular embeddings. However\, current models are constrained by tokenizers that fail to capture the full breadth of chemical space or even the periodic table of elements. In his talk\, Alex will introduce smirk\, a new tokenizer for molecular foundation models that can represent the entirety of the OpenSMILES specification. We’ll also discuss performance metrics for tokenizers and the results of Alex’s systematic evaluation of thirteen chemistry-specific tokenizers using N-gram language models as a low-cost proxy for transformer models. \nIf you are unable to attend in person but are interested\, please feel free to join virtually. \nJoin Zoom Meeting\nhttps://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1\nMeeting ID: 978 2352 7756\nPasscode: 2024
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:FSML,Micde,Sciml
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/SciML-Lectures-e1727980667262.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241007T140000
DTEND;TZID=America/Detroit:20241007T150000
DTSTAMP:20260605T115526
CREATED:20240920T130536Z
LAST-MODIFIED:20240920T130536Z
UID:10000751-1728309600-1728313200@micde.umich.edu
SUMMARY:MICDE / MIDAS Graduate Information Session
DESCRIPTION:The educational programs represented are: \n\nPhD in Scientific Computing (MICDE)\nGraduate Certificate in Computational Discovery & Engineering (MICDE)\nGraduate Certificate in Computational Neuroscience (MICDE)\nGraduate Certificate in Data Science (MIDAS)\n\nThese programs are open to all U-M graduate students with an interest in scientific computing or data science. These methodologies can have a wide range of applications – current and past students have come from a variety of home departments including Aerospace Engineering\, Applied Physics\, Biostatistics\, Biomedical Engineering\, Civil & Environmental Engineering\, Chemistry\, Chemical Engineering\, Climate and Space Sciences and Engineering\, Computational Medicine and Bioinformatics\, Ecology and Evolutionary Biology\, Earth and Environmental Sciences\, Epidemiology\, Health Behavior and Health Education\, Health Infrastructures & Learning Systems\, Information\, Industrial & Operations Engineering\, Kinesiology\, Linguistics\, Macromolecular Science & Engineering\, Math\, Molecular\, Cellular\, and Developmental Biology\, Mechanical Engineering\, Materials Science & Engineering\, Naval Architecture & Marine Engineering\, Nuclear Engineering & Radiological Sciences\, Neuroscience\, Pharmaceutical Sciences\, Physics\, Political Science\, Psychology\, Environment and Sustainability\, Sociology and Statistics.\nIf you have any questions about these programs or about the information session\, please reach out to MICDE (micde-contact@umich.edu) or MIDAS (midas-contact@umich.edu).
URL:https://micde.umich.edu/event/micde-midas-graduate-information-session-2/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Computation,Computational Modeling,Computational Science,Computational Social Science,data,Data Science,Deep Learning,Engineering,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Machine Learning,Micde,Michigan Engineering,Midas,Neuroscience,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing
ATTACH;FMTTYPE=image/gif:https://micde.umich.edu/wp-content/uploads/2023/08/Info-session.gif
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X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241014T121500
DTEND;TZID=America/Detroit:20241014T131500
DTSTAMP:20260605T115526
CREATED:20240924T215157Z
LAST-MODIFIED:20240926T204844Z
UID:10000758-1728908100-1728911700@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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-14/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241018
DTEND;VALUE=DATE:20241019
DTSTAMP:20260605T115526
CREATED:20241003T220654Z
LAST-MODIFIED:20241008T155431Z
UID:10000775-1729209600-1729295999@micde.umich.edu
SUMMARY:SciML Lecture Series- Christopher Wentland: Domain decomposition and coupling data-driven models of fluid flows
DESCRIPTION:More details will be provided soon!
