Events for the 4th week of January
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Jan 22, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Miroslav Krstic, University of California, San Diego
Talk Title: Traffic Congestion Control: A PDE Backstepping Perspective
Abstract: Control of freeway traffic using ramp metering is a "boundary control" problem when modeling is approached using widely adopted coupled hyperbolic PDE models of the Aw-Rascle-Zhang type, which include the velocity and density states, and which incorporate a model of driver reaction time. Unlike the "free traffic" regime, in which ramp metering can affect only the dynamics downstream of the ramp, in the "congested traffic" regime ramp metering can be used to suppress stop-and-go oscillations both downstream and upstream of the ramp -“ though not both simultaneously. Controlling the traffic upstream of a ramp is harder -“ and more interesting -“ because, unlike in free traffic, the control input doesn't propagate at the speed of the vehicles but at a slower speed, which depends on a weighted difference between the vehicle speed and the traffic density. I will show how PDE backstepping controllers, which have been effective recently in oil drilling and production applications (similarly modeled by coupled hyperbolic PDEs), can help stabilize traffic, even in the absence of distributed measurements of vehicle speed and density, and when driver reaction times are unknown.
Biography: Miroslav Krstic is Distinguished Professor of Mechanical and Aerospace Engineering, holds the Alspach endowed chair, and is the founding director of the Cymer Center for Control Systems and Dynamics at UC San Diego. He also serves as Associate Vice Chancellor for Research at UCSD. As a graduate student, Krstic won the UC Santa Barbara best dissertation award and student best paper awards at CDC and ACC. Krstic has been elected Fellow of seven scientific societies - IEEE, IFAC, ASME, SIAM, AAAS, IET (UK), and AIAA (Assoc. Fellow) - and as a foreign member of the Academy of Engineering of Serbia. He has received the ASME Oldenburger Medal, Nyquist Lecture Prize, Paynter Outstanding Investigator Award, Ragazzini Education Award, Chestnut textbook prize, the PECASE, NSF Career, and ONR Young Investigator awards, the Axelby and Schuck paper prizes, and the first UCSD Research Award given to an engineer. Krstic has also been awarded the Springer Visiting Professorship at UC Berkeley, the Distinguished Visiting Fellowship of the Royal Academy of Engineering, the Invitation Fellowship of the Japan Society for the Promotion of Science, and honorary professorships from four universities in China. He serves as Senior Editor in IEEE Transactions on Automatic Control and Automatica, as editor of two Springer book series, and has served as Vice President for Technical Activities of the IEEE Control Systems Society and as chair of the IEEE CSS Fellow Committee. Krstic has coauthored twelve books on adaptive, nonlinear, and stochastic control, extremum seeking, control of PDE systems including turbulent flows, and control of delay systems.
Host: Mihailo Jovanovic, mihailo@usc.edu
More Information: krstic.jpg
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Adversarial Machine Learning: The Case of Optimal Attack Strategies Against Recommendation Systems
Wed, Jan 24, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Negar Kiyavash, Associate Professor/UIUC
Talk Title: Adversarial Machine Learning: The Case of Optimal Attack Strategies Against Recommendation Systems
Abstract: Adversarial machine learning which lies in the intersection of learning and security aims to understand the effects of adversaries on learning algorithms and safe guard against them by design of protection mechanisms. In this talk, we discuss the effect of strategic adversaries in recommendation systems. Such systems can be modeled using a multistage sequential prediction framework where at each stage, the recommendation system combines the predictions of set of experts about an unknown outcome with the aim of accurately predicting the outcome. The outcome is often the "rating/interest" of a user in an item. Specifically, we study an adversarial setting in which one of the experts is malicious and his goal is to impose the maximum loss on the system. We show that in some settings the greedy policy of always reporting false prediction is asymptotically optimal for the malicious expert. Our result could be viewed as a generalization of the regret bound for learning from expert advice problem in the adversarial setting with respect to the best dynamic policy, rather than the conventional regret bound for the best action (static policy) in hindsight.
Biography: Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering and Electrical and Computer Engineering. She is also affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.
Host: Sandeep Gupta, sandeep@usc.edu, x02251
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Secure Hardware Platforms for the Internet of Things (IoT)
Wed, Jan 24, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Srini Devadas, Massachusetts Institute of Technology
Talk Title: Secure Hardware Platforms for the Internet of Things (IoT)
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The Internet is expanding into the physical world, connecting billions of devices. In this Internet of Things, two contradictory trends are appearing. On the one hand, the cost of security breaches is increasing as we place more responsibilities on the devices that surround us. On the other hand, wireless computing elements are becoming small, unsupervised, and physically exposed. Unfortunately, existing systems do not address many new attacks, such as resource sharing and physical attacks.
Hardware to the rescue! This talk will describe how secure systems can be built from the ground up. Physical Unclonable Functions (PUFs) are a tamper resistant way of establishing shared secrets with a physical device. They rely on the inevitable manufacturing variations between devices to produce private keys that can be used as a hardware root of trust in a secure processor. Architectural isolation can be used to secure computation on a remote secure processor with a private key where the privileged software is potentially malicious as recently deployed by Intel's Software Guard Extensions (SGX). The Sanctum secure processor architecture offers the same promise as SGX, namely strong provable isolation of software modules running concurrently and sharing resources, but is much more lightweight and protects against an important class of additional software attacks that infer private information by exploiting resource sharing.
Biography: Srini Devadas is the Webster Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) where he has been on the faculty since 1988. Devadas's research interests span Computer-Aided Design (CAD), computer security and computer architecture. He is a Fellow of the IEEE and ACM. He has received the 2014 IEEE Computer Society Technical Achievement award, the 2015 ACM/IEEE Richard Newton technical impact award, and the 2017 IEEE Wallace McDowell award for his research. Devadas is a MacVicar Faculty Fellow and an Everett Moore Baker teaching award recipient, considered MIT's two highest undergraduate teaching honors.
Host: Professor Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Optimal Stochastic Control for Generalized Network Flow Problems
Thu, Jan 25, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Eytan Modiano, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology
Talk Title: Optimal Stochastic Control for Generalized Network Flow Problems
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: We will describe a new online dynamic policy, called Universal Max-Weight (UMW), for throughput-optimal routing and scheduling in wireless networks with an arbitrary mix of unicast, broadcast, multicast and anycast traffic. To the best of our knowledge, UMW is the first throughput-optimal algorithm for solving the generalized network-flow problem. Building upon UMW, we also design an admission control, routing and scheduling policy that maximizes network utility, while simultaneously keeping the physical queues in the network stable.
When specialized to the unicast setting, the UMW policy yields a throughput-optimal, loop-free, routing and link-scheduling policy. This is in contrast to the Back-Pressure (BP) policy which allows for packet cycling, resulting in excessive latency. Extensive simulation results show that the proposed UMW policy incurs substantially smaller delays as compared to backpressure. Conceptually, the UMW policy is derived by relaxing the precedence constraints associated with multi-hop routing and then solving a min-cost routing and max-weight scheduling problem on a virtual network of queues. The proof of optimality combines ideas from stochastic Lyapunov theory with a sample path argument from adversarial queueing theory.
Biography: Eytan Modiano received his B.S. degree in Electrical Engineering and Computer Science from the University of Connecticut at Storrs in 1986 and his M.S. and PhD degrees, both in Electrical Engineering, from the University of Maryland, College Park, MD, in 1989 and 1992 respectively. He was a Naval Research Laboratory Fellow between 1987 and 1992 and a National Research Council Post Doctoral Fellow during 1992-1993. Between 1993 and 1999 he was with MIT Lincoln Laboratory. Since 1999 he has been on the faculty at MIT, where he is a Professor and Associate Department Head in the Department of Aeronautics and Astronautics, and Associate Director of the Laboratory for Information and Decision Systems (LIDS).
His research is on communication networks and protocols with emphasis on satellite, wireless, and optical networks. He is the co-recipient of the MobiHoc 2016 best paper award, the Wiopt 2013 best paper award, and the Sigmetrics 2006 Best paper award. He is the Editor-in-Chief for IEEE/ACM Transactions on Networking, and served as Associate Editor for IEEE Transactions on Information Theory and IEEE/ACM Transactions on Networking. He was the Technical Program co-chair for IEEE Wiopt 2006, IEEE Infocom 2007, ACM MobiHoc 2007, and DRCN 2015. He is a Fellow of the IEEE and an Associate Fellow of the AIAA, and served on the IEEE Fellows committee.
Host: Professor Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White