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Events for April 03, 2017
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CS Colloquium: Austin Benson (Stanford) -Tools for higher-order network analysis
Mon, Apr 03, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Austin Benson , Stanford University
Talk Title: Tools for higher-order network analysis
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Networks are a fundamental model of complex systems in biology, neuroscience, engineering, and social science. Networks are typically described by lower-order connectivity patterns that are captured at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, or network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. In this talk, I will discuss several higher-order analyses based on higher-order connectivity patterns that I have developed to gain new insights into network data. Specifically, I will introduce a motif-based clustering methodology, a generalization of the classical network clustering coefficient, and a formalism for temporal motifs to study temporal networks. I will also show applications of higher-order analysis in several domains including ecology, biology, transportation, neuroscience, social networks, and human communication.
Biography: Austin Benson is a PhD candidate at Stanford University in the Institute for Computational and Mathematical Engineering where he is advised by Professor Jure Leskovec of the Computer Science Department. His research focuses on developing data-driven methods for understanding complex systems and behavior. Broadly, his research spans the areas of network science, applied machine learning, tensor and matrix computations, and computational social science. Before Stanford, he completed undergraduate degrees in Computer Science and Applied Mathematics at the University of California, Berkeley. Outside of the university, he has spent summers interning at Google (four times), Sandia National Laboratories, and HP Labs.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical Engineering
Mon, Apr 03, 2017 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Song Li, PhD, Chancellor Professor & Dept. Chair, UCLA, Dept. of Bioengineering & Medicine
Talk Title: Microbiology Application
Host: Qifa Zhou
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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CS Colloquium: Stephan Mandt (Disney Research) - Next generation variational inference: algorithms, models, and applications
Mon, Apr 03, 2017 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stephan Mandt, Disney Research
Talk Title: Next generation variational inference: algorithms, models, and applications
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Probabilistic modeling is a powerful paradigm in machine learning. In this field, we assume a generative process in order to explain our observations, and then use a Bayesian inference algorithm to reason about its parameters. Probabilistic modeling has become scalable due to stochastic variational inference which reduces Bayesian inference to non-convex stochastic optimization. This talk focuses on two new inference algorithms: variational tempering-an algorithm that operates on several artificial temperatures simultaneously to find better local optima, and constant SGD-a scalable inference algorithm with applications to hyperparameter optimization. I will then present several new models that have become tractable due to modern variational inference with applications in text modeling, recommendations, and computer vision. I will show how a probabilistic view on Google's word2vec algorithm allows for extensions to other types of high dimensional data and show new applications: analyzing supermarket shopping data, movie ratings, and tracking semantic changes of individual words over centuries of digitized books. Finally, I will show how factorized variational autoencoders allow us to analyze audience reactions to movies.
Biography: Stephan Mandt is a research scientist at Disney Research Pittsburgh, where he leads the statistical machine learning group. From 2014 to 2016 he was a postdoctoral researcher with David Blei at Columbia University, and from 2012 to 2014 a PCCM postdoctoral fellow at Princeton University. Stephan did his Ph.D. with Achim Rosch at the Institute for Theoretical Physics at the University of Cologne, supported by a fellowship of the German National Merit Foundation. His research interests include scalable approximate Bayesian inference and machine learning for media analytics.
Host: Fei Sha
Location: Kaprielian Hall (KAP) - 140
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Mon, Apr 03, 2017 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Oleg Sokolsky, Research Professor, University of Pennsylvania
Talk Title: Behavior Modeling in Patient-in-the-Loop Medical CPS
Abstract: Human-in-the-loop cyber-physical systems (CPS) is an active area of research. As the level of autonomy in systems we use every day is rapidly increasing, the problems of human-automation interaction and of trust in technology are becoming more important. In medical CPS, interactions between the human and technology happen both through behavior as well as through patient physiology. This talk motivates the need for modeling and analysis techniques that take both behavioral and physiological interactions into consideration. We present a case study of diabetic patients interacting with smart insulin pumps and consider how behavioral modeling and analysis can impact treatment outcomes.
Biography: Oleg Sokolsky is a Research Professor of Computer and Information Science at PRECISE Center, University of Pennsylvania. His research interests include applications of formal methods and runtime verification to the design and analysis. He received a Ph.D. in Computer Science from State University of New York at Stony Brook.
Host: Paul Bogdan and Chao Wang
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Estela Lopez
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Entrepreneurial Visa Options for International Students and Scholars
Mon, Apr 03, 2017 @ 05:00 PM - 06:30 PM
Viterbi School of Engineering Career Connections
Student Activity
Students and scholars who would like to found a start-up venture in the U.S. face vexing immigration law challenges. Immigration attorney Tien-Li Loke Walsh, Loke Walsh Immigration Law, will explain issues related to start-up businesses and self-employment for international students and scholars and discuss possible visa options for such entrepreneurial endeavors. Reserve your seat online.
https://events.r20.constantcontact.com/register/eventReg?oeidk=a07edu8pa5781ee3a27&oseq=&c=&ch=Location: Mark Taper Hall Of Humanities (THH) - 101
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
