BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Michigan Institute for Computational Discovery and Engineering - ECPv6.17.1//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:20160313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20161106T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20170312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20171105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20180311T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20181104T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20190310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20191103T060000
END:STANDARD
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
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20260529T110000
DTEND;TZID=America/Detroit:20260529T120000
DTSTAMP:20260601T195905Z
CREATED:20260514T175620Z
LAST-MODIFIED:20260601T195905Z
UID:10000862-1780052400-1780056000@micde.umich.edu
SUMMARY:MICDE - Mechanical Engineering seminar: Phani Motamarri\, Indian Institute of Science\, Bangalore
DESCRIPTION:Bio: Phani Motamarri is an Assistant Professor in the Department of Computational and Data Sciences at the Indian Institute of Science\, Bengaluru\, where he leads the MATRIX Lab. He is an alumnus of the University of Michigan–Ann Arbor\, where he earned his PhD in Mechanical Engineering.\nHis research lies at the intersection of computational mechanics\, materials science\, numerical analysis\, and high-performance computing. His work focuses on developing mathematical techniques and hardware-aware algorithms for quantum modeling of materials\, with applications to structural and functional materials and multiscale modeling methodologies. He is also interested in machine learning frameworks for accelerating materials discovery and quantum computing\, particularly in the context of quantum-centric supercomputing. \nProf. Motamarri’s research contributions include advances in finite-element methods\, numerical analysis\, and large-scale scientific software development. He is one of the lead developers of DFT-FE\, an open-source\, massively parallel finite-element code for density functional theory calculations. He received the ACM Gordon Bell Prize in 2023 and was a finalist for the ACM Gordon Bell Prize in 2019. \nInexact yet Accurate: Unlocking Quantum Modeling of Materials at Scale through Approximation-Tolerant Algorithms\nAbstract:  Modern computing architectures increasingly rely on iterative solvers that employ reduced-precision computation and communication-reduction techniques to lower time-to-solution and improve scalability. However\, eigensolvers in scientific simulations have struggled to exploit such approximations without compromising accuracy. We present an eigensolver R-ChFSI\, a residual-based reformulation of Chebyshev Filtered Subspace Iteration (ChFSI) provably tolerant to inexact matrix–vector products. By expressing the Chebyshev recurrence in terms of residuals rather than eigenvector estimates\, R-ChFSI naturally accommodates multiple sources of approximation\, including reduced-precision arithmetic (FP32 and TF32) in the filtering step\, lossy compression with compression ratios exceeding 4x for inter-process communication\, and approximate inverses for generalized eigenproblems\, while preserving eigensolver robustness. Large-scale experiments on GPU accelerators are conducted using finite-element discretized generalized eigenproblems arising in Kohn–Sham density functional theory for quantum modeling of materials. The results demonstrate that R-ChFSI achieves eigen-residual norms orders of magnitude smaller than standard ChFSI under comparable inexactness\, while delivering substantial performance gains. This work provides a practical pathway toward approximation-tolerant eigensolvers enabling accurate and scalable simulations on modern computing architectures. \n\nThe MICDE 2025-26 Seminar Series is open to all. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-me-seminar-phani-motamarri-iisc/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:College Of Engineering,Computational Science,Featured Events,Graduate Students,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2026/05/Phani-Motamarri.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250320T153000
DTEND;TZID=America/Detroit:20250320T163000
DTSTAMP:20250120T170935Z
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
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2024/10/Mikhail-Belkin-2.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220405T150000
DTEND;TZID=America/Detroit:20220405T160000
DTSTAMP:20260522T182816Z
CREATED:20220111T193640Z
LAST-MODIFIED:20260522T182816Z
UID:10000552-1649170800-1649174400@micde.umich.edu
SUMMARY:MICDE Seminar: Douglas Spearot\, Professor of Mechanical & Aerospace Engineering\, University of Florida
DESCRIPTION:WATCH THE RECORDING HERE.\nBio: Dr. Douglas Spearot is a Newton C. Ebaugh Professor in the Department of Mechanical & Aerospace Engineering in the Herbert Wertheim College of Engineering at the University of Florida. He also holds an affiliate appointment in the Department of Materials Science & Engineering. From 2005-2015\, he was an Assistant/Associate Professor in the Department of Mechanical Engineering and a member of the Institute for Nanoscience and Engineering at the University of Arkansas. His research focuses on the use of atomistic and mesoscale simulation techniques to study the mechanical and thermodynamic properties of materials\, with particular focus on the behavior of dislocations and interfaces\, and the development of computational tools to extract experimentally relevant metrics from simulation generated data. Dr. Spearot received his B.S. in Mechanical Engineering from the University of Michigan\, and his M.S. and Ph.D. in Mechanical Engineering from the Georgia Institute of Technology. \nAwards: \n\n2010 NSF CAREER Award to elucidate the nanoscale mechanisms associated with phase selection during vapor deposition.\n2007 Ralph E. Power Junior Faculty Enhancement Award to study plasticity in nanostructured materials.\n2020 Teacher of the Year in the Department of Mechanical & Aerospace Engineering\, University of Florida.\n2014 College of Engineering Imhoff Outstanding Teaching Award\, University of Arkansas.\n2014 Arkansas Alumni Association Rising Teaching Award\, University of Arkansas.\n\nMesoscale Modeling of Plasticity in Metallic Materials via Advancement of the Discrete Dislocation Dynamics Simulation Method\nPlastic deformation in metallic materials is governed by the individual and collective behaviors of defects\, such as dislocations and grain boundaries (GBs). Among computational methods for modeling this inherently multi scale problem\, discrete dislocation dynamics (DDD) is a powerful mesoscale technique that explicitly simulates the dynamics and interactions of dislocations and provides a continuum-level understanding of plasticity. Yet\, the utility of DDD simulations for certain problems is compromised by missing defect physics and limited linkages to experiments. The focus of this seminar will be on two advancements to the DDD method. First\, a disclination-dislocation framework for modeling the mechanical structure of equilibrium GBs (EGBs) and nonequilibrium GBs (NEGBs) is incorporated into the DDD method. This approach accounts for the mechanical and kinetic effects of multiple transmission events\, and the absorption of residual dislocations at the GB. DDD simulations reveal that accumulated dislocation content from prior slip transmission lowers the external driving stresses required for subsequent slip transmission\, indicating GB softening. Second\, to enhance the connection between DDD simulations and experiments\, a new “virtual” diffraction method is developed to generate strain-broadened diffraction profiles from DDD microstructures. This method is used to generate a database of diffraction profiles from simulated dislocation microstructures\, which enables a new data-driven approach for dislocation density prediction from diffraction line profile analysis. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in the mechanical and thermodynamic properties of materials are encouraged to attend. \nDr. Spearot will be hosted by Dr. Yue Fan\, Assistant Professor of Mechanical Engineering. \nThis is a hybrid event and will be held in-person and broadcast online via Zoom.  \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-douglas-spearot-phd-professor-of-mechanical-aerospace-engineering-university-of-florida/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/01/Douglas-Spearot.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20181126T150000
DTEND;TZID=America/Detroit:20181126T160000
DTSTAMP:20230905T171421Z
CREATED:20230905T171421Z
LAST-MODIFIED:20230905T171421Z
UID:10000162-1543244400-1543248000@micde.umich.edu
SUMMARY:CANCELLED --MICDE Seminar: Ali Yilmaz\, Electrical Engineering\, University of Texas at Austin
DESCRIPTION:CANCELLED\nBio: Ali Yilmaz is an Associate Professor of Electrical and Computer Engineering and a core faculty member at the Institute for Computational Engineering and Sciences at the University of Texas at Austin. \nDr. Yilmaz received the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2005. He spent 2005 to 2006 as a post-doctoral research associate with the Center for Computational Electromagnetics at the University of Illinois; in 2006\, he joined the faculty of The University of Texas at Austin. \nHis research interests include computational electromagnetics (particularly fast frequency- and time-domain integral equation solvers)\, parallel algorithms\, antenna and scattering analysis\, bioelectromagnetics\, geoelectromagnetics\, and electronic packages. He has authored or co-authored over 170 papers in refereed journals and international conferences on these topics. \nUSING (SUPER) COMPUTERS JUDICIOUSLY FOR HIGHER FIDELITY ELECTROMAGNETIC ANALYSIS\nIncreasing the fidelity of the electromagnetic models generally increases the predictive power of the analyses based on the models. It also generally increases the results’ sensitivity to model features/parameters as well as the difficulty of constructing the models\, accurately solving the governing equations\, and interpreting the resulting data. Therefore\, one should base the analysis on the lowest-fidelity model one can get away with or\, equivalently\, the highest-fidelity model one can afford. The sweet spot for the tradeoff\, “the appropriate model”\, has changed over time in part because past successes in simulation-based science and engineering have increased expectations/requirements from electromagnetic analysis and in part because tremendous improvements in computing infrastructure and advances in computational methods have increased the affordability of complex analysis. Finding the appropriate model requires understanding both the benefits and the costs of analysis when a lower- or higher-fidelity model is used; neither side of the ledger\, however\, is known beforehand (unless one is repeating previously established analyses). A possible approach to revealing these unknowns is to construct models by gradually increasing their fidelity\, performing analysis at each fidelity level\, and comparing the analysis results and costs to those from the previous steps. I will show examples of this “analysis-driven modeling” in bioelectromagnetics (using the AustinMan and AustinWoman human body models) and signal integrity (using an electronic package example) by employing parallel algorithms and advanced integral-equation solvers on leading-edge supercomputers. \nThe examples will highlight many of the challenges arising from this approach to modeling. An important one is that “the appropriate method” of analysis generally depends on the model\, e.g.\, a method can outperform alternatives for low-fidelity models but underperform them for high-fidelity ones; indeed\, inappropriate (but convenient) methods can not only inflate the cost side of the ledger but also deflate the benefit side\, leading to misjudgment of the appropriate model fidelity. Thus\, not surprisingly\, the development of appropriate electromagnetic models and appropriate computational methods are tightly linked (aka “if all you have is a hammer\, everything looks like a nail”). Unfortunately\, evaluating computational methods to find the appropriate one for a given model is surprisingly difficult\, even for unbiased experts\, as method performances depend not just on the models but also on the computers\, the software realizations of the methods\, and the users/developers of the software. On the one hand\, theoretical comparisons (e.g.\, of asymptotic complexities\, error convergence rates\, parallel scalability limits) are often incapable of factoring in the large impact of software and hardware infrastructure on the realized/observed performance of a computational method—a problem that has worsened as the traditional Dennard scaling of clock frequencies ended in the last decade. On the other hand\, empirical comparisons are beset by the same problems that physical measurements face (including irreproducible and uncertain results)\, require many (potentially low-efficiency) computations\, and suffer from the large number of alternative methods. I will discuss whether benchmark suites can improve the judicious use of computational methods for electromagnetic analysis and what the necessary ingredients for such benchmarks are. \nProf. Yilmaz is being hosted by Prof. Michielssen (EECS). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Yilmaz for lunch\, please fill out this form.
URL:https://micde.umich.edu/event/micde-seminar-ali-yilmaz-electrical-engineering-university-of-texas-at-austin/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2019/07/Ali-Yilmaz.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20180406T150000
DTEND;TZID=America/Detroit:20180406T160000
DTSTAMP:20230905T171419Z
CREATED:20230905T171419Z
LAST-MODIFIED:20230905T171419Z
UID:10000138-1523026800-1523030400@micde.umich.edu
SUMMARY:CEE/MICDE Seminar: Khachik Sargsyan\, Sandia National Laboratories
DESCRIPTION:Bio: Khachik Sargsyan is a Principal Member of Technical Staff at Sandia National Laboratories (SNL) in Livermore\, CA. Before staff and postdoctoral positions at SNL\, he received his Ph.D. in Applied and Interdisciplinary Mathematics from University of Michigan\, Ann Arbor\, in 2007. His Bachelors degree\, awarded in 2002\, is in Applied Math and Physics from Moscow Institute of Physics and Technology. Dr. Sargsyan’s research evolves around uncertainty quantification (UQ) and predictability analysis of physical and computational models. He has developed and applied methods for model reduction\, UQ and data assimilation\, targeting fundamental challenges such as structural errors\, intrinsic stochasticity\, high-dimensionality\, limited data\, discontinuities and rare events\, with applications in climate modeling\, chemical kinetics\, hardware architecture simulators and turbulent combustion. He is one of the lead developers of UQTk (www.sandia.gov/uqtoolkit)\, a lightweight C++/Python software toolkit for quantification of uncertainties in model predictions.\n \nDr. Sargsyan is being hosted by Prof. Ivanov (Civil and Env. Engineering). If you would like to meet him\, please send an email to Chase Dwelle at dwellem@umich.edu \nProbabilistic Methods for Uncertainty Quantification in Computational Models\nOver the last decade\, improved measurement capabilities and computational resources have led to significant algorithmic developments toward efficient uncertainty quantification (UQ) for computational models. Such models of physical systems often involve input parameters that exhibit certain degree of uncertainty. Estimation and propagation of these uncertainties are crucial for model validation\, computational/experimental design and decision making. ​This talk will focus on probabilistic methods with emphasis on Polynomial Chaos (PC) expansions as a means for functional representation of random variables. The talk will highlight the use of PC methods both for forward propagation of uncertainties and for inverse problems\, such as parameter estimation via Bayesian inference. I will list associated major challenges\, including the curse of dimensionality and model structural error estimation\, in the context of computationally expensive models of physical systems. Both fundamental and more recent methods will be introduced and demonstrated\, impacting a wide range of applications\, such as climate modeling\, turbulent combustion and chemical kinetics.
URL:https://micde.umich.edu/event/cee-micde-seminar-khachik-sargsyan-sandia-national-laboratories/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2018/03/Khachik-Sargsyan.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171201T180000
DTEND;TZID=America/Detroit:20171201T190000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000114-1512151200-1512154800@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop/2017-12-01/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171124T180000
DTEND;TZID=America/Detroit:20171124T190000
DTSTAMP:20230905T171416Z
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000622-1511546400-1511550000@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-7/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171117T180000
DTEND;TZID=America/Detroit:20171117T190000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000621-1510941600-1510945200@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-2/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171110T180000
DTEND;TZID=America/Detroit:20171110T190000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000620-1510336800-1510340400@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-3/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171103T180000
DTEND;TZID=America/Detroit:20171103T190000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000619-1509732000-1509735600@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-4/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171027T180000
DTEND;TZID=America/Detroit:20171027T190000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000618-1509127200-1509130800@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-5/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171020T180000
DTEND;TZID=America/Detroit:20171020T190000
DTSTAMP:20230905T171415Z
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000617-1508522400-1508526000@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-6/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171013T180000
DTEND;TZID=America/Detroit:20171013T190000
DTSTAMP:20230905T171438Z
CREATED:20230905T171438Z
LAST-MODIFIED:20230905T171438Z
UID:10000616-1507917600-1507921200@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-8/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171006T180000
DTEND;TZID=America/Detroit:20171006T190000
DTSTAMP:20230905T171439Z
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000615-1507312800-1507316400@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170202T163000
DTEND;TZID=America/Detroit:20170202T173000
DTSTAMP:20230905T171439Z
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000067-1486053000-1486056600@micde.umich.edu
SUMMARY:[SC2] Launch of 2017 Visualization Challenge + Presentation: Simple Data Management with Signac
DESCRIPTION:Simon Adorf (PhD Candidate\, Chem. Eng.) will give a presentation about “Simple Data Management with Signac“. \nABSTRACT: We will give a brief introduction to the signac data management framework for agile computational workflows\, followed by presenting interactive examples using jupyter notebooks hosted online. The signac framework aids in the management of large and heterogeneous data spaces. It provides a simple and robust data model to create a well-defined indexable storage layout for data and metadata. This makes it easier to operate on large data spaces\, streamlines post-processing and analysis and makes data collectively accessible. \nEveryone is encouraged to bring a laptop in order to be able to follow along. \n+ \nThe Scientific Computing Student Club\, partnered with MICDE\, the U-M 3D Lab and NVIDIA\, will officially launch the 2017 NVIDIA Visualization Challenge aimed for students to use the latest visualization tools and technology to show their research data in creative ways. The first prize will include sponsorship to show their work at the Supercomputing ’17 Visualization Showcase\, and more. Join us at the meeting to learn more. \nSponsored by \n 
URL:https://micde.umich.edu/event/sc2-launching-of-2017-visualization-challenge-presentation-simple-data-management-with-signac/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/signature-vertical.png
GEO:42.292322;-83.713272
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=1311 EECS 1301 Beal Ave. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=1301 Beal Ave.:geo:-83.713272,42.292322
END:VEVENT
END:VCALENDAR