SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for April
-
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE-EP Faculty Candidate, Negar Reiskarimian - Monday, April 9th at 12pm in EEB 132
Mon, Apr 09, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Negar Reiskarimian, Columbia University
Talk Title: Breaking Lorentz Reciprocity: From New Physical Concepts to Applications
Abstract: Lorentz reciprocity is a fundamental characteristic of the vast majority of electronic and photonic structures. However, breaking reciprocity enables the realization of non-reciprocal components, such as isolators and circulators, which are critical to electronic and optical communication systems, as well as new components and functionalities based on novel wave propagation modes. In this talk, I will present a novel approach to break Lorentz reciprocity based on linear periodically-time-varying (LPTV) circuits. We have demonstrated the world's first CMOS passive magnetic-free non-reciprocal circulator through spatio-temporal conductivity modulation. Since conductivity in semiconductors can be modulated over a much wider range than the more traditionally exploited permittivity, our structure is able to break reciprocity within a compact form factor with very low loss and high linearity. I will discuss fundamental limits of space-time modulated nonreciprocal structures, as well as new directions to build non-reciprocal components which can ideally be infinitesimal in size. Furthermore, I cover some of the applications of nonreciprocal components in wireless communication systems.
Looking to the future, I am broadly interested in exploring novel fundamental physical concepts that have strong engineering applications. I wish to work in an interdisciplinary area between integrated circuit design and closely related fields such as applied physics, applied electromagnetics and nanophotonics, and to identify and investigate ideas and concepts that can best be implemented using the semiconductor platform. Finally, I will share with you some examples of the exciting research directions I would like to pursue with the aim of participating in building the next generation of technologies that augment human lives.
Biography: Negar Reiskarimian received the Bachelor's and Master's degrees in electrical engineering from Sharif University of Technology in Iran, and is currently a PhD candidate in Electrical Engineering at Columbia University. She has published in top-tier IEEE IC-related journals and conferences, as well as broader-interest high-impact journals in the Nature family. Her research has been widely covered in the press, and featured in IEEE Spectrum, Gizmodo and EE Times among others. She is the recipient of numerous awards and fellowships, including Forbes 30 under 30, Paul Baran Young Scholar, Qualcomm Innovation Fellowship and multiple IEEE societies awards and fellowships.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Apr 09, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Anders Rantzer, Lund University
Talk Title: Towards a Scalable Theory of Control
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: Classical control theory does not scale well for large systems like traffic networks, power networks and chemical reaction networks. To change this situation, new approaches need to be developed, not only for analysis and synthesis of controllers, but also for modelling and verification. In this lecture we will present a class of networked control problems for which scalable distributed controllers can be proved to achieve the same performance as the best centralized ones. The control objective is stated in terms of frequency weighted H-infinity norms, which makes it possible to combine disturbance rejection at low frequencies with robustness to high frequency measurement noise and model errors. An optimal controller is given in the form of a multi-variable PI controller, which is distributed in the sense that control action along a given network edge is entirely determined by states at nodes connected by that edge. We will discuss some application examples, as well as connections to other aspects of scalability.
Biography: Anders Rantzer received a PhD in 1991 from KTH, Stockholm, Sweden. After postdoctoral positions at KTH and at IMA, University of Minnesota, he joined Lund University in 1993 and was appointed professor of Automatic Control in 1999. During the academic year of 2004-2005 he was visiting associate faculty member at Caltech and 2015-2016 he was Taylor Family Distinguished Visiting Professor at the University of Minnesota. Since 2008 he coordinates the Linnaeus center LCCC at Lund University.
Professor Rantzer is an editorial board member of Proceedings of the IEEE and several other publications. He is a winner of the SIAM Student Paper Competition, the IFAC Congress Young Author Price, and the award for best article in IEE Proceedings - Control Theory and Applications. He is a Fellow of IEEE, a member of the Royal Swedish Academy of Engineering Sciences, and former chairman of the Swedish Scientific Council for Natural and Engineering Sciences.
His research interests are in modeling, analysis and synthesis of control systems, with particular attention to uncertainty, optimization, scalability and adaptation.
