Events for the 1st week of April
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EE-EP Faculty Candidate, Marina Radulaski, Monday, April 2nd at 12pm in EEB 132
Mon, Apr 02, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Marina Radulaski, Stanford University
Talk Title: Scalable Nanophotonics for Quantum and Classical Information Processing
Abstract: Technological commodities of the 21st century come with exponential demands on information processing. While the electronic devices face physical limits of scalability, nanophotonics emerges as a leading solution for the Big Data manipulation. In the first part of the seminar, I will discuss the role of novel photonic architectures and robust device design algorithms in meeting the short-term classical hardware speedup goals. Moving toward the implementation of quantum information processing paradigms, I will evaluate applicability of color centers in silicon carbide and diamond to quantum computing, communication and cryptography. Finally, I will present advances in integration of color centers with nanoscale photonic devices serving as efficient quantum bits and quantum light sources.
Biography: Marina Radulaski is a Nano- and Quantum Science and Engineering Postdoctoral Fellow at Stanford University's Ginzton Laboratory. She obtained a PhD in Applied Physics from Stanford University under the supervision of Prof. Jelena Vuckovic, a BSc/MSc in Physics from the University of Belgrade, Serbia, and a BSc/MSc in Computer Science from the Union University, Serbia. Marina was selected among the Rising Stars in EECS in 2017, Stanford Graduate Fellows 2012-2014, and Scientific American's "30-Under-30 Up and Coming Physicists" in 2012. She has performed research internationally at Berkeley Lab, Hewlett-Packard Labs, Oxford University, IQOQI Vienna, Helmholtz Center Berlin, and more. In addition to research, Marina enjoys building communities and promoting science through podcasts, videos and festivals.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Apr 02, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: TBA, TBA
Talk Title: TBA
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: TBA
Biography: TBA
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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EE Seminar - Robust Model-Free Control, Optimization, and Learning in Cyber-Physical Societal Systems
Mon, Apr 02, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jorge I. Poveda, University of California, Santa Barbara
Talk Title: Robust Model-Free Control, Optimization, and Learning in Cyber-Physical Societal Systems
Abstract: The deployment of advanced real-time control and optimization strategies in socially-integrated engineering systems could significantly improve our quality of life while creating jobs and economic opportunity. However, in cyber-physical systems such as smart grids, transportation networks, healthcare, and robotic systems, there still exist several challenges that prevent the implementation of intelligent control strategies. These challenges include the existence of limited communication networks, dynamic environments, multiple decision makers interacting with the system, and complex hybrid dynamics emerging from the feedback interconnection of physical processes and computational devices. In this talk, I will present a set of tools for the analysis and design of model-free feedback mechanisms that can cope with these challenges, and that are suitable for the real-time control and optimization of cyber-physical societal systems. The first part of the talk will focus on the problem of designing a class of robust model-free adaptive pricing mechanisms for systems such as the smart grids, transportation networks, and the Internet, where users behave in a selfish way, and where the objective of the social planner is to maximize the total welfare of the system. Next, I will show that this problem belongs to a broader family of model-free extremization problems, and I will present a general framework for the design of a family of algorithms that can successfully optimize the performance of cyber-physical systems having unknown mathematical models. Finally, I will illustrate how these results can be extended to achieve distributed control of large-scale autonomous systems by implementing novel robust coordination and synchronization feedback mechanisms. The talk will finish by discussing some future directions and preliminary results in the areas of data-driven hybrid control and security in stochastic learning dynamics.
Biography: Jorge I. Poveda is a Ph.D. Candidate at the Center for Control, Dynamical Systems, and Computation (CCDC) at the University of California, Santa Barbara. He received the B.S. degrees in Electronics Engineering and Mechanical Engineering in 2012, and the M.S. degree (Magna Cum Laude) in Electrical Engineering in 2013, all from University of Los Andes, Bogota, Colombia, and the M.S. degree in Electrical and Computer Engineering from the University of California, Santa Barbara, USA, in 2015. He was a Research Intern with the Mitsubishi Electric Research Laboratories in Cambridge, MA, during the summers of 2016 and 2017. He received the 2013 CCDC Outstanding Scholar Fellowship at UCSB, and was a finalist for the Best Student Paper Award at the 56th IEEE Conference on Decision and Control in 2017. His main research interests lie at the intersection of robust feedback control theory, adaptive control, online optimization, and game theory, with applications to cyber-physical and societal systems.
Host: Ashutosh Nayyar, ashutosn@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE Seminar - Embracing Uncertainty: from Differential Privacy to Generative Adversarial Privacy
Tue, Apr 03, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Peter Kairouz, Postdoctoral Scholar, Stanford University
Talk Title: Embracing Uncertainty: from Differential Privacy to Generative Adversarial Privacy
Abstract: The explosive growth in connectivity and data collection is accelerating the use of machine learning to guide consumers through a myriad of choices and decisions. While this vision is expected to generate many disruptive businesses and social opportunities, it presents one of the biggest threats to privacy in recent history. In response to this threat, differential privacy (DP) has recently surfaced as a context-free, robust, and mathematically rigorous notion of privacy.
The first part of my talk will focus on understanding the fundamental tradeoff between DP and utility for a variety of learning applications. Surprisingly, our results show the universal optimality of a family of extremal privacy mechanisms called staircase mechanisms. While the vast majority of early works on DP have focused on using the Laplace mechanism, our results indicate that it is often strictly suboptimal and can be replaced by a staircase mechanism to improve utility. Our results also show that the strong privacy guarantees of DP often come at a significant loss in utility.
The second part of my talk is motivated by the following question: can we exploit data statistics to achieve a better privacy-utility tradeoff? To address this question, I will present a novel context-aware notion of privacy called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to arrive to a unified framework for data-driven privacy that has deep game-theoretic and information-theoretic roots. I will conclude my talk by showcasing the performance of GAP on real life datasets.
Biography: Peter Kairouz is a postdoctoral scholar at Stanford University. He received his PhD in ECE from the University of Illinois at Urbana-Champaign (UIUC). He interned twice at Qualcomm and more recently at Google where he designed privacy-aware machine learning algorithms. He is the recipient of the 2015 ACM SIGMETRICS Best Paper Award, the 2012 Roberto Padovani Scholarship from Qualcomm's Research Center, and the 2016 Harold L. Olesen Award for Excellence in Undergraduate Teaching from UIUC. His research interests are interdisciplinary and span the areas of data and network sciences, privacy-preserving data analysis, machine learning, and information theory.
Host: Keith Chugg, chugg@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Robust Classification and Change Detection for Brain-Computer Interfaces
Wed, Apr 04, 2018 @ 02:00 AM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Vahid Tarokh, Duke University
Talk Title: Robust Classification and Change Detection for Brain-Computer Interfaces
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In this talk, we will first discuss eye movement decoding in a working memory experiment involving a macaque monkey. Our objective is to use the local field potentials (LFPs) collected from the brain of the monkey to decode the type of task that the monkey is doing, and the direction of saccade in each task. We will show that the LFP time-series data can be modeled using a nonparametric regression framework, and show that the classifiers trained using minimax function estimators as features are robust and consistent. We will also discuss application of the resulting classifier to the brain data.
We will then briefly discuss the problem of change detection apply it to spike data from a mice experiment collected using cues and electric shocks.
This is a joint work with Taposh Banerjee.
Biography: Vahid Tarokh is Rhodes Family Professor of Electrical and Computer Engineering, Professor of Mathematics, and Computer Science at Duke University. He worked at AT&T Labs-Research until 2000, and subsequently at MIT (as an Associate Professor of EECS) until 2002. He joined Harvard University as Perkins Professor of Applied Mathematics and Hammond Vinton Hayes Senior Fellow of Electrical Engineering. He then joined Duke University in January 2018. His current research focuses on statistical signal processing and applications. Dr. Tarokh has received a number of awards, and holds four honorary degrees.
Host: Prof. Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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EE Seminar - Beyond Binary Failures in Networks
Thu, Apr 05, 2018 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Monia Ghobadi, Researcher, Microsoft Research Mobility and Networking
Talk Title: Beyond Binary Failures in Networks
Abstract: Fiber optic cables are the workhorses of today's Internet services, but they are an expensive resource and require significant monetary investment. Their importance has driven a conservative deployment approach with redundancy baked into multiple layers of the network under the assumption that links have a constant reliability status and operate at a fixed capacity. In this talk, I take an unconventional approach and argue that link failures should not be always considered binary events; this approach enables the foundation of a framework for network links with dynamic capacity and reliability. I investigated this idea by conducting the first ever large-scale study of operational optical signals, analyzing over 2,000 channels in a wide-area network for a period of three years, as well as 350,000 links in 20 data center networks worldwide. My analysis uncovered several findings that enable cross-layer optimizations and smart algorithms to improve traffic engineering, increase capacity, and reduce cost. First, the capacity of 99% of wide-area links can be augmented by at least 50 Gbps, leading to an overall capacity gain of more than 100 Tbps. This means we get higher capacity and better availability using the same links. Second, I will show that 99.99% of data center links have an incoming optical power level that is higher than the design threshold; by allowing links to have multiple reliability levels, we can cut the cost of data center networks by nearly half. Finally, the framework opens the door to revisiting several classical networking problems, such as the maximum-flow problem and graph abstractions. Microsoft has invested in this new framework and is rolling out the necessary infrastructure for deployment.
Biography: Monia Ghobadi is a researcher at the Microsoft Research Mobility and Networking research group. Prior to MSR, she was a software engineer at Google. She received her Ph.D. in Computer Science at the University of Toronto and B.Eng. in Computer Engineering at the Sharif University of Technology. Monia is a computer systems researcher with a networking focus and has worked on a broad set of topics, including data center networking, optical networks, transport protocols, network measurement, and hardware-software co-design. Many of the technologies she has helped develop are part of real-world systems at Microsoft and Google. Monia was recognized as an N2women rising star in networking and communications in 2017. Her work has won the best dataset award, Google research excellent paper award (twice), and the ACM IMC best paper award.
Host: Konstantinos Psounis, kpsounis@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Landscape of Practical Blockchain Systems and their Applications
Thu, Apr 05, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Chandrasekaran Mohan, IBM Almaden Research Center
Talk Title: Landscape of Practical Blockchain Systems and their Applications
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The concept of a distributed ledger was invented as the underlying technology of the public or permissionless Bitcoin cryptocurrency network. But the adoption and further adaptation of it for use in the private or permissioned environments is what I consider to be of practical consequence and hence only such private blockchain systems will be the focus of this talk.
Computer companies like IBM, Intel, Oracle, Baidu and Microsoft, and many key players in different vertical industry segments have recognized the applicability of blockchains in environments other than cryptocurrencies. IBM did some pioneering work by architecting and implementing Fabric, and then open sourcing it. Now Fabric is being enhanced via the Hyperledger Consortium as part of The Linux Foundation. A couple of the other efforts include Enterprise Ethereum, Sawtooth and R3 Corda.
While currently there is no standard in the private blockchain space, all the ongoing efforts involve some combination of database, transaction, encryption, virtualization, consensus and other distributed systems technologies. Some of the application areas in which blockchain pilots are being carried out are: smart contracts, derivatives processing, e-governance, Know Your Customer (KYC), healthcare, supply chain management and provenance management.
In this talk, I will describe some use-case scenarios, especially those in production deployment. I will also survey the landscape of private blockchain systems with respect to their architectures in general and their approaches to some specific technical areas. I will also discuss some of the opportunities that exist and the challenges that need to be addressed. Since most of the blockchain efforts are still in a nascent state, the time is right for mainstream database and distributed systems researchers and practitioners to get more deeply involved to focus on the numerous open problems.
An earlier version of this talk was delivered as the opening keynote at the 37th IEEE International Conference on Distributed Computing Systems (ICDCS) in Atlanta (USA) on 6 June 2017. Extensive blockchain related collateral can be found at http://bit.ly/CMbcDB
Biography: Dr. C. Mohan has been an IBM researcher for 36 years in the database and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol. This IBM (1997), and ACM and IEEE (2002) Fellow has also served as the IBM India Chief Scientist for 3 years (2006-2009). In addition to receiving the ACM SIGMOD Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the US and Indian National Academies of Engineering (2009) and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. He is currently focused on Blockchain, Big Data and HTAP technologies (http://bit.ly/CMbcDB, http://bit.ly/CMgMDS). Since 2016, he has been a Distinguished Visiting Professor of China's prestigious Tsinghua University. He has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. Mohan is a frequent speaker in North America, Europe and India, and has given talks in 40 countries. He is very active on social media and has a huge network of followers. More information can be found in the Wikipedia page at http://bit.ly/CMwIkP
Host: Prof. Paul Bogdan
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Talyia White
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EE Seminar - A System Level Approach to the Design of Robust Autonomous Systems
Thu, Apr 05, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nikolai Matni, Postdoctoral Scholar, Dept of EECS, UC Berkeley
Talk Title: A System Level Approach to the Design of Robust Autonomous Systems
Abstract: As the systems we build and the environments that they operate in become more complex, first-principle modeling becomes either impossible, impractical, or intractable, motivating the use of machine learning techniques for their control. As impressive as the empirical success of these methods appears to be on stylized test-cases, strong theoretical guarantees of performance, safety, or robustness are few and far between; however, such guarantees are essential when data-driven methods are applied to safety-critical systems or infrastructures. In the first part of this talk, we make concrete steps towards developing performance and stability guarantees in the data-driven setting by considering a classical problem from the optimal control literature, the Linear Quadratic Regulator (LQR), with the added twist that now the system dynamics are unknown. We provide, to the best of our knowledge, the first end-to-end baselines for learning and control in an LQR problem that do not require restrictive or unrealistic assumptions. A key technical tool used in deriving this result is our recently developed System Level Approach (SLA) to Controller Synthesis. The SLA provides a transparent connection between system structure, constraints, and uncertainty and their effects on controller synthesis, implementation, and performance -” we exploit these properties to combine results from contemporary high-dimensional statistics and robust controller synthesis in a way that is amenable to non-asymptotic analysis. We then show how the solution to the "Learning-LQR" problem can be incorporated into an adaptive polynomial-time algorithm that achieves sub-linear regret. In the second part of this talk, we discuss how we can extend these ideas to large-scale data-driven autonomous systems, which encompass future incarnations of the smart-grid, intelligent transportation systems and software-defined networks. In this large-scale distributed setting, an additional challenge must be addressed: even when the system model is exactly known, designing robust systems with optimal performance guarantees is a challenging task. We show how the SLA allows for localized optimal controllers to be synthesized using convex programming, thus extending the performance and robustness guarantees of optimal/robust control, under mild and practically relevant assumptions, to systems of arbitrary size. We illustrate the usefulness of this approach with a frequency regulation problem in the power-grid, and show how it can be used to systematically explore tradeoffs in controller performance, robustness, and synthesis/implementation complexity. We conclude with our vision for a contemporary theory of autonomy and data-driven control, and outline ongoing efforts in extending the previous results to incorporate the guarantees of other learning and control paradigms, such as model predictive control and experiment design.
Biography: Nikolai is a postdoctoral scholar in EECS at UC Berkeley working with Benjamin Recht. Prior to that, he was a postdoctoral scholar in Computing and Mathematical Sciences at the California Institute of Technology. He received the B.A.Sc. and M.A.Sc. in Electrical Engineering from the University of British Columbia, and the Ph.D. in Control and Dynamical Systems from the California Institute of Technology in June 2016 under the advisement of John C. Doyle. His research interests broadly encompass the use of learning, layering, dynamics, control and optimization in the design and analysis of large-scale data-driven cyber-physical systems. He was awarded the IEEE CDC 2013 Best Student Paper Award, the IEEE ACC 2017 Best Student Paper Award (as co-advisor), and was an Everhart Lecture Series speaker at Caltech.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE-EP Faculty Candidate, Shimeng Yu, Friday, April 6th at 2pm in EEB 132
Fri, Apr 06, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Shimeng Yu, Arizona State University
Talk Title: Neuro-Inspired Computing with Resistive Synaptic Devices
Abstract: Resistive device is a two-terminal electronic device based on oxides/chalcogenides that can switch its resistance under programming voltage. This technology has made significant progresses in the past decade as a competitive candidate for the next generation non-volatile memory (NVM), namely resistive random access memory (RRAM). In this presentation, I will discuss its new applications in the context of neuro-inspired computing, as it has a great potential to serve as the synaptic devices in the neuromorphic hardware such as machine/deep learning accelerators. First, I will discuss the desired characteristics of the resistive synaptic devices (e.g. analog multilevel states, weight tuning linearity, variation/noises) and oscillation neuron devices, and show the representative device prototypes of offline training and online training. Next, I will introduce the crossbar array architecture to efficiently implement the weighted sum and weight update operations that are commonly used in the machine/deep learning algorithms, and show the array-level experimental demonstrations for these key operations such as the convolution kernel. Then I will introduce "NeuroSim", a device-circuit-algorithm co-design framework to evaluate the impact of non-ideal device effects on the neuromorphic system performance (i.e. learning accuracy) and trade-offs in the circuit-level performance (i.e. area, latency, energy). Last, I propose to possible future research directions including new materials and device engineering for achieving linear weight update, binarizing neural network algorithm by allowing binary memory cells and our efforts in chip-scale tape-out of a XNOR-Net accelerator with SRAM and heterogeneous integration of RRAM on top of CMOS. This presentation will be concluded with a holistic view of my research vision from materials/device engineering, and circuit/architecture co-optimization for neuro-inspired computing with emerging nanoelectronic devices.
Biography: Shimeng Yu received the B.S. degree in microelectronics from Peking University, Beijing, China in 2009, and the M.S. degree and Ph.D. degree in electrical engineering from Stanford University, Stanford, CA, USA in 2011, and in 2013, respectively. He is currently an assistant professor of electrical engineering and computer engineering at Arizona State University, Tempe, AZ, USA.
His research interests are emerging nano-devices and circuits with a focus on the resistive memories for different applications including machine/deep learning, neuromorphic computing, monolithic 3D integration, hardware security, radiation-hard electronics, etc. He has published >70 journal papers and >100 conference papers with citations >5500 and H-index 34.
Among his honors, he is a recipient of the DOD-DTRA Young Investigator Award in 2015, the NSF Faculty Early CAREER Award in 2016, the ASU Fulton Outstanding Assistant Professor in 2017 and the IEEE Electron Devices Society Early Career Award in 2017.
He served the Technical Program Committee for IEEE International Symposium on Circuits and Systems (ISCAS) 2015-2017, ACM/IEEE Design Automation Conference (DAC) 2017-2018, and IEEE International Electron Devices Meeting (IEDM) 2017-2018, etc.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski