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Events for March 07, 2016
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A Network-Centric Approach To Data Science: From Distributed Learning To Social Recommender Systems
Mon, Mar 07, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Zhenming Liu, Princeton University
Talk Title: A Network-Centric Approach To Data Science: From Distributed Learning To Social Recommender Systems
Abstract: Networks play important roles in various stages of a data science life cycle, including the design of scalable platforms, the collection of data, and the analysis of statistical models. I will talk about my efforts to develop a suite of network-based techniques in these stages. After briefly describing my work on designing scalable platforms for online machine learning algorithms and that for sampling data from the Web, I will discuss the details of a recent project that uses network analysis to study social recommender systems. A social recommender system leverages its users social connections to improve recommendation service. The recommender system we have designed simultaneously maximizes (a) an individuals benefit from using a social network and (b) the networks efficiency in disseminating information. The design solution brings together techniques from spectral analysis, random walk theory, and large-scale optimization.
Biography: Zhenming Liu received his PhD from Harvard University (working with Michael Mitzenmacher) and then spent two years as a postdoc at Princeton University (primarily working with Mung Chiang and Jennifer Rexford). Presently, he is a machine learning researcher for a quantitative hedge fund. Dr. Liu's research focus is the intersection of data science and network analysis; he designs both algorithms that analyze network structures inherent in the data (e.g., social and biological networks) and scalable platforms in support of big data analytics. He has received several awards for his research, including a Best Paper Runner Up at INFOCOM 2015 and a Best Student Paper Award at ECML/PKDD 2010.
Host: Professor Bhaskar Krishnamachari
Location: 248
Audiences: Everyone Is Invited
Contact: Theodore Low
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Seminars in Biomedical Engineering
Mon, Mar 07, 2016 @ 12:30 PM - 01:49 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Chethan Pandarinath, PhD, Postdoctoral Fellow Stanford University Departments of Neurosurgery and Electrical Engineering
Talk Title: Advancing brain-machine interfaces towards clinical viability
Host: K. Kirk Shung, PhD
More Information: Abstract_Bio_ Chethan Pandarinath.pdf
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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EE-EP Seminar - Mona Zebarjadi, Monday, March 7th in EEB 132 @ 2:00pm
Mon, Mar 07, 2016 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mona Zebarjadi, Rutgers University
Talk Title: Manipulation of Electricity and Heat at Nanoscales
Abstract: Fundamental understanding of electron and phonon transport enables us to manipulate electricity and heat at nanoscales. Advances in computations and in parallel in nanotechnology, allow design of specific quantum potentials inside materials and devices to guide charge and heat carriers. The resulting capability of manipulating electricity and heat inspires design of new electronic devices, power generators, and heat pumps. In this talk, I will discuss several examples to elucidate our approach. First I will discuss novel strategies to enhance materials carrier mobility and conductivity for design of fast transistors, switches, and thermoelectric modules via un-conventional doping schemes including 3D modulation-doping and invisible-doping. Transport modeling and experimental results on single layer graphene will be presented next as a design example of active and passive coolers. Finally, design of solid-state thermionic coolers in micron-scales (using Monte-Carlo simulations) as well as nano-scales (using first-principles modeling approach) will be described.
Biography: Mona Zebarjadi is a professor of mechanical engineering at Rutgers University. Her research interests are in electron and phonon transport modeling; materials and device design, fabrication and characterization; with emphasis on energy conversion systems such as thermoelectric, thermionic, and thermomagnetic power generators, and heat management in high power electronics and optoelectronic devices. She received her Bachelor's degree in physics from Sharif University in 2004 and her PhD in EE from UCSC in 2009, after which she spent 3 years at MIT as a postdoctoral fellow working jointly with electrical and mechanical engineering departments. She joined Rutgers University in January 2013. She is the recipient of 2014 AFOSR career award, 2015 A.W. Tyson assistant professorship award, MRS graduate student gold medal, and SWE electronics for imaging scholarship.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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EE 598 Cyber-Physical Systems Seminar Series
Mon, Mar 07, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Saman Zonouz, Assistant Professor at Rutgers University
Talk Title: Trustworthy Critical Infrastructures Threats, Challenges, and Countermeasures Applications
Abstract: Critical cyber-physical infrastructures, such as the power grid, integrate networks of computational and physical processes to provide the people across the globe with essential functionalities and services. Protecting these critical infrastructures is a vital necessity because the failure of these systems would have a debilitating impact on economic security and public health and safety. Our research and development projects aim at provision of real-world solutions to facilitate the secure and reliable operation of next-generation critical infrastructures and require interdisciplinary research efforts across adaptive systems and network security, cyber-physical systems, and trustworthy real-time detection and response mechanisms. In this talk, I will focus on real past and potential future threats against critical infrastructures and embedded devices, and discuss the challenges in design, implementation, and analysis of security solutions to protect cyber-physical platforms. I will introduce novel classes of working systems that we have developed to overcome these challenges in practice, and finally conclude with several concrete directions for future research.
Biography: Saman Zonouz is an Assistant Professor in the Electrical and Computer Engineering Department at Rutgers University since September 2014 and the Director of the 4N6 Cyber Security and Forensics Laboratory. His research has been awarded NSF CAREER Award in 2015, Google Security Reward in 2015, Top-3 Demo at IEEE SmartGridComm 2015, the Faculty Fellowship Award by AFOSR in 2013, the Best Student Paper Award at IEEE SmartGridComm 2013, the University EARLY CAREER Research award in 2012 as well as the Provost Research Award in 2011. The 4N6 research is currently supported by National Science Foundation (NSF), Department of Homeland Security (DHS), Office of Naval Research (ONR), Department of Energy (DOE), Advanced Research Projects Agency Energy (ARPA-E), Department of Education (DOE), Siemens Research Labs (SRL), WinRiver, GrammaTech, Google, and Fortinet Corporation including tech-to-market initiatives. Saman's current research focuses on systems security and privacy, trustworthy cyber-physical critical infrastructures, binary/malware analysis and reverse engineering, as well as adaptive intrusion tolerance architectures. Saman has served as the chair, program committee member, guest editor and a reviewer for top international conferences and journals. Saman serves on Editorial Board for IEEE Transactions on Smart Grid. He obtained his Ph.D. in Computer Science, specifically, intrusion tolerance architectures for the cyber-physical infrastructures, from the University of Illinois at Urbana-Champaign in 2011.
Host: Paul Bogdan
Location: 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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CAMS Colloquium: Shang-Hua Teng (USC) - Through the Lens of the Laplacian Paradigm: Big Data and Scalable Algorithms -- a Pragmatic Match Made On Earth
Mon, Mar 07, 2016 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Shang-Hua Teng, USC
Talk Title: Through the Lens of the Laplacian Paradigm: Big Data and Scalable Algorithms -- a Pragmatic Match Made On Earth
Abstract: In the age of Big Data, efficient algorithms are in higher demand now more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, the explosive growth of problem size has also significantly challenged the classical notion of efficient algorithms:
Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation.
In this talk, I will discuss the emerging Laplacian Paradigm, which has led to breakthroughs in scalable algorithms for several fundamental problems in network analysis, machine learning, and scientific computing. I will focus on three recent applications: (1) PageRank Approximation (and identification of network nodes with significant PageRanks). (2) Random-Walk Sparsification. (3) Scalable Newton's Method for Gaussian Sampling.
Biography: Dr. Shang-Hua Teng has twice won the prestigious Godel Prize in theoretical computer science, first in 2008, for developing the theory of smoothed analysis , and then in 2015, for designing the groundbreaking nearly-linear time Laplacian solver for network systems. Both are joint work with Dan Spielman of Yale --- his long-time collaborator. Smoothed analysis is fundamental for modeling and analyzing practical algorithms, and the Laplacian paradigm has since led to several breakthroughs in network analysis, matrix computation, and optimization. Citing him as, "one of the most original theoretical computer scientists in the world", the Simons Foundation named Teng a 2014 Simons Investigator, for pursuing long-term curiosity-driven fundamental research. He and his collaborators also received the best paper award at ACM Symposium on Theory of Computing (STOC) for what's considered to be the "first improvement in 10 years" of a fundamental optimization problem --- the computation of maximum flows and minimum cuts in a network. In addition, he is known for his joint work with Xi Chen and Xiaotie Deng that characterized the complexity for computing an approximate Nash equilibrium in game theory, and his joint papers on market equilibria in computational economics. He and his collaborators also pioneered the development of well-shaped Dalaunay meshing algorithms for arbitrary three-dimensional geometric domains, which settled a long-term open problem in numerical simulation, also a fundamental problem in computer graphics. Software based on this development was used at the University of Illinois for the simulation of advanced rockets. Teng is also interested in mathematical board games. With his former Ph.D. student Kyle Burke, he designed and analyzed a game called Atropos , which is played on the Sperner's triangle and based on the beautiful, celebrated Sperner's Lemma. In 2000 at UIUC, Teng was named on the List of Teachers Ranked as Excellent by Their Students for his class, "Network Security and Cryptography". He has worked and consulted for Microsoft Research, Akamai, IBM Almaden Research Center, Intel Corporation, Xerox PARC, and NASA Ames Research Center, for which he received fifteen patents for his work on compiler optimization, Internet technology, and social network.
Host: USC CAMS
Location: Kaprielian Hall (KAP) - 414
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
