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X-WR-CALNAME:Computational Science
X-ORIGINAL-URL:https://computationalscience.uci.edu
X-WR-CALDESC:Events for Computational Science
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230303T153000
DTEND;TZID=UTC:20230303T163000
DTSTAMP:20260422T011255
CREATED:20221116T173958Z
LAST-MODIFIED:20221116T173958Z
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SUMMARY:CSRC Colloquium - Sven Leffer
DESCRIPTION:Title:  Nonlinear Optimization at the National Labs \nAbstract:  We review a range of challenging DOE applications in design and control that are formulated as nonlinear optimization problems\, ranging from the modeling of gas and electricity networks\, over the design of new materials\, to the analysis of complex data. We show how research into new algorithms and solution techniques enables DOE scientists to solve these applications. \nSpeaker:  Sven Leffer\, Mathematics and Computer Science\, Argonne National Laboratory \nBio:  Sven Leyffer joined the Mathematics and Computer Science Division at Argonne in 2002\, where he is now a senior computational mathematician. Sven is a SIAM Fellow\, and a senior fellow of the University of Chicago/Argonne Computation Institute. Leyffer obtained his Ph.D. in 1994 from the University of Dundee\, Scotland\, and held postdoctoral research positions at Dundee\, Argonne\, and Northwestern University. \nPresenter Website: https://wiki.mcs.anl.gov/leyffer/ \nHost: Jose Castillo (CSRC) and the Sustainable Horizons Institute CRLC Virtual Seminar Series
URL:https://computationalscience.uci.edu/event/csrc-colloquium-sven-leffer/
LOCATION:Virtual Zoom Seminar
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BEGIN:VEVENT
DTSTART;TZID=UTC:20230407T153000
DTEND;TZID=UTC:20230407T163000
DTSTAMP:20260422T011255
CREATED:20221116T174058Z
LAST-MODIFIED:20221116T174058Z
UID:799-1680881400-1680885000@computationalscience.uci.edu
SUMMARY:CSRC Colloquium - Prasanna Balaprakash
DESCRIPTION:Title:  Democratizing Deep Learning Development with DeepHyper \nAbstract:  In recent years\, deep neural networks (DNNs) have achieved considerable success in learning complex nonlinear relationships between features and targets from large datasets. Nevertheless\, designing high-performing DNN architecture for a given data set is an expert-driven\, time-consuming\, trial-and-error manual task. A major bottleneck in the construction of DNNs is the vast search space of architectures that need to be explored in the face of new data sets. Moreover\, DNNs typically require user-specified values for hyperparameters\, which strongly influence performance factors such as training time and prediction accuracy. In this talk\, we will introduce participants to DeepHyper\, a scalable automated machine learning package for developing deep neural networks. DeepHyper provides an infrastructure that targets experimental research in neural architecture search (NAS) and hyperparameter search (HPS) methods\, scalability\, and portability across diverse supercomputers through the use of the different workflow manager. \nSpeaker: Prasanna Balaprakash\, Computer Scientist\, Argonne National Laboratory \nBio: Prasanna Balaprakash is a computer scientist with a joint appointment in the Mathematics and Computer Science Division and the Leadership Computing Facility at Argonne National Laboratory. His research work spans the areas of artificial intelligence\, machine learning\, optimization\, and high-performance computing. He is a recipient of the U.S. Department of Energy 2018 Early Career Award. Prior to Argonne\, he worked as a Chief Technology Officer at Mentis Sprl\, a machine learning startup in Brussels\, Belgium. He received his PhD from CoDE-IRIDIA (AI Lab)\, Università Libre de Bruxelles\, Brussels\, Belgium\, where he was a recipient of European Commissions Marie Curie and Belgian F.R.S-FNRS Aspirant fellowships. \nPresenter Website: http://pbalapra.github.io/\nDepartment Link: https://deephyper.readthedocs.io/en/latest/
URL:https://computationalscience.uci.edu/event/csrc-colloquium-prasanna-balaprakash/
LOCATION:Virtual Zoom Seminar
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