Host: Mihailo Jovanovic, mihailo@usc.edu
More Information: rantzer.jpg (JPEG Image, 300 × 400 pixels).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar: Analysis, Design, and Operation of Secure Cyber-Physical Systems
Tue, Apr 10, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Fabio Pasqualetti, Assistant Professor, University of California, Riverside
Talk Title: Analysis, Design, and Operation of Secure Cyber-Physical Systems
Abstract: Today's cyber-physical systems are the building blocks of smart and citizen-centric applications that will revolutionize the way people interact with the urban environment. Smart systems, cities, and communities will emerge, in which advanced levels of autonomy hold the promise of greater efficiency, reliability and sustainability in areas of national interest and social need, such as health, energy, and transportation. In this new realm of applications, however, enhanced connectivity and advanced autonomy will also pose novel and significant risks to people and the infrastructure, including safety, security, and privacy.
In this talk, I present a unified framework for the analysis of fundamental vulnerabilities affecting cyber-physical systems, the design of targeted detection and protection schemes, and the construction of systems that are provably resilient to accidental malfunctions and malicious attacks. I show how cyber-physical security differs from well-established disciplines, including cyber security and fault tolerance, and how our control- and graph-theoretic methods complement existing security practices to fully protect cyber-physical systems. Further, I reveal a novel class of integrity attacks against smart power grids, and show how these attacks lead to the formulation of novel sparse network control problems, which we also solve. Finally, I discuss directions of future research and open questions in cyber-physical security.
Biography: Fabio Pasqualetti is an Assistant Professor in the Department of Mechanical Engineering, University of California, Riverside. He completed a Doctor of Philosophy degree in Mechanical Engineering at the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree (M.Sc. equivalent) in Automation Engineering at the University of Pisa, Italy, in 2007, and a Laurea degree (B.Sc. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2004. He received a Young Investigator Program award from ARO in 2017, and the 2016 TCNS Outstanding Paper Award from IEEE CSS. His main research interest is in secure control systems, with application to multi-agent networks, distributed computing, and power networks. Other interests include computational neuroscience, vehicle routing, and combinatorial optimization.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Research and Technology Seminar
Wed, Apr 11, 2018 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sunil Bharitkar, Distinguished Member of Tech. Staff (HP Labs)
Talk Title: Advances in Joint Signal Processing, Perception, and Machine Learning at HP Labs
Abstract: In HP's Emerging Compute Lab, research is being conducted at the intersection of signal processing, auditory perception and machine learning to create fundamentally new experiences for differentiation in HP devices including VR HMD. In this talk we will present various techniques and algorithms, incorporating knowledge of binaural perception, machine learning, and signal processing, to enhance low-frequency perception, spatial rendering, and automated content classification. The research results have been validated through perceptual testing in large-scale studies giving statistically meaningful results. Ongoing research being conducted in the areas deep learning (stacked autoencoders and LSTM) for VR head-related transfer function synthesis, content classification, speech and multimodal biometrics, sensing towards emotion interpretation, and cancer cell data classification (jointly with Life Sciences Lab) will also be presented. The presentation will be accompanied with demonstrations.
Biography: Sunil Bharitkar received his Ph.D. in Electrical Engineering from the University of Southern California (USC) in 2004 and is involved in research in speech/audio analysis and processing including spatial audio for AR/VR, biometric & biomedical signal processing, multimodal signal processing, and machine learning. From 2011-2016 he was at Dolby leading/guiding research in audio, signal processing, haptics, machine learning, hearing augmentation, and standardization activities at ITU, SMPTE, AES. He co-founded the company Audyssey Laboratories in 2002 where he was VP of Research and responsible for inventing new technologies which were licensed to companies including IMAX, Denon, Audi, Sharp, etc. He also taught in the Department of Electrical Engineering at USC. Sunil has published over 50 technical papers and has over 20 patents in the area of signal processing applied to acoustics, neural networks and pattern recognition, and a textbook (Immersive Audio Signal Processing) from Springer-Verlag. He is a reviewer for papers at various conferences and journals. He has also been on the Organizing and Technical Program Committees of various conferences such as the 2008 and 2009 European Sig. Proc. Conference (EUSIPCO), the 57th AES Conference, SMPTE Conferences. He has also served as an invited tutorial speaker at the 2006 IEEE Conf. on Acoustics Speech and Signal Processing (ICASSP). He is a Senior Member of the IEEE, the Acoustical Soc. of America (ASA), European Association for Signal and Image Processing (EURASIP), and the Audio Eng. Soc. (AES). Sunil is a PADI diver and enjoys playing the Didgeridoo.
