SUNMONTUEWEDTHUFRISAT
Events for October 21, 2015
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Oct 21, 2015 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Soheil Feizi, MIT
Talk Title: Learning (from) networks: fundamental limits, algorithms, and applications
Series: CommNetS
Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging.
In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.
Biography: Soheil Feizi is a PhD candidate at Massachusetts Institute of Technology (MIT), co-supervised by Prof. Muriel Médard and Prof. Manolis Kellis. His research interests include analysis of complex networks and the development of inference and learning methods based on Optimization, Information Theory, Machine Learning, Statistics, and Probability, with applications in Computational Biology, and beyond. He completed his B.Sc. at Sharif University of Technology, awarded as the best student of his class. He received the Jacobs Presidential Fellowship and EECS Great Educators Fellowship, both from MIT. He has been a finalist in the Qualcomm Innovation contest. He received an Ernst Guillemin Award for his Master of Science Thesis in the department of Electrical Engineering and Computer Science at MIT.
Host: Dr. Salman Avestimehr
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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EE DISTINGUISHED LECTURER SERIES
Wed, Oct 21, 2015 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Naomi Ehrich Leonard, Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering / Princeton University
Talk Title: On the Nonlinear Dynamics of Collective Decision-Making in Nature and Design
Series: Distinguished Lecturer Series
Abstract: The successful deployment of complex multi-agent systems requires well-designed, agent-level control strategies that guarantee system-level dynamics to be robust to disturbance and adaptive in the face of changes in the environment. In applications, such as mobile sensor networks, limitations on individual agents in sensing, communication, and computation create a further challenge. However, system-level dynamics that are both robust and adaptive are observed in animal groups, from bird flocks to fish schools, despite limitations on individual animals in sensing, communication, and computation. To better understand and leverage the parallels between networks in nature and design, a principled examination of collective dynamics is warranted. I will describe an analytical framework based on nonlinear dynamical systems theory for the realization of collective decision-making that allows for the rigorous study of the mechanisms of observed collective animal behavior together with the design of distributed strategies for collective dynamics with provable performance.
Biography: Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and an associated faculty member of the Program in Applied and She received a John D. and Catherine T. MacArthur Foundation Fellowship in 2004, the UCSB Mohammed Dahleh Award in 2005, the Glenn L. Martin Medal from the University of Maryland in 2014, and the Nyquist Lecture Award from the ASME in 2014. She is a Fellow of the IEEE, ASME, SIAM, and IFAC. She received the B.S.E. degree in Mechanical Engineering from Princeton University in 1985 and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland in 1991 and 1994. From 1985 to 1989, she worked as an engineer in the electric power industry.
Host: Sandeep Gupta, Justin Haldar, Urbashi Mitra
Webcast: https://bluejeans.com/694216021Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://bluejeans.com/694216021
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