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-2/
LOCATION:Venue TBA\, MI\, 48109\, United States
CATEGORIES:Micde,Sciml
GEO:42.3053253;-83.6694169
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241021T121500
DTEND;TZID=America/Detroit:20241021T131500
DTSTAMP:20260605T115526
CREATED:20240924T215157Z
LAST-MODIFIED:20240926T204936Z
UID:10000760-1729512900-1729516500@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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-2/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241022T132000
DTEND;TZID=America/Detroit:20241022T170000
DTSTAMP:20260605T115526
CREATED:20241018T223652Z
LAST-MODIFIED:20241018T223652Z
UID:10000786-1729603200-1729616400@micde.umich.edu
SUMMARY:Conference / Symposium:MICDE ACES Mini-Symposium 2024
DESCRIPTION:This year’s focus of the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium is connecting advanced algorithms\, artificial intelligence (AI)\, and high-performance computing (HPC) architectures to advance scientific discovery. The event showcases the work of the University of Michigan faculty members at the intersection of AI\, HPC\, and advanced algorithms. It also includes a panel discussion on the current trends in AI\, novel algorithms\, and computer architectures for efficient scientific applications.\nACES is an event that promotes state-of-the-art research at the University of Michigan and the current trends and hot topics in computational science and engineering. Furthermore\, it is the nucleus for increasing collaboration at the University of Michigan by offering an opportunity for faculty members to share their ideas and network during a reception. Take advantage of this exciting opportunity to connect\, learn\, and be inspired!
URL:https://micde.umich.edu/event/conference-symposiummicde-aces-mini-symposium-2024/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms\, 3rd floor
CATEGORIES:Aces,Ai In Science And Engineering,Artificial Intelligence,Computation,Computational Science,Engineering,Free,High Performance Computing,In Person,Interdisciplinary,Micde,Michigan Engineering,parallel computing,Research,Science,Scientific Computing,symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241028T121500
DTEND;TZID=America/Detroit:20241028T131500
DTSTAMP:20260605T115526
CREATED:20240924T215158Z
LAST-MODIFIED:20240926T204946Z
UID:10000762-1730117700-1730121300@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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-4/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241107T180000
DTEND;TZID=America/Detroit:20241107T190000
DTSTAMP:20260605T115526
CREATED:20241029T230120Z
LAST-MODIFIED:20241030T170447Z
UID:10000787-1731002400-1731006000@micde.umich.edu
SUMMARY:Taking the Next Step: Graduate Studies in Computation/AI for Science and Engineering at U-M
DESCRIPTION:PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master’s students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend. \nPlease register via the link: https://sessions.studentlife.umich.edu/p/track/12857 \nZoom option available after registering.
URL:https://micde.umich.edu/event/taking-the-next-step-2024/
LOCATION:GG Brown Laboratory – 2147
CATEGORIES:Aerospace Engineering,Ai In Science And Engineering,Artificial Intelligence,Astronomy,Biology,Biomedical Engineering,Biosciences,Biostatistics,Chemical Engineering,Chemistry,Civil and Environmental Engineering,Climate and Space Sciences and Engineering,College Of Engineering,Complex Systems,Computation,Computational Science,Computational Social Science,computer science,computing,Earth And Environmental Sciences,Ecology And Evolutionary Biology,Economics,Education,Electrical And Computer Engineering,Electrical Engineering and Computer Science,Engineering,Epidemiology,Free,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,High Performance Computing,Industrial and Operations Engineering,Interdisciplinary,Kinesiology,Machine Learning,Materials Science,Mathematics,Mechanical Engineering,Medicine,Micde,Michigan Engineering,Naval Architecture and Marine Engineering,Neuroscience,Nuclear Engineering and Radiological Sciences,Pharmacy,Physics,Politics,Prospective Graduate Students,Psychology,Public Health,Public Policy,Rackham,Research,Robotics,Scientific Computing,Statistics,Talk,Undergraduate,Undergraduate Students,Virtual,Workshop
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Happening@UM.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241111T121500
DTEND;TZID=America/Detroit:20241111T131500
DTSTAMP:20260605T115526
CREATED:20240924T215158Z
LAST-MODIFIED:20250107T180016Z
UID:10000764-1731327300-1731330900@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars: Vishal Subramanian / Heting Fu
