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Events for September 27, 2016
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USC Stem Cell Seminar: Maksim Plikus, University of California, Irvine
Tue, Sep 27, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Maksim Plikus, University of California, Irvine
Talk Title: Regeneration of adipocytes in skin scars via reprograming of myofibroblasts
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: USC Stem Cell
More Info: http://stemcell.usc.edu/events
Webcast: http://keckmedia.usc.edu/Mediasite/Catalog/catalogs/StemCellSeminarWebCast Link: http://keckmedia.usc.edu/Mediasite/Catalog/catalogs/StemCellSeminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
Event Link: http://stemcell.usc.edu/events
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Epstein Institute Seminar - ISE 651
Tue, Sep 27, 2016 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Michael L. Overton, Professor of Computer Science and Mathematics at the Courant Instittute of Mathematical Sciences, New York University
Talk Title: Nonsmooth, Nonconvex Optimization: Algorithms and Examples
Abstract: In many applications one wishes to minimize an objective function that is not convex and is not differentiable at its minimizers. We discuss two algorithms for minimization of nonsmooth, nonconvex functions. Gradient Sampling is a simple method that, although computationally intensive, has a nice convergence theory. The method is robust and the convergence theory has recently been extended to constrained problems.
BFGS is a well-known method, developed for smooth problems, but which is remarkably effective for nonsmooth problems too. Although our theoretical results in the nonsmooth case are quite limited, we have made some remarkable empirical observations and have had broad success in applications. Limited Memory BFGS is a popular extension for large problems, and it is also applicable to the nonsmooth case, although our experience with it is more mixed. Throughout the talk we illustrate the ideas through examples, some very easy and some very challenging. Our work is with Jim Burke U. Washington and Adrian Lewis Cornell.
Biography: Michael L. Overton is Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University. He received his Ph.D. in Computer Science from Stanford University in 1979. He is a fellow of SIAM Society for Industrial and Applied Mathematics and of the IMA -Institute of Mathematics and its Applications, UK. He served on the Council and Board of Trustees of SIAM from 1991 to 2005, including a term as Chair of the Board from 2004 to 2005. He served as Editor-in-Chief of SIAM Journal on Optimization from 1995 to 1999 and of the IMA Journal of Numerical Analysis from 2007 to 2008, and was the Editor-in-Chief of the MPS Mathematical Programming Society-SIAM joint book series from 2003 to 2007. He is currently an editor of SIAM Journal on Matrix Analysis and Applications, IMA Journal of Numerical Analysis, Foundations of Computationa Mathematics, and Numerische Mathematik. His research interests are at the interface of optimization and linear algebra, especially nonsmooth optimization problems involving eigenvalues, pseudospectra, stability and robust control. He is the author of Numerical Computing with IEEE Floating Point Arithmetic SIAM, 2001.
Host: Dr. Jong-Shi Pang
Location: 206
Audiences: Everyone Is Invited
Contact: Angela Reneau
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CS Colloquium: Le Song (GATECH) - Discriminative Embedding of Latent Variable Models for Structured Data
Tue, Sep 27, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Le Song, GATECH
Talk Title: Discriminative Embedding of Latent Variable Models for Structured Data
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium. Part of Yahoo! Labs Machine Learning Seminar Series.
Structured data, such as sequences, trees, graphs and hypergraphs, are prevalent in a number of interdisciplinary areas such as network analysis, knowledge engineering, computational biology, drug design and materials science. The availability of large amount of such structured data has posed great challenges for the machine learning community. How to represent such data to capture their similarities or differences? How to learn predictive models from a large amount of such data, and efficiently? How to learn to generate structured data de novo given certain desired properties?
A common approach to tackle these challenges is to first design a similarity measure, called the kernel function, between two data points, based on either statistics of the substructures or probabilistic generative models; and then a machine learning algorithm will optimize a predictive model based on such similarity measure. However, this elegant two-stage approach has difficulty scaling up, and discriminative information is also not exploited during the design of similarity measure.
In this talk, I will present Structure2Vec, an effective and scalable approach for representing structured data based on the idea of embedding latent variable models into a feature space, and learning such feature space using discriminative information. Interestingly, Structure2Vec extracts features by performing a sequence of nested nonlinear operations in a way similar to graphical model inference procedures, such as mean field and belief propagation. In applications involving genome and protein sequences, drug molecules and energy materials, Structure2Vec consistently produces the-state-of-the-art predictive performance. Furthermore, in the materials property prediction problem involving 2.3 million data points, Structure2Vec is able to produces a more accurate model yet being 10,000 times smaller. In the end, I will also discuss potential improvements over current work, possible extensions to network analysis and computer vision, and thoughts on the structured data design problem.
Biography: Le Song is an assistant professor in the Department of Computational Science and Engineering, College of Computing, Georgia Institute of Technology. He received his Ph.D. in Machine Learning from University of Sydney and NICTA in 2008, and then conducted his post-doctoral research in the Department of Machine Learning, Carnegie Mellon University, between 2008 and 2011. Before he joined Georgia Institute of Technology, he was a research scientist at Google. His principal research direction is machine learning, especially kernel methods and probabilistic graphical models for large scale and complex problems, arising from artificial intelligence, network analysis, computational biology and other interdisciplinary domains. He is the recipient of the AISTATS'16 Best Student Paper Award, IPDPS'15 Best Paper Award, NSF CAREER Award'14, NIPS'13 Outstanding Paper Award, and ICML'10 Best Paper Award. He has also served as the area chair or senior program committee for many leading machine learning and AI conferences such as ICML, NIPS, AISTATS and AAAI, and the action editor for JMLR.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Networking Best Practices- Presented by Principal Development Group
Tue, Sep 27, 2016 @ 04:30 PM - 05:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join a Principal Development Group representative to go over the do's and don'ts of networking. Learn who you should be networking with and where for maximum success.
Location: Kaprielian Hall (KAP) - 159
Audiences: All Viterbi Students
Contact: RTH 218 Viterbi Career Connections
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Ericsson Info Session
Tue, Sep 27, 2016 @ 05:30 PM - 08:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
At Ericsson, we strive to connect everyone, wherever they may be. Because by being connected, people can take part in the emerging global collaboration that is the Networked Society - a society in which every person and every industry is empowered to reach their full potential.
Our services, software and infrastructure - especially in mobility, broadband and the cloud - are enabling the communications industry and other sectors to do better business, increase efficiency, improve their users' experience and capture new opportunities.
By enabling the Networked Society, we make a real difference to people's lives, and the world we live in.Location: Seeley G. Mudd Building (SGM) - 101
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Connections
