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Events for April 08, 2013
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Sequential Decision-making in Decentralized Systems
Mon, Apr 08, 2013 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ashutosh Nayyar, PhD, UC Berkeley
Talk Title: Sequential Decision-making in Decentralized Systems
Abstract: Decentralized systems are ubiquitous in the modern world. Communication systems, sensor networks, power systems and economic systems like markets and auctions are all examples of decentralized systems. Such systems are characterized by the presence of multiple decision-making agents acting on different information. In this talk, I focus on the problem of finding optimal decision-strategies for co-operative agents in a decentralized system. In particular, I consider a decentralized stochastic decision-making problem with multiple decision-makers that share information with each other with a fixed delay. Such decision problems arise in queuing networks, wireless communication networks, distributed control systems, sensing and surveillance systems etc. In spite of initial conjectures as early as 1971, finding the general structure of agents' optimal decision-strategies with delayed information sharing had remained an open problem for 40 years. My research provides a conceptual framework that not only identifies the structure of optimal decision strategies but also provides a sequential decomposition of the optimization problem. Moreover, the methodology developed here is shown to be applicable to a broader class of decentralized decision making problems arising in diverse application domains.
Biography: Ashutosh Nayyar received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Delhi, India in 2006. He received the MS degree in in Electrical Engineering and Computer Science in 2008, the MS degree in Applied Mathematics in 2011 and the PhD degree in Electrical Engineering and Computer Science in 2011, all from the University of Michigan, Ann Arbor. He worked as a post-doctoral researcher at the University of Illinois at Urbana-Champaign from Fall 2011 to Summer 2012. He is currently a post-doctoral researcher at the University of California, Berkeley. His research interests include decentralized stochastic control, game theory, mechanism design and their applications in sensing and communication systems, decentralized control systems and electric power systems.
Host: Rahul Jain
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Dian Gong (USC), Student Seminar Series
Mon, Apr 08, 2013 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dian Gong, USC Electrical Engineering Ph.D. Student
Talk Title: Machine Learning to Structured Time Series Analysis
Series: Student Seminar Series
Abstract: Time series and sequential data have been investigated for several decades in statistics, signal processing and econometrics. The success of machine learning techniques brings opportunities to efficiently analyze more complex, high dimensional and large-scale time series data. In this talk, we present a non-parametric learning framework to multivariate time series data. The raw time series is first temporally decomposed into different units with certain semantic meanings by our newly proposed Kernelized Temporal Cut (KTC). KTC is an online non-parametric change-point detection method that can detect regime changes for complex sequential data. Given two time series units, the similarity (distance) can be calculated by using spaito-temporal alignment methods such as DTW. To handle the nonlinearity of multivariate time series data in many applications, we propose Dynamic Manifold Warping (DMW). DMW is a combination of DTW and manifold learning by exploring the intrinsic manifold structure of time series data. After temporal segmentation and the design of distance metric, we can treat each time series unit as one data instance, and many tasks can be performed, such as clustering, classification and retrieval. We apply this framework to human action analysis and achieve promising results.
Biography: Dian Gong is a PhD Candidate major in Electrical Engineering and minor in Computer Science at USC. His advisor is Prof. Gerard Medioni, and his research areas are machine learning to structured time series, manifold learning and probabilistic Tensor Voting. He also uses cutting-edge machine learning techniques to computer vision applications such as human activity recognition. His works are published at conferences such as AISTATS 2010, ICCV 2011, ICML 2012 and ECCV 2012. He worked as a summer quantitative trading associate at Barclays Capital New York office. He also worked as research intern at Sony US Research, San Jose, CA on distance metric learning, and Microsoft Research Asia, on probabilistic graphical model. In the past, he won several mathematics contest awards such as the Gold medal of China Mathematics Olympiad, and selected as national team candidate for International Mathematics Olympiad. He got his BS in Electronic Engineering from Tsinghua University.
Advisor: Gerard Medioni
Refreshments will be provided
Host:
More Info: https://mhi.usc.edu/events/event-details/?event_id=901927
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://mhi.usc.edu/events/event-details/?event_id=901927
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Seminars in Biomedical Engineering
Mon, Apr 08, 2013 @ 12:30 PM - 01:50 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Donald Arnold, Ph.D. USC, Associate Professor Department Molecular Biology Division Dornsife College of Letters, Arts and Sciences University of Southern California
Talk Title: Now you see 'em, Now you don't: New molecular tools for visualizing and ablating endogenous proteins in living cells.
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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CS Colloquium: Lorenzo Torresani (Dartmouth): Challenges and Opportunities in Visual Recognition with Big Image Data
Mon, Apr 08, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Lorenzo Torresani, Dartmouth
Talk Title: Challenges and Opportunities in Visual Recognition with Big Image Data
Series: CS Colloquium
Abstract: The last few years have seen a tremendous explosion of image and video data on the Web. Unfortunately only a small portion of this visual data is annotated with text. Even when tags are available, they often do not describe accurately the semantics of the image or the video. This renders traditional text-search an ineffective tool on these collections. In this talk I will describe some of my recent work on designing visual recognition systems that can help users browse and search image repositories more effectively.
I will begin with an algorithm that addresses the computational challenges posed by visual recognition in Web-scale image databases. Our approach centers around the learning of a compact image code optimized to yield accurate recognition with linear (i.e., efficient) classifiers: even when the representation is compressed to less than 300 bytes per image, linear classifiers trained on our descriptor yield accuracy matching the state-of-the-art but at orders of magnitude lower computational cost.
In the second part of my talk I will present a method that embraces Big Image Data as an opportunity to improve visual recognition. Our algorithm exploits a dataset of 10 million labeled photos to learn a universal semantic distance between images. This metric can be used either as a similarity measure to find pictures by example or as a ââ¬Åkernelââ¬Â in distance-based image classifiers, yielding a significant boost in accuracy over traditional metrics.
Biography: Lorenzo Torresani is an Assistant Professor in the Computer Science Department at Dartmouth College. He received a Laurea Degree in Computer Science with summa cum laude honors from the University of Milan (Italy) in 1996, and an M.S. and a Ph.D. in Computer Science from Stanford University in 2001 and 2005, respectively. In the past, he has worked at several industrial research labs including Microsoft Research Cambridge, Like.com, and Digital Persona. His research interests are in computer vision and machine learning. In 2001, Torresani and his coauthors received the Best Student Paper Award at the IEEE Conference On Computer Vision and Pattern Recognition (CVPR). He is the recipient of a National Science Foundation CAREER Award.=
Host: Gerard Medioni
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Cypress Semiconductor PSoC Workshop
Mon, Apr 08, 2013 @ 06:00 PM - 08:00 PM
Viterbi School of Engineering Student Organizations
Workshops & Infosessions
Interested in getting some hands on experience with a real-world engineering device? Want to network with the corporate world while doing so?
Well, look no further!
IEEE will be hosting Cypress Semiconductor for a workshop with their Programmable System-On-Chip, or PSoC, product. There will be a workshop and a short presentation combined with a question and answer session. Feel free to invite your friends from other majors, just don't forget your resume!
Before the event, please download and install their Creator 2.2 software. This can be found at www.cypress.com > software downloads > PSoC Creator. Hope to see you there!Location: John Stauffer Science Lecture Hall (SLH) - 102
Audiences: Everyone Is Invited
Contact: Institute of Electrical and Electronics Engineers