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. \n\nAccelerating Fock exact exchange calculations using Tucker Tensor techniques \nDensity Functional Theory (DFT) is widely used to predict the electronic structure and properties of a broad range of materials. Although exact in theory\, DFT simulations rely on exchange-correlation (Exc) functionals that are approximated in practice. The accuracy of DFT calculations is solely dependent on the accuracy of the Exc functionals. Hybrid exchange-correlation functionals are a class of functionals that have been shown to match experimental observations more closely compared to other Exc functionals. However\, the use of hybrid Exc functionals necessitates the computation of Fock exact exchange\, significantly increasing the computational cost. Furthermore\, the nature of Fock exact exchange demands a substantial increase in memory requirements and communication across processors. The latter is a serious issue as it affects the scalability of the code\, restricting routine simulations to a few tens of atoms. In this work\, we have developed a Tucker Tensor-based approach that significantly reduces the computational cost of Fock exact exchange calculations. We have incorporated an innovative communication pattern that reduces communication without significantly increasing peak memory usage. Consequently\, we have developed a robust\, efficient\, and scalable algorithm that achieves an order-of-magnitude speedup over the current state of the art. \nVishal Subramanian (Materials Science & Engineering and Scientific Computing) \nVishal Subramanian is a PhD candidate in the Materials Science and Engineering department. He is interested in harnessing the power of linear algebra and high-performance computing to develop robust\, and efficient algorithms that can compute material properties accurately. His work with Prof. Gavini’s group developing algorithms and scalable implementations for fast density functional theory (DFT) calculations on large-scale systems earned him the 2023 Gordon Bell Prize – the highest honor given in high-performance computing. \n\nTopology Optimization for Die Casting with Nonplanar Parting Surfaces \nThis talk presents a density-based topology optimization method for the simultaneous design of die-castable geometry\, die drawing directions\, and arbitrarily nonplanar parting surface. Viewing a die casted part as a two-component system consisting of the cavities of die halves\, an arbitrarily nonplanar parting surface is represented as the boundaries between adjacent partitioned domains similar to the joints in multi-component topology optimization (MTO). The draw direction of each die half is represented as a probability distribution to avoid premature convergence\, and the undercut of a part geometry in the draw direction is evaluated using the gradient of the density field. Several numerical examples are presented to demonstrate the advantages of the inclusion of nonplanar parting surfaces as optimization variables. \nHeting Fu (Mechanical Engineering and Scientific Computing) \nHeting Fu is a Ph.D. candidate under the guidance of Professor Kazuhiro Saitou in Mechanical Engineering. His research involves multi-component\, multi-material\, and multi-process topology optimization.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-6/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/09/Vishal-Subramanian-Heting-Fu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241118T121500
DTEND;TZID=America/Detroit:20241118T131500
DTSTAMP:20260605T115526
CREATED:20240924T215200Z
LAST-MODIFIED:20241101T183341Z
UID:10000770-1731932100-1731935700@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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-12/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241202T121500
DTEND;TZID=America/Detroit:20241202T131500
DTSTAMP:20260605T115526
CREATED:20240924T215200Z
LAST-MODIFIED:20241115T184037Z
UID:10000768-1733141700-1733145300@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:Shuai Che from the Nuclear Engineering & Radiological Sciences department will be giving a talk on Thermal-sturctural analysis of printed circuit heat exchangers and supporting structures for molten salt test facility. \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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-10/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/09/2024-12-2-Updated.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241204T160000
DTEND;TZID=America/Detroit:20241204T170000
DTSTAMP:20260605T115526
CREATED:20241126T144049Z
LAST-MODIFIED:20241210T173845Z
UID:10000788-1733328000-1733331600@micde.umich.edu
SUMMARY:MICDE-Aerospace Engineering Seminar: Jan Janssen\, Scientist\, Max Planck Institute
DESCRIPTION:Bio:\nDr. Jan Janssen is the group leader for materials informatics at the Max Planck Institute for Sustainable Materials in Düsseldorf\, Germany. Previously\, he was a Director’s Postdoctoral Fellow at Los Alamos National Laboratory\, where he designed materials for fusion reactors as part of the Exascale Computing Project. In addition\, he leads the development of the open-source pyiron software package\, is a maintainer of over 900 open-source software packages for the conda-forge community and an active contributor to open-source projects on Github. \nTitle:\nHow to use machine learning in the discovery and design of materials for the future? \nAbstract:\nDesigning materials for a sustainable future requires rethinking traditional materials design\, which is centered on optimizing and fine-tuning already known alloying compositions. In a mathematical sense this can be identified as a local or global optimization in the multi-dimensional alloy phase space. To sample the whole periodic table\, already a three-component alloy with 20 temperature steps and 10 concentration steps requires a million experiments\, making it prohibitive for purely experimental approaches.\nTo address this challenge\, simulation approaches and\, more recently\, machine learning models are applied to screen the periodic table. The pyiron workflow framework developed at the Max-Planck-Institute for sustainable materials predicts new materials using ab-intio thermodynamics. Starting from the interaction of electrons\, it predicts macroscopic material properties like heat capacity\, thermal expansion\, and phase stability. Recently\, the pyiron workflow framework was extended with a large language model (LLM) interface named LangSim.\nThis raises the question: Can a LLM replace a scientist? Or how does the thought process of a scientist differ from the statistical approach of the LLM? Can the LLM make us better scientists? We benchmark the capabilities of current LLMs to design new materials using atomistic simulation. The presentation introduces ab-initio thermodynamics\, covers the importance of simulation workflows to efficiently predict sustainable materials and highlights how LLMs accelerate their discovery.
URL:https://micde.umich.edu/event/workshop-seminarmicde-aerospace-engineering-seminar-jan-janssen-how-to-use-machine-learning-in-the-discovery-and-design-of-materials-for-the-future/
LOCATION:Cooley Building – 906
CATEGORIES:Aerospace Engineering,Micde,Micde Seminar,Michigan Engineering
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/11/Jan-Janssen.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241209T121500
DTEND;TZID=America/Detroit:20241209T131500
DTSTAMP:20260605T115526
CREATED:20240924T215159Z
LAST-MODIFIED:20241115T183858Z
UID:10000767-1733746500-1733750100@micde.umich.edu
SUMMARY:Ph.D. in Scientific Computing Student Seminars
DESCRIPTION:Mahmoud Komaiha (Biomedical Engineering) will give a talk on BME and Scientific Computing. \nZhucong Xi (Materials Science & Engineering) will give a talk on Multiscale Simulations of Solute Clustering in Aluminum Alloys. \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.
URL:https://micde.umich.edu/event/workshop-seminarph-d-in-scientific-computing-student-seminars-8/
LOCATION:Undergraduate Science Building – 1250
CATEGORIES:Micde,Phd Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250117T160000
DTEND;TZID=America/Detroit:20250117T170000
DTSTAMP:20260605T115526
CREATED:20241224T044635Z
LAST-MODIFIED:20260522T182843Z
UID:10000789-1737129600-1737133200@micde.umich.edu
SUMMARY:MICDE - NERS Seminar: Teresa Bailey\, Lawrence Livermore National Laboratory
DESCRIPTION:Bio: Teresa S. Bailey is the Associate Program Director of Computational Physics in LLNL’s Weapon Simulation and Computing program. She oversees the development of multiple multiphysics simulation tools across a wide range of applications. These codes span a broad range of physics\, chemistry\, and engineering application space. As required\, the codes are production-quality software products that are portable and computationally efficient on DOE’s most advanced HPC systems. \nBailey has been an LLNL employee since 2008. She began her career as a code physicist before moving into technical leadership roles as the Deterministic Transport project leader and the Nuclear Science program group leader. Bailey earned her B.S. in Nuclear Engineering from Oregon State University in 2002. She received the DOE Computational Science Graduate Fellowship to support her graduate work and earn her Ph.D. in Nuclear Engineering from Texas A&M in 2008. \nComputational Science and High-Performance Computing at Lawrence Livermore National Laboratory
URL:https://micde.umich.edu/event/workshop-seminarmicde-ners-seminar-teresa-bailey-lawrence-livermore-national-laboratory/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:College Of Engineering,Computational Science,Micde,Micde Seminar,Michigan Engineering,Nuclear Engineering and Radiological Sciences,Physics,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/12/Teresa-Bailey-2.png
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250121T150000
DTEND;TZID=America/Detroit:20250121T160000
DTSTAMP:20260605T115526
CREATED:20250106T213237Z
LAST-MODIFIED:20250128T161655Z
UID:10000790-1737471600-1737475200@micde.umich.edu
SUMMARY:MICDE-CSE Seminar: Andrew Appel\, Professor\, Princeton University
DESCRIPTION:Bio: Andrew Appel is Eugene Higgins Professor Computer Science\, and served from 2009-2015 as Chair of Princeton’s CS department. His research is in software verification\, computer security\, programming languages and compilers\, and technology policy. He received his A.B. summa cum laude in physics from Princeton in 1981\, and his Ph.D. in computer science from Carnegie Mellon University in 1985. Professor Appel has been editor in chief of ACM Transactions on Programming Languages and Systems and is a fellow of the ACM (Association for Computing Machinery). He has worked on fast N-body algorithms (1980s)\, Standard ML of New Jersey (1990s)\, Foundational Proof-Carrying Code (2000s)\, and the Verified Software Toolchain (2010-present). \nFormally Verified Numerical Methods\nAbstract: Formal machine-checked program verification uses mechanized logical tools to connect low-level programs to the specifications of the algorithms they are supposed to implement. The same program verification tools can work in many application domains. But it’s not enough just to implement an algorithm; the program is fully “correct” only if the algorithm (provably) computes an answer to the problem or question of interest. Proofs of algorithm correctness rely on the mathematics of the application domains\, and each domain has its own mathematics.\nIn recent years we have applied this method to numerical methods (algorithms for scientific computing) and numerical analysis (reasoning about the accuracy of those methods)\, with machine-checked proofs formally connected to low-level program-correctness proofs. I will discuss the results of the numerical integration of differential equations and the solving of linear systems. Some of these results are joint work with Ariel Kellison and David Bindel (Cornell)\, Mohit Tekriwal and Jean-Baptiste Jeannin (Michigan).
URL:https://micde.umich.edu/event/micde-cse-seminar-andrew-appel-professor-princeton-university/
LOCATION:BBB 3725\, 2260 Hayward St.\, Ann Arbor\, United States
CATEGORIES:Micde,Micde Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250128T160000
DTEND;TZID=America/Detroit:20250128T170000
DTSTAMP:20260605T115526
CREATED:20241011T222157Z
LAST-MODIFIED:20260522T151639Z
UID:10000780-1738080000-1738083600@micde.umich.edu
SUMMARY:MICDE Seminar: Joshua Dolence\, Research Scientist\, Los Alamos National Lab
DESCRIPTION:Bio: Josh Dolence is a scientist in the Computational Physics & Methods Group at Los Alamos National Laboratory and a founding member of the LANL Michigan SPARC\, a permanent LANL presence at U-M in Ann Arbor. Before joining LANL\, he received a PhD in Astronomy from UIUC in 2011 and spent three years in Astrophysical Sciences at Princeton University where he worked in computational astrophysics\, studying topics like black hole accretion and supernovae. More recently\, he leads the Methods for Multiscale\, Multiphysics Accelerated Prediction project for LANL’s Advanced Simulation and Computing Program\, focusing efforts on enabling unprecedented fidelity and scale in modeling complex systems like high energy density physics experiments. \nParthenon: a flexible framework for rapid development of performance portable multiphysics codes\nAbstract: In many areas of computational science\, developing new\, state-of-the-art capabilities has become a high-cost\, risky proposition. The complexity and diversity of models\, methods\, algorithms\, and machines often lead to fundamental challenges in designing and building codes that enable advances in science and engineering. In fields like high energy density physics and astrophysics\, multiphysics simulations leveraging adaptive meshes\, particles\, and a variety of numerical methods are foundational to progress but difficult to realize performantly on ever-evolving high-performance computing platforms. In this talk\, I will present the Parthenon framework\, an open-source code base that aims to facilitate the development of highly adaptive\, multiphysics codes that are fast\, scalable\, and capable of leveraging modern platforms with both CPUs and GPUs. I will describe the basic principles behind its design and some of its most enabling features and highlight the ~10 downstream codes it already supports. \n 
URL:https://micde.umich.edu/event/joshua-dolence/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Joshua-Dolence-LANL.png
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250204T120000
DTEND;TZID=America/Detroit:20250204T130000
DTSTAMP:20260605T115526
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250206T150000
DTEND;TZID=America/Detroit:20250206T160000
DTSTAMP:20260605T115526
CREATED:20241011T222159Z
LAST-MODIFIED:20260522T151605Z
UID:10000781-1738854000-1738857600@micde.umich.edu
SUMMARY:MICDE-IOE Seminar: Jong-Shi Pang\, Professor\, University of Southern California
DESCRIPTION:Bio: Elected a member of the National Academy of Engineering in February 2021 and appointed a Distinguished Professor in April 2023\, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position\, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A. Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007. He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003\, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas\, and an Assistant and then an Associate Professor in the Graduate School of Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full-time) and 2002 (part-time)\, he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences. \nHeaviside Composite Optimization\, a new paradigm of optimization\nAbstract: This talk introduces the topic of Heaviside composite optimization and briefly covers its many facets: breadth in modeling\, roles in old and new applications\, theory of optimizers and stationary solutions\, bridge with discrete optimization\, and the progressive integer programming method. By definition\, a univariate Heaviside function is the (discontinuous) indicator of an interval. By its name\, a Heaviside composite function is the composition of a Heaviside function with a continuous multivariate function that may be nonconvex and nondifferentiable. While very natural in modeling many physical phenomena\, a Heaviside composite optimization problem\, possibly with Heaviside composite functional constraints\, has never been formally studied. Our work aims to fill this void with a comprehensive research program covering the applications\, theory\, and algorithms for this novel class of very challenging optimization problems. \nThis research has benefitted from previous collaboration with Ying Cui (UC Berkeley)\, Yue Fan (CUHK-SZ)\, Shaoning Han (NUS)\, Junyi Liu (Tsinghua)\, and Xinyao Zhang (USC)\, and is presently being organized in a monograph co-authored with Junyi Lui.
URL:https://micde.umich.edu/event/jong-shi-pang/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Jong-Shi-Pang.png
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor LEC 3213ABC 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250211T120000
DTEND;TZID=America/Detroit:20250211T130000
DTSTAMP:20260605T115526
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250225T120000
DTEND;TZID=America/Detroit:20250225T130000
DTSTAMP:20260605T115526
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250311T120000
DTEND;TZID=America/Detroit:20250311T130000
DTSTAMP:20260605T115526
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250314T120000
DTEND;TZID=America/Detroit:20250314T130000
DTSTAMP:20260605T115526
CREATED:20250311T164812Z
LAST-MODIFIED:20250311T164812Z
UID:10000815-1741953600-1741957200@micde.umich.edu
SUMMARY:Workshop / Seminar:Frontiers in Scientific Machine Learning (FSML) Seminar: Alexander Tong (Post-doctoral Fellow\, Mila - Quebec AI Institute)
DESCRIPTION:Abstract:\nGenerative flow models learn a (possibly stochastic) mapping between source and target distributions. Common paradigms include diffusion models\, score matching models\, and continuous normalizing flows. In this talk I will first present methods for improved training of flow models using flow matching objectives using ideas from optimal transport. I will then show how these improved methods can be applied to the tasks of (1) modelling cell dynamics\, which allow us to better understand disease programs –leading to a new potential therapeutic pathway for triple-negative breast cancer and (2) generative protein design\, with applications to biologic drug discovery.\nBio:\nAlexander Tong is a postdoctoral fellow at Mila with Yoshua Bengio\, visiting researcher at Oxford with Michael Bronstein\, cofounder of Dreamfold—a protein design startup\, and incoming assistant professor at Duke University starting July 2025. Alex completed his Ph.D. in Computer Science at Yale University in 2021 with Smita Krishnaswamy. His research interests span generative modeling\, graph signal processing\, and optimal transport to understand biological systems with a focus on cells and proteins.
URL:https://micde.umich.edu/event/workshop-seminarfrontiers-in-scientific-machine-learning-fsml-seminar-alexander-tong-post-doctoral-fellow-mila-quebec-ai-institute/
LOCATION:GG Brown Laboratory – 1642
CATEGORIES:Ai In Science And Engineering,Artificial Intelligence,Micde
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250318T120000
DTEND;TZID=America/Detroit:20250318T130000
DTSTAMP:20260605T115526
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:20250320T153000
DTEND;TZID=America/Detroit:20250320T163000
DTSTAMP:20260605T115526
CREATED:20241011T222200Z
LAST-MODIFIED:20250120T170935Z
UID:10000782-1742484600-1742488200@micde.umich.edu
SUMMARY:MICDE-EECS Seminar: Mikhail Belkin\, Professor\, University of California San Diego
DESCRIPTION:Bio: Mikhail Belkin is a Professor at Halicioglu Data Science Institute and Computer Science and Engineering Department at UCSD and an Amazon Scholar. Prior to that he was a Professor at the Department of Computer Science and Engineering and the Department of Statistics at the Ohio State University. He received his Ph.D. from the Department of Mathematics at the University of Chicago (advised by Partha Niyogi). His research interests are broadly in theory and applications of machine learning\, deep learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps\, Graph Regularization and Manifold Regularization algorithms\, which brought ideas from classical differential geometry and spectral graph theory to data science. His more recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. The empirical evidence necessitated revisiting some of the classical concepts in statistics and optimization\, including the basic notion of over-fitting. One of his key findings has been the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. His recent work focusses on understanding feature learning and over-parameterization in deep learning. Mikhail Belkin is an ACM Fellow and a recipient of a NSF Career Award and a number of best paper and other awards. He had served on the editorial boards of IEEE Proceedings on Pattern Analysis Machine Intelligence and the Journal of the Machine Learning Research. He is the editor-in-chief of SIAM Journal on Mathematics of Data Science (SIMODS). \nEmergence and grokking in “simple” architectures\nAbstract: In recent years\, transformers have become a dominant machine learning methodology.\nA key element of transformer architectures is a standard neural network (MLP). I argue that MLPs alone already exhibit many remarkable behaviors observed in modern LLMs\, including emergent phenomena. Furthermore\, despite large amounts of work\, we are still far from understanding how 2-layer MLPs learn relatively simple problems\, such as “grokking” modular arithmetic. I will discuss recent progress and argue that feature-learning kernel machines (Recursive Feature Machines) isolate some key computational aspects of modern neural architectures and are preferable to MLPs as a model for analysis of emergent phenomena.
URL:https://micde.umich.edu/event/mikhail-belkin/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Micde,Micde Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250401T120000
DTEND;TZID=America/Detroit:20250401T130000
DTSTAMP:20260605T115526
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250401T160000
DTEND;TZID=America/Detroit:20250401T170000
DTSTAMP:20260605T115526
CREATED:20250308T043515Z
LAST-MODIFIED:20250310T172100Z
UID:10000812-1743523200-1743526800@micde.umich.edu
SUMMARY:Scientific Computing in the Physical Sciences information session
DESCRIPTION:Learn about academic opportunities and fellowships for graduate students who combine Scientific Computing with Applied Physics\, Astronomy\, Biophysics\, Chemistry\, Earth and Environmental Sciences\, Math\, Physics\, or any other physical science. \nThis session will be offered in-person and on Zoom. Please indicate how you plan to attend when you register. \nRegister to attend
URL:https://micde.umich.edu/event/presentationscientific-computing-in-the-physical-sciences/
LOCATION:Weiser Hall – 170
CATEGORIES:Applied Physics,Astronomy,Biophysics,Chemistry,Computation,Computational Modeling,Computational Science,computing,Earth And Environmental Sciences,Environment,Generative Ai,Graduate,Graduate and Professional Students,Graduate School,Graduate Students,In Person,Interdisciplinary,Life Science,Machine Learning,Mathematics,Micde,Natural Sciences,Physics,Prospective Graduate Students,Rackham,Research,Science,Scientific Computing,Virtual
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