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Events for March 11, 2008
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Optimization and Estimation in Sensor Networks and other Distributed Systems
Tue, Mar 11, 2008 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Workshops & Infosessions
Abstract: Large-scale systems with components spread across space presents significant challenges in analysis and design of optimization and estimation algorithms. These difficulties stem primarily from the difficulty of reasoning about dynamic update rules applied in an asynchronous fashion, and with very limited local access to data and signals. While "classical" computing research has answered many of the main questions posed by such systems, very little exists that easily integrates with the body of theory developed by systems, signals, and dynamics engineers; as a consequence, interfaces between these domains are often very rough, if they exist at all. We will discuss a family of techniques aimed at remedying this gap in modeling and formalism. In short, we will present a geometric framework for converting formal descriptions of certain computations (as optimization problems) into automatically generated distributed algorithms with provable dynamic properties. This formalism allows one to cast many computations relevant to sensor networks and estimation in a "centralized" fashion, while obtaining automatically generated, provably convergent distributed algorithms to implement the computation in the network.Bio: Dr. Spanos received his PhD in Control and Dynamical Systems from the California Institute of Technology, where he also recently completed a Post-Doctoral position. His doctoral research focused on dynamic coordination problems in distributed systems, including filtering and optimization tasks in sensor networks. His current research attempts to integrate knowledge and techniques from dynamics, systems theory, networks, and artificial intelligence. In this vein, he has been active in a number of "non-traditional" collaborations, including predictive systems for epidemiological models of breast cancer (with the Harvard School of Public Health), and optimization of wind power farms in Southern Europe (with Siemens).
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Shane Goodoff
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Workshop: Im Hired, Now What
Tue, Mar 11, 2008 @ 12:00 PM - 01:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Workshop entitled "I'm Hired, Now What?" presented by Lockheed Martin.
Location: Ronald Tutor Hall (RTH) 211
Audiences: All Viterbi Students
Contact: RTH 218 Viterbi Career Services
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CS Colloq: Optimizing Sensing from Water to the Web
Tue, Mar 11, 2008 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Title: Optimizing Sensing from Water to the WebSpeaker: Andreas Krause (CMU)Abstract:
Where should we place sensors to quickly detect contaminations in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the web? These problems share a fundamental challenge:
How can we obtain the most useful information about the state of the world, at minimum cost?Such sensing, or active learning, problems are typically NP-hard, and were commonly addressed using heuristics without theoretical guarantees about the solution quality. In this talk, I will present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms exploit submodularity, an intuitive notion of diminishing returns, common to many sensing problems; the more sensors we have already deployed, the less we learn by placing another sensor. To quantify the uncertainty in our predictions, we use probabilistic models, such as Gaussian Processes. In addition to identifying the most informative sensing locations, our algorithms can handle more challenging settings, where sensors need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries and sensor failures. I will also present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, deciding which blogs to read on the web, and a sensor placement competition.Bio:
Andreas Krause is a Ph.D. Candidate at the Computer Science Department of Carnegie Mellon University. He is a recipient of a Microsoft Research Graduate Fellowship, and his research on sensor placement and information acquisition received awards at several conferences (KDD '07, IPSN '06, ICML'05 and UAI '05). He obtained his Diplom in Computer Science and Mathematics from the Technische Universität Mˆ¢nchen, where his research received the NRW Undergraduate Science Award.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Colloquia