Host: Panos Georgiou and Shri Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Cathy Huang
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
From Gaussian Multiterminal Source Coding to Distributed Karhunen Loève Transform
Wed, Apr 11, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jun Chen, Department of Electrical and Computer Engineering, McMaster University
Talk Title: From Gaussian Multiterminal Source Coding to Distributed Karhunen Loève Transform
Series: Joint Seminar Series Seminar Series on Cyber-Physical Systems and CommNetS-MHI Seminar Series
Abstract: Characterizing the rate-distortion region of Gaussian multiterminal source coding is a longstanding open problem in network information theory. In this talk, I will show how to obtain new conclusive results for this problem using nonlinear analysis and convex relaxation techniques. A byproduct of this line of research is an efficient algorithm for determining the optimal distributed Karhunen-“Loève transform in the high-resolution regime, which partially settles a question posed by Gastpar, Dragotti, and Vetterli. I will also introduce a generalized version of the Gaussian multiterminal source coding problem where the source-encoder connections can be arbitrary. It will be demonstrated that probabilistic graphical models offer an ideal mathematical language for describing how the performance limit of a generalized Gaussian multiterminal source coding system depends on its topology, and more generally they can serve as the long-sought platform for systematically integrating the existing achievability schemes and converse arguments. The architectural implication of our work for low-latency lossy source coding will also be discussed. This talk is based on joint work with Jia Wang, Farrokh Etezadi, and Ashish Khisti.
Biography: Jun Chen received the B.E. degree with honors in communication engineering from Shanghai Jiao Tong University, Shanghai, China, in 2001 and the M.S. and Ph.D. degrees in electrical and computer engineering from Cornell University, Ithaca, NY, in 2004 and 2006, respectively. He was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign, Urbana, IL, from September 2005 to July 2006, and a Postdoctoral Fellow at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, from July 2006 to August 2007. Since September 2007 he has been with the Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON, Canada, where he is currently an Associate Professor and a Joseph Ip Distinguished Engineering Fellow. His research interests include information theory, machine learning, wireless communications, and signal processing. He received the Josef Raviv Memorial Postdoctoral Fellowship in 2006, the Early Researcher Award from the Province of Ontario in 2010, and the IBM Faculty Award in 2010. He served as an Associate Editor for the IEEE Transactions on Information Theory from 2014 to 2016.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar: Towards Generalizable Imitation in Robotics
Thu, Apr 12, 2018 @ 01:30 PM - 02:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Animesh Garg, Postdoctoral Researcher, Stanford University AI lab
Talk Title: Towards Generalizable Imitation in Robotics
Abstract: Robotics and AI are experiencing radical growth, fueled by innovations in data-driven learning paradigms coupled with novel device design, in applications such as healthcare, manufacturing and service robotics. And in our quest for general purpose autonomy, we need abstractions and algorithms for efficient generalization.
Data-driven methods such as reinforcement learning circumvent hand-tuned feature engineering, albeit lack guarantees and often incur a massive computational expense: training these models frequently takes weeks in addition to months of task-specific data-collection on physical systems. Further such ab initio methods often do not scale to complex sequential tasks. In contrast, biological agents can often learn faster not only through self-supervision but also through imitation. My research aims to bridge this gap and enable generalizable imitation for robot autonomy. We need to build systems that can capture semantic task structures that promote sample efficiency and can generalize to new task instances across visual, dynamical or semantic variations. And this involves designing algorithms that unify in reinforcement learning, control theoretic planning, semantic scene & video understanding, and design.
In this talk, I will discuss two aspects of Generalizable Imitation: Task Imitation, and Generalization in both Visual and Kinematic spaces. First, I will describe how we can move away from hand-designed finite state machines by unsupervised structure learning for complex multi-step sequential tasks. Then I will discuss techniques for robust policy learning to handle generalization across unseen dynamics. I will revisit structure learning for task-level understanding for generalization to visual semantics.
And lastly, I will present a program synthesis based method for generalization across task semantics with a single example with unseen task structure, topology or length. The algorithms and techniques introduced are applicable across domains in robotics; in this talk, I will exemplify these ideas through my work on medical and personal