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Events for the 1st week of November
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Fall 2018 Joint CSC@USC/CommNetS-MHI Seminar Series
Mon, Oct 29, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Amir Rahmani, NASA Jet Propulsion Laboratory
Talk Title: Swarm Autonomy and a New Era of Space Exploration
Abstract:
Teams and swarms of autonomous robots and spacecraft hold the promise to change the way some missions are designed and provide new mission opportunities. Monolithic systems can be traded for a swarm of interconnected and coordinating assets. Swarm robotics has reached a level of maturity that can be reliably fielded. NASA's Jet Propulsion Laboratory has long enjoyed leadership in spacecraft formation flying and swarm robotics. This talk will present an overview of JPL's multi-agent autonomy tasks and technologies, including our multi-mission multi-agent autonomy architecture, as well as a number of multi-robot motion-planning tools developed at JPL.
Biography: Dr. Amir Rahmani has a Ph.D. from University of Washington in aeronautics and astronautics and was an assistant professor of aerospace engineering at the University of Miami prior to joining JPL. He has over a decade research experience in distributed space systems, formation flying, as well as swarm robotics. He is the NASA Small Business Technology Transfer (STTR) subtopic manager for coordination and control of swarm of space vehicles.
Host: Mihailo Jovanovic, mihailo@usc.edu
More Info: http://csc.usc.edu/seminars/2018Fall/rahmani.html
More Information: 18.10.29_Amir_Rahmani_NASA_Seminar.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Brienne Moore
Event Link: http://csc.usc.edu/seminars/2018Fall/rahmani.html
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Resilient Distributed Inference in Cyber-Physical Systems
Wed, Oct 31, 2018 @ 12:00 PM - 01:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Soummya Kar, Carnegie Mellon University
Talk Title: Resilient Distributed Inference in Cyber-Physical Systems
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In applications such as large-scale cyber-physical systems (CPS) and Internet-of-Things (IoT), as the number of devices or agents continues to grow, the integrity and trustworthiness of data generated by these devices becomes a pressing issue of paramount importance. An adversary may hijack individual devices or the communication channel between devices to maliciously alter data streams. In numerous IoT applications, we deploy physical devices throughout an environment, and we are interested in using the stream of sensor measurements to make inferences about the environmental state. Due to the large-scale and distributed nature of devices and data it might be infeasible to carry out computation and decision-making in a classical centralized fashion as well as to prevent attacks and intrusions on all data sources. As a result, reactive countermeasures, such as intrusion detection schemes and resilient inference algorithms become a vital component of security in distributed IoT-type setups.
As an alternative to traditional fusion-center based cloud setups, in this talk we focus on fog-type architectures in which devices themselves perform the necessary computations using local data and peer-to-peer information exchange with neighboring devices to make inferences about an environment. In the first part of the talk, we review distributed inference approaches and algorithms based on the consensus+innovations paradigm. We discuss performance metrics such as rates of convergence, communication complexity, and optimality. The second part of the talk focuses on recent work on secure and resilient variants of these algorithms in adversarial environments. Specifically, focusing on the case of data integrity attacks on the device network, we characterize fundamental trade-offs between resilience, quantified in terms of achievable inference performance and ability to detect intrusions and threats, and model properties such as observability and connectivity of the inter-device communication network.
Biography: Soummya Kar received a B.Tech. in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur, India, in May 2005 and a Ph.D. in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 2010. From June 2010 to May 2011, he was with the Electrical Engineering Department, Princeton University, Princeton, NJ, USA, as a Postdoctoral Research Associate. He is currently an Associate Professor of Electrical and Computer Engineering at Carnegie Mellon University, Pittsburgh, PA, USA. His research interests include decision-making in large-scale networked systems, stochastic systems, multi-agent systems and data science, with applications to cyber-physical systems and smart energy systems. Recent recognition of his work includes the 2016 O. Hugo Schuck Best Paper Award from the American Automatic Control Council and a 2016 Dean's Early Career Fellowship from CIT, Carnegie Mellon.
Host: Professor Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Sample Complexity of Partition Identification using Multi-armed Bandits with Applications to Nested Monte Carlo
Fri, Nov 02, 2018 @ 02:00 AM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Sandeep Juneja, TIFR, Mumbai, India
Talk Title: Sample Complexity of Partition Identification using multi-armed Bandits with Applications to Nested Monte Carlo
Series: Special/Joint CPS/CommNetS Seminar
Abstract: Given a vector of probability distributions, or arms, each of which can be sampled independently, we consider the problem of identifying the partition to which this vector belongs from a finitely partitioned universe of such vector of distributions. We study this as a pure exploration problem in multi-armed bandit settings and develop sample complexity bounds on the total mean number of samples required for identifying the correct partition with high probability. This framework subsumes well-studied problems in the literature such as finding the best arm or the best few arms. We consider distributions belonging to the single parameter exponential family and primarily consider partitions where the vector of means of arms lie either in a given set or its complement. The sets considered correspond to distributions where there exists a mean above a specified threshold, where the set is a half space and where either the set or its complement is convex. In all these settings, we characterize the lower bounds on mean number of samples for each arm. Further, we propose algorithms that can match these bounds asymptotically with decreasing probability of error. Applications of this framework may be diverse. We briefly discuss a few associated with nested Monte Carlo and its applications to finance.
Biography: Sandeep is a Professor and Dean at the School of Technology and Computer Science in Tata Institute of Fundamental Research in Mumbai. His research interests lie in applied probability including in mathematical finance, Monte Carlo methods, multi-armed bandit based sequential decision making, and game theoretic analysis of queues. He is currently on the editorial board of Stochastic Systems. Earlier he has been on editorial boards of Mathematics of Operations Research, Management Science and ACM TOMACS.
Host: Rahul Jain
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
