Speaker: Prof. Nick Freris New York University
Time: 2015-12-22 14:00-2015-12-22 15:00
Venue: FIT 1-312
We are entering the era of cyberphysical systems (CPS), i.e., very large networks in which collaborating intelligent agents possessing sensing, communication and computation capabilities are interconnected for controlling physical systems via complex real-time operations. The design of such systems imposes many challenges, most notably: a) decentralized coordination, b) efficient resource allocation and c) mining information from big data generated by thousands, possibly millions of nodes. In this talk, I will present results on distributed and asynchronous management of CPS, in specific: computing, clock synchronization, and exact data mining from inexact big data.
We propose and analyze a randomized iterative algorithm for solving large-scale linear systems. The scheme has exponential convergence and is amenable to distributed implementation. Our method demonstrates substantial speed-up for sparse systems over state-of-art linear solvers. We leverage the analysis to propose a new design method for randomized gossip algorithms for achieving network-wide consensus.
Clock synchronization is indispensable for a CPS to perform as a whole via decentralized actions, and real-time applications impose stringent constraints on synchronization accuracy. We present fundamental limits on synchronizing clocks in a network, and in fact prove that clock synchronization is generally impossible. Inspired by the system implications of our theory as well as our results on gossiping, we design novel synchronization protocols with improved accuracy, convergence speed, as well as energy savings.
A large CPS inevitably generates big data, and efficient information retrieval in the real-time is required. The performance of similarity search/classification is highly dependent on distance estimation from compressed data. We develop a fast algorithm to obtain the tightest upper/lower bounds on Euclidean distance between data series. Extensive experiments indicate a significant speed-up of search schemes due to the effective pruning resulted from accurate distance estimation.
Keywords: Cyberphysical systems, Big data, Gossip algorithms, Clock synchronization, Distributed, randomized, asynchronous algorithms, Data Mining, Fast search, Wireless Sensor Networks
Research areas: Optimization, Control, Wireless networking, Theory, Numerical Analysis, Data mining, Machine Learning, Signal Processing, Graph Theory.
Nick Freris is currently an assistant professor of Electrical and Computer Engineering at New York University Abu Dhabi, and the director of Cyberphysical Systems Laboratory (CPSLab). He is also a member of the Center for Interdisciplinary Studies in Security and Privacy (CRISSP). He received the Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece in 2005 and the M.S. degree in Electrical and Computer Engineering, the M.S. degree in Mathematics, and the Ph.D. degree in Electrical and Computer Engineering all from the University of Illinois at Urbana-Champaign in 2007, 2008, and 2010, respectively.
His research lies in cyberphysical systems, in particular: distributed estimation, optimization and control in wireless and sensor networks, data mining/machine learning, transportation networks, as well as sparse sampling. Dr. Freris has published in several top-tier journals and conferences on Electrical Engineering, Computer Science and Applied Mathematics, held by IEEE, ACM and SIAM. His research was recognized with two IBM invention achievement awards, a Vodafone fellowship and the Gerondelis foundation award. Previously, Dr. Freris was a senior researcher in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he was the project manager of a long-standing collaboration with Qualcomm. From 2010-2012, he was a postdoctoral researcher in IBM Research – Zurich, Switzerland, where he was involved in a 5-year ERC project on Big Data, as well as the IBM Operations Research Group. During his graduate years, he also worked as a research intern in Deutsche Telekom and Xerox Research labs. Dr. Freris is a member of IEEE, SIAM and ACM.