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X-WR-CALDESC:Events for Simons Foundation
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DTSTART;TZID=America/New_York:20160511T170000
DTEND;TZID=America/New_York:20160511T180000
DTSTAMP:20260404T101059
CREATED:20160210T050000Z
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UID:339-1462986000-1462989600@www.simonsfoundation.org
SUMMARY:Contemporary Supercomputing: Opportunities for Science and Challenges for Computer Engineering
DESCRIPTION:President Obama’s July 2015 Executive Order\, which established a National Strategic Computing Initiative\, ensures that the U.S. will make substantial investments in the development of exascale computing systems. While this opens many opportunities in science\, construction of such systems calls for new approaches to software\, mathematical algorithms and systems engineering. \nIn this lecture\, Dr. Schulthess will show how recent developments in architecture have moved us away from traditional abstractions\, forcing software development and mathematical algorithms to acknowledge the physical reality of computing systems. Data locality and asynchrony will be key to the effective use of exascale computing systems. Furthermore\, the dusk of complementary metal-oxide semiconductor (CMOS) scaling is increasing the diversity of computer architectures. This is profoundly challenging to software development and systems engineering\, but at the same time\, it opens many new opportunities for science. A strategy to manage this software challenge will be discussed in terms of recent experiences in numerical weather predictions. \nThomas Schulthess is director of the Swiss National Supercomputing Centre (CSCS) and a professor for computational physics at ETH Zürich. He received his Ph.D. in 1994 from ETH Zürich and spent many years at Oak Ridge National Laboratory\, where today he holds a distinguished visiting scientist appointment. While his primary research is on computational methods for materials science\, he recently took interest in the development of energy-efficient computing systems for climate modeling and meteorology.
URL:https://www.simonsfoundation.org/event/contemporary-supercomputing-opportunities-for-science-and-challenges-for-computer-engineering/
LOCATION:Gerald D. Fischbach Auditorium\, 160 5th Avenue\, New York\, NY\, 10010\, United States
CATEGORIES:Frontiers of Data Science
ATTACH;FMTTYPE=image/jpeg:https://sf-web-assets-prod.s3.amazonaws.com/wp-content/uploads/2017/07/10180934/Schulthess_headshot.jpg
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DTSTART;TZID=America/New_York:20160513T170000
DTEND;TZID=America/New_York:20160513T181500
DTSTAMP:20260404T101059
CREATED:20160420T040000Z
LAST-MODIFIED:20211208T181552Z
UID:347-1463158800-1463163300@www.simonsfoundation.org
SUMMARY:Universality Phenomena in Machine Learning\, and Their Applications
DESCRIPTION:A canonical task in machine learning is to fit a model (from a certain class) to a dataset. In many settings there is little theoretical understanding of the algorithms used for this task\, since they involve nonconvex optimization. \nWe have empirically observed that in many settings the models fitted to real-life datasets display randomlike properties — the model parameters behave like random numbers for various tests. This is somewhat reminiscent of the ‘universality’ phenomenon in mathematics and physics\, whereby matrices in a host of settings turn out to display properties similar to those of the Gaussian ensemble. \nIn this talk\, Sanjeev Arora will describe how these randomlike properties can be used to gain a new understanding in some settings — for example\, they can offer insights into linear algebraic properties of word meanings in natural languages\, and reversibility properties of fully connected deep nets. In some cases\, they can lead us to provably efficient algorithms\, such as algorithms for making inferences in a topic model. \nArora is the Charles C. Fitzmorris Professor of Computer Science at Princeton University. His research area spans several areas of theoretical Computer Science including computational complexity and algorithm design\, and theoretical problems in machine learning. He has received the ACM-EATCS Gödel Prize (in 2001 and 2010)\, Packard Fellowship (1997)\, the ACM Infosys Foundation Award in the Computing Sciences (2012)\, the Fulkerson Prize (2012)\, and the Simons Investigator Award (2012).
URL:https://www.simonsfoundation.org/event/universality-phenomena-in-machine-learning-and-their-applications/
LOCATION:Gerald D. Fischbach Auditorium\, 160 5th Avenue\, New York\, NY\, 10010\, United States
CATEGORIES:Interdisciplinary
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DTSTART;TZID=America/New_York:20160525T170000
DTEND;TZID=America/New_York:20160525T181500
DTSTAMP:20260404T101059
CREATED:20160113T050000Z
LAST-MODIFIED:20211208T181356Z
UID:336-1464195600-1464200100@www.simonsfoundation.org
SUMMARY:Imaging Life at High Spatiotemporal Resolution
DESCRIPTION:As our understanding of biological systems has increased\, so has the complexity of our questions and the need for more advanced optical tools to answer them. For example\, there is a hundredfold gap between the resolution of conventional optical microscopy and the scale at which molecules self-assemble to form subcellular structures. Furthermore\, as we attempt to peer more closely at the three-dimensional\, dynamic complexity of living systems\, the actinic glare of our microscopes can adversely influence the specimens we hope to study. Finally\, the heterogeneity of living tissue can seriously impede our ability to image at high resolution\, due to the resulting warping and scattering of light rays. \nEric Betzig will describe three areas focused on addressing these challenges: super-resolution microscopy for imaging specific proteins within cells at various lengths\, scaling down to near-molecular resolution; plane illumination microscopy using non-diffracting optical lattices for noninvasive imaging of three-dimensional dynamics within live cells and embryos; and adaptive optics to recover optimal images from within large\, optically heterogeneous specimens\, such as zebrafish and the cortex of living mice. \nDr. Betzig obtained a B.S. in physics at the California Institute of Technology and a Ph.D. in applied physics at Cornell University. In 1988\, he became a principal investigator at AT&T Bell Labs\, where he extended his thesis work on near-field optical microscopy\, the first method to break the diffraction barrier. By 1993\, he held a world record for data-storage density and recorded the first super-resolution fluorescence images of cells as well as the first single molecule images at ambient temperature. Frustrated with technical limitations and declining standards as more jumped into the field\, he quit science and\, by 1996\, was working for his father’s machine tool company. Commercial failure of the technologies he developed there left him unemployed in 2003 and looking for new directions. This search eventually culminated in his co-invention of the super-resolution technique PALM with his best friend and Bell Labs colleague Harald Hess. For this work\, he was co-recipient of the 2014 Nobel Prize in Chemistry. Since 2005\, he has been a group leader at the Howard Hughes Medical Institute’s Janelia Research Campus\, developing new optical imaging technologies for biology.
URL:https://www.simonsfoundation.org/event/imaging-life-at-high-spatiotemporal-resolution/
LOCATION:Gerald D. Fischbach Auditorium\, 160 5th Avenue\, New York\, NY\, 10010\, United States
CATEGORIES:New Directions in Imaging
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