Fall 2019 Seminars
A complete listing
|Sep 11||3pm - 4pm||Seisuke Kyochi||The University of Kitakyushu, Japan||Sparsity/Low-rankness-aware proximal methods and their applications to signal processing||370 Jay, Room: 824|
|Sep 12||11am - 12pm||Jie Wu||Temple University||Collaborative Mobile Charging: From Abstraction to Solution||370 Jay, Room: 1013|
|Sep 19||11am - 12pm||Nitin Vaidya||McDevitt Chair, Department of Computer Science, Georgetown University||Security and and Privacy for Distributed Optimization and Learning||370 Jay, Room: 824|
|Sep 20||11am - 12pm||Vladimir V. Vantsevich||The University of Alabama at Birmingham||Manned and Autonomous Vehicle Dynamics and Design: Towards Convergence and Transdisciplinary Technologies||370 Jay, Room: 824|
|Sep 26||11am - 12pm||Guru Prasadh Venkataramani||George Washington University||Unmasking and Defeating Cache Timing Channels||370 Jay, Room: TBD|
|Oct 7||11am - 12pm||Leon Bottou||Facebook AI Research||370 Jay, 1201 Seminar Room|
|Oct 8||11am - 12pm||José Moura||Carnegie Mellon University||Jack Keil Wolf Lecture Series: Understanding Behaviors in Instrumented Cities: Challenges and Opportunities||370 Jay, Room: TBD|
|Oct 10||11am - 12pm||Serge Leef||DARPA||TBD||370 Jay, Room: TBD|
|Oct 17||11am - 12pm||Slawomir Stanczak||Heinrich Hertz Institute, Germany||TBD||370 Jay, Room: TBD at 9th Floor|
|Oct 24||11am - 12pm||Flavio du Pin Calmon||Harvard University||TBD||370 Jay, Room: TBD|
|Oct 31||11am - 12pm||Kurt Becker||TBD||370 Jay, Room: TBD|
|Nov 14||11am - 12pm||Francis Bach||INRIA, Paris France||AI Seminar Series: Distributed Machine Learning over Networks||370 Jay, 1201 Seminar Room|
|Dec 6||11am - 12pm||Raiai Hadsell||DeepMind||AI Seminar Series: TBD||6MTC/Rogers Hall MakerEvent Space|
Speaker: Seisuke Kyochi, The University of Kitakyushu, Japan
Abstract: Optimization with proximal method has shown to be effective and widely applied in many signal and image processing tasks. In particular, proximal operators for sparse and low-rank modeling have been proposed conventionally, since those characteristics often appear in signal processing. For example, in image recovery problem, the sparsity of gradient is often considered to estimate a latent image, since natural images are basically smooth. For more accurate signal and image processing, more sophisticated structured proximal operators are discussed recently. In this talk, some examples of sparsity/low-rankness-aware proximal methods from the author's group are introduced and shown their effectiveness in experiments.
About the Speaker: Seisuke Kyochi (S’08–M’10) received the B.S. degree in mathematics from Rikkyo University, Japan, in 2005, and the M.E. and Ph.D. degrees from Keio University, Japan, in 2007 and 2010, respectively. He was a Researcher with NTT Cyberspace Laboratories, from 2010 to 2012. In 2012, he joined The University of Kitakyushu and, since 2015, he has been an Associate Professor with the Faculty of Environmental Engineering. His research interests include the theory and design of wavelets/filter banks for efficient image processing applications, convex optimization in signal processing.
Speaker: Jie Wu, Temple University
Abstract: Wireless energy charging using mobile vehicles has been a viable research topic recently in the area of wireless networks and mobile computing. This talk gives a short survey of recent research conducted in our research group in the area of collaborative mobile charging. In collaborative mobile charging, multiple mobile chargers work together to accomplish a given set of objectives. These objectives include charging sensors at different frequencies with a minimum number of mobile chargers and reaching the farthest sensor for a given set of mobile chargers, subject to various constraints, including speed and energy limits of mobile chargers. Testbed results are also given on the proposed mobile charging solutions. Through the process of problem formulation, solution construction, and future work extension for problems related to collaborative mobile charging and coverage, we present three principles for good practice in conducting research, that is, select a simple problem, find an elegant solution, and use imagination for extension.
About the Speaker: Jie Wu is the Director of the Center for Networked Computing and Laura H. Carnell professor at Temple University. He also serves as the Director of International Affairs at College of Science and Technology. He served as Chair of Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University. His current research interests include mobile computing and wireless networks, routing protocols, cloud and green computing, network trust and security, and social network applications. Dr. Wu regularly publishes in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Mobile Computing, IEEE Transactions on Service Computing, the Journal of Parallel and Distributed Computing, and the Journal of Computer Science and Technology. Dr. Wu was general co-chair for IEEE MASS 2006, IEEE IPDPS 2008, IEEE ICDCS 2013, ACM MobiHoc 2014, ICPP 2016, and IEEE CNS 2016, as well as program co-chair for IEEE INFOCOM 2011 and CCF CNCC 2013. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a Fellow of the AAAS and a Fellow of the IEEE.
Speaker: Nitin Vaidya, McDevitt Chair, Department of Computer Science, Georgetown University
Abstract: Consider a network of agents wherein each agent has a private cost function. In the context of distributed machine learning, the private cost function of an agent may represent the “loss function” corresponding to the agent’s local data. The objective here is to identify parameters that minimize the total cost over all the agents. In machine learning for classification, the cost function is designed such that minimizing the cost function should result model parameters that achieve higher accuracy of classification. Similar problems arise in the context of other applications as well, including swarm robotics.
Our work addresses privacy and security of distributed optimization with applications to machine learning. In privacy-preserving machine learning, the goal is to optimize the model parameters correctly while preserving the privacy of each agent’s local data. Privacy-preserving machine learning is becoming important due to the increasing reliance on user-generated data for machine learning. In security, the goal is to identify the model parameters correctly while tolerating adversarial agents that may be supplying incorrect information. When a large number of agents participate in distributed optimization, security compromise of some of the agents becomes increasingly likely. We constructively show that such privacy-preserving and secure algorithms for distributed optimization exist. The talk will provide intuition behind the design and correctness of the algorithms.
About the Speaker: Nitin Vaidya is the Robert L. McDevitt, K.S.G., K.C.H.S. and Catherine H. McDevitt L.C.H.S. Chair of Computer Science at Georgetown University. He received Ph.D. from the University of Massachusetts at Amherst. He previously served as a Professor and Associate Head in Electrical and Computer Engineering at the University of Illiniois at Urbana-Champaign. He has co-authored papers that received awards at several conferences, including 2015 SSS, 2007 ACM MobiHoc and 1998 ACM MobiCom. He is a fellow of the IEEE. He has served as the Chair of the Steering Committee for the ACM PODC conference, as the Editor-in-Chief for the IEEE Transactions on Mobile Computing, and as the Editor-in-Chief for ACM SIGMOBILE publication MC2R.
Manned and Autonomous Vehicle Dynamics and Design: Towards Convergence and Transdisciplinary Technologies
Speaker: Vladimir Vantsevich, University of Alabama at Birmingham
Abstract: This seminar discusses four emerging research directions in manned and autonomous ground vehicle dynamics and vehicle design that provide necessary conditions for creating vehicle transformative technologies:
1. Vehicle operational properties and vehicle system design
2. Specific features of autonomous vehicle dynamics and design
3. Coupled and interactive dynamics of vehicle systems
4. Vehicle mechatronics and intelligent physical systems
5. Open architecture system design
6. Agile tire and vehicle dynamics
Wheel-power-distribution optimization and control ensured by mechanical and mechatronic driveline systems and by hybrid and electrical virtual/distributed driveline systems is presented as a key foundation of the above-listed research directions.
Several research and engineering projects accomplished for industry and government within the four research avenues are presented to illustrate the effectiveness of the proposed analytical methods and engineered systems, including a new hybrid-electric power transmitting unit, a new wheel rotational kinematics sensor, various limited slip and controllable differentials, and wheel power management control algorithms for fully electric vehicles. The seminar presents and discusses results on fundamental improvements of terrain mobility, vehicle energy efficiency, maneuver, stability of motion and safety of terrain vehicles.
About the Speaker: Dr. Vladimir V. Vantsevich joined the UAB Department of Mechanical Engineering as a Professor and the founding Director of Vehicle and Robotics Engineering Laboratory in 2012 (Secondary appointment is in the ECE Dept.). Prior to UAB he was a Professor and the founding Director of the M.S. in Mechatronic Systems Engineering Program and the Laboratory of Mechatronic Systems at Lawrence Technological University, Michigan. He was also a co-founder and served as Associate Director/Director of the LTU Automotive Engineering Institute. Before Lawrence Tech, Dr. Vantsevich was a Professor and the Head of Research and Design Group on Multi-Wheel Drive Vehicles that designed and developed mechatronic and mechanical driveline systems for various purpose vehicles in Belarus. He earned his Ph.D. and Sc.D. degrees from Belarusian National Technical University.
