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Events for March 09, 2016
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From Communication to Sensing and Learning: An Information Theoretic Perspective
Wed, Mar 09, 2016 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hamed Hassani, Postdoctoral Scholar/ETH Zurich
Talk Title: From Communication to Sensing and Learning: An Information Theoretic Perspective
Abstract: We are witnessing a new era of science -” ushered in by our ability to collect massive amounts of data and by unprecedented ways to learn about the physical world. Beyond the challenges of storage and communication, there are new frontiers in the acquisition, analysis and exploration of data. In this talk, I will view these frontiers through the lens of information theory. I will argue that information theory lies at the center of data science, offering insights beyond its classical applications. As a concrete example, I will consider the problem of optimal data acquisition, a challenge that arises in active learning, optimal sensing and experimental design. Based on information theoretic foundations, and equipped with tools from submodular optimization theory, I will present a rigorous analysis of the widely-used sequential information maximization policy (also known as the information-gain heuristic). Our analysis establishes conditions under which this policy provably works near-optimally and identifies situations where the policy fails. In the latter case, our framework suggests novel, efficient surrogate objectives and algorithms that outperform classical techniques.
Biography: Hamed Hassani is a post-doctoral scholar at the Institute for Machine Learning at ETH Zurich. He received a Ph.D. degree in Computer and Communication Sciences from EPFL, Lausanne. Prior to that, he received a B.Sc. degree in Electrical Engineering and a B.Sc. degree in Mathematics from Sharif University of Technology, Tehran. Hamed's fields of interest include machine learning, coding and information theory as well as theory and applications of graphical models. He is the recipient of the 2014 IEEE Information Theory Society Thomas M. Cover Dissertation Award. His co-authored paper at NIPS 2015 was selected for an oral (plenary) presentation, and his co-authored paper at ISIT 2015 received the IEEE Jack Keil Wolf ISIT Student Paper Award.
Host: Professor Urbashi Mitra, ubli@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Computational Imaging for Real-Time Gigapixel and 3D Wave-Field Microscopy
Wed, Mar 09, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Lei Tian, Postdoctoral Associate / Dept of EECS, University of California, Berkeley
Talk Title: Computational Imaging for Real-Time Gigapixel and 3D Wave-Field Microscopy
Abstract: Abstract: Computational imaging is a new frontier of imaging technology that overcomes fundamental limitations of conventional systems by jointly designing optics, devices, signal processing, and algorithms. In this talk, I will present recent advancements in computational wave-field imaging that enable Gigapixel and 3D phase microscopy capability, breaking the limit of space-time-bandwidth product in traditional systems. In particular, I will describe a computational microscopy platform that implements coded illumination and nonlinear phase retrieval algorithms to reconstruct wide field-of-view and high-resolution phase images. Further, new illumination multiplexing techniques reduce data requirements by one order of magnitude, and acquisition times from minutes to sub-second. Experiments demonstrate quantitative dynamic imaging of rare biological events across multiple scales in both space and time. Finally, new 3D wave-optical model and reconstruction technique allow Gigavoxel reconstruction of 3D objects, achieving lateral resolution and depth sectioning well beyond the physical limit of traditional systems. Such computational imaging approach creates significant new capabilities by integrating hardware and computation at the system level. It promises wide applications, such as biomedicine, metrology, inspection, security and X-ray.
Biography: Bio: Lei Tian is a postdoctoral associate in the department of Electrical Engineering and Computer Sciences at University of California Berkeley. He received his Ph.D. in 2013 and M.S. in 2010, both from Massachusetts Institute of Technology (MIT). His research interests include computational imaging, computational-optical instrumentation, phase retrieval, imaging through 3D complex media, large-scale microscopy, and their applications in biomedicine, security, metrology, inspection, X-ray and EUV.
Dr. Tian is the author of over 30 peer-reviewed articles and is a named inventor on 3 US patent applications. His recent work on coded illumination for Gigapixel imaging was awarded the Best Paper in Optical Society of America (OSA) Imaging Systems and Applications conference (2014). His work on optical coherence recovery using low-rank method was awarded the Emil Wolf Best Student Paper in OSA Frontier in Optics annual meeting (2011). Dr. Tian is currently serving as conference chair and program committee member in multiple conferences of OSA, SPIE, and IEEE.
Host: Dr. Justin Haldar, jhaldar@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Mar 09, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Na Li, Harvard University
Talk Title: Distributed Energy Management with Limited Communication
Series: CommNetS
Abstract: A major issue in future smart grid is how intelligent devices and independent producers can respectively change their power consumption/production to achieve near maximum efficiency for the power network. Limited communications between devices, producers etc. necessitates an approach where the elements of the network can act in an autonomous manner with limited information/communications yet achieve near optimal performance. In this talk, I will present our recent work on distributed energy management with limited communication. In particular, I will show how we can extract information from physical measurements and recover information from local computation. We will investigate the minimum amount of communication for achieving the optimal energy management and study how limited communication affects the convergence rate of the distributed algorithms.
Biography: Na Li is an assistant professor in Electrical Engineering and Applied Mathematics of the School of Engineering and Applied Sciences in Harvard University since 2014. She received her PhD degree in Control and Dynamical systems from California Institute of Technology in 2013 and was a postdoctoral associate of the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology. Her research lies in the design, analysis, optimization and control of distributed network systems, with particular applications to power networks. She received NSF career award (2016) and entered the Best Student Paper Award ï¬nalist in the 2011 IEEE Conference on Decision and Control.
Host: Prof. Ashutosh Nayyar
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
