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Events for March 03, 2016
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PhD Visit Day, 2016
Thu, Mar 03, 2016
Thomas Lord Department of Computer Science
Receptions & Special Events
Event details will be distributed closer to the date.
Audiences: Registration Required
Contact: Assistant to CS chair
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MHI Distinguished Visitor Talk
Thu, Mar 03, 2016 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. K.J. Ray Liu, University of Maryland
Talk Title: Learning with Strategic Decision Making in Social Media
Abstract: With the increasing ubiquity and power of mobile devices, as well as the prevalence of social media, more and more activities in our daily life are being recorded, tracked, and shared, creating the notion of 'social media'. Such abundant and still growing real life data, known as 'big data', provide a tremendous research opportunity in many fields. To analyze, learn and understand such user generated big data, machine learning has been an important tool and various machine learning algorithms have been developed. However, since the user generated big data is the outcome of users decisions, actions and their socio economic interactions, which are highly dynamic, without considering users local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignore users local decisions at the microeconomic level. As such there is a growing need in bridging machine/social learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomenon and local effects to understand/model/analyze better the newly arising issues in the emerging social media. In this talk, we present the notion of 'decision learning that can involve users behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users optimal decision from a user perspective as well as design a mechanism from the system designers perspective to achieve a desirable outcome.
Biography: Dr. K. J. Ray Liu was named a Distinguished Scholar-Teacher of University of Maryland, College Park, in 2007, where he is Christine Kim Eminent Professor of Information Technology. He leads the Maryland Signals and Information Group conducting research encompassing broad areas of information and communications technology with recent focus on future wireless technologies, network science, and information forensics and security. Dr. Liu was a recipient of the 2016 IEEE Leon K. Kirchmayer Technical Field Award on graduate teaching and mentoring, IEEE Signal Processing Society 2014 Society Award, IEEE Signal Processing Society 2009 Technical Achievement Award, and various best paper awards. Recognized by Thomson Reuters as a Highly Cited Researcher, he is a Fellow of IEEE and AAAS. Dr. Liu is a member of IEEE Board of Director. He was President of IEEE Signal Processing Society, where he has served as Vice President -“ Publications and Board of Governor. He was the Editor-in-Chief of IEEE Signal Processing Magazine. He also received teaching and research recognitions from University of Maryland including university-level Invention of the Year Award; and college-level Poole and Kent Senior Faculty Teaching Award, Outstanding Faculty Research Award, and Outstanding Faculty Service Award, all from A. James Clark School of Engineering.
Host: Prof. Shrikanth Narayanan & Prof. C.-C. Jay Kuo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Tanya Acevedo-Lam/EE-Systems
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Less Talking, More Learning: Avoiding Coordination In Parallel Machine Learning Algorithms
Thu, Mar 03, 2016 @ 01:30 PM - 02:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dimitris Papailiopoulos, Postdoctoral Researcher/UC Berkeley
Talk Title: Less Talking, More Learning: Avoiding Coordination In Parallel Machine Learning Algorithms
Abstract: The recent success of machine learning (ML) in both science and industry has generated an increasing demand to support ML algorithms at scale. In this talk, I will discuss strategies to gracefully scale machine learning on modern parallel computational platforms. A common approach to such scaling is coordination-free parallel algorithms, where individual processors run independently without communication, thus maximizing the time they compute. However, analyzing the performance of these algorithms can be challenging, as they often introduce race conditions and synchronization problems.
In this talk, I will introduce a general methodology for analyzing asynchronous parallel algorithms. The key idea is to model the effects of core asynchrony as noise in the algorithmic input. This allows us to understand the performance of several popular asynchronous machine learning approaches, and to determine when asynchrony effects might overwhelm them. To overcome these effects, I will propose a new framework for parallelizing ML algorithms, where all memory conflicts and race conditions can be completely avoided. I will discuss the implementation of these ideas in practice, and demonstrate that they outperform the state-of-the-art across a large number of machine learning tasks.
Biography: Dimitris Papailiopoulos is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley and a member of the AMPLab. His research interests span machine learning, coding theory, and parallel and distributed algorithms, with a current focus on coordination-free parallel machine learning, large-scale data and graph analytics, and the use of codes to speed up distributed computation. Dimitris completed his Ph.D. in electrical and computer engineering at UT Austin in 2014. At Austin he worked under the supervision of Alex Dimakis. In 2015, he received the IEEE Signal Processing Society, Young Author Best Paper Award.
Host: Professor Keith Chugg, chugg@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