Prof. Vantsevich’s research area is mechanical and intelligent mechatronic multi-physics systems, system modeling, design and control. Applications include conventional and autonomous multi-wheel ground vehicles, and vehicle driveline systems. He developed a new research avenue – inverse ground vehicle dynamics, which is the basis of his optimization of power distribution among the drive wheels and control of vehicle mobility, energy/fuel consumption, traction performance, maneuver and stability of motion. Prof. Vantsevich’s recent research work is on coupled and interactive dynamics of vehicle systems, agile tire dynamics, virtual drivelines for electric and hybrid vehicles, and protection of vehicle sensors from cyber-threats.
He is author of 6 technical books and more than 170 research articles. Prof. Vantsevich delivered more than 160 science and engineering seminars, invited lectures and technical presentations to industry, academic institutions and professional societies across 17 countries. He is a registered inventor of the U.S.S.R. with 30 certified inventions and holds US invention disclosures. Prof. Vantsevich is the Founder and Editor of two book series: (i) Ground Vehicle Engineering at Taylor and Francis Group/CRC Press and (ii) Robotics Engineering at ASME Press. Dr. Vantsevich is Editor-in-Chief of the Journal of Terramechanics and Associate Editor of the SAE International Journal of Commercial Vehicles; he is a member of the Editorial Board of the International Journal of Vehicle Autonomous Systems.
Prof. Vantsevich was honored with Fellowship of the American Society of Mechanical Engineers. He is the Technical Committee Chair on Transportation Machinery at the International Federation for the Promotion of Mechanism and Machine Science. He served as the Chair of the ASME Vehicle Design Committee (VDC). Prof. Vantsevich is the founder of the ASME VDC William Milliken Invited Lecture Award. He is also a member of Association for Unmanned Vehicle Systems International, International Society for Terrain-Vehicle Systems, Society of Automotive Engineers, and International Association for Vehicle System Dynamics. His major awards include the ASME Design Engineering Division Thar Energy Design Award for significant contributions to the design research, innovation and product design in the areas related to energy engineering; Hyundai Distinguished Lecturer; Laureate of the National Academy of Sciences of Belarus for the Best Mechanical Engineering Book of the Year; Biographical article in the Encyclopedia of the Republic of Belarus; Certificate of an Honorary Citizen of Dallas, TX, and Medal of the U.S.S.R. Government for exemplary efficiency in work.
Speaker: Guru Prasadh Venkataramani, George Washington University
Abstract: Caches present a large attack surface for adversaries to exploit and realize their timing channels. With advances in cache monitoring and protection techniques within individual caches, adversaries may resort to more sophisticated methods such as taking undue advantage of inter-cache hardware mechanisms in order to meet their malicious objectives. It becomes necessary to understand the hardware vulnerabilities and devise mitigation mechanisms to prevent adversaries from exploiting them.
In this talk, I will first briefly describe CC-Hunter our early work that detects cache timing channels using correlation patterns between cache misses. I will then go over some of our recent research that highlights how performance monitoring hardware can be leveraged for better security. Next, we will see how more sophisticated adversaries may target inter-cache hardware mechanisms such as cache coherence protocols.
About the Speaker: Guru Prasadh Venkataramani is an associate professor of electrical and computer engineering at George Washington university in Washington, DC. He received his PhD from Georgia Tech, and his current research interests are in computer architecture and security. He is a recipient of NSF Career Award, ORAU Ralph E. Powe junior faculty enhancement award, best poster award in PACT'11, GWU Hegarty Award for faculty in innovation. His research has been funded by NSF, ONR and SRC. He served as a General Chair for IEEE HPCA 2019, and is a senior member of both IEEE and ACM.
Speaker: José M F Moura, Carnegie Mellon University
Abstract: Cities are increasingly instrumented and many of their activities progressively digitized. How and what can be done and can be gained from the datasets that are and will continue to be gathered. In this talk, we consider the challenges and opportunities to extract relevant behaviors from real time NYC webcam videos and offline pick-up and drop-off taxi records.
About the Speaker: José M. F. Moura is the Philip L. and Marsha Dowd University Professor at CMU. His areas of expertise are signal and image processing, graph signal processing, data science, and learning. Two of his patents (co-inventor Alek Kavcic) are used in over three billion hard disk drives in 60% of all computers sold in the last 13 years, and they were the subject of a $750million settlement between CMU and a chip semiconductor company. He serves as IEEE President and CEO and is a member of the US NAE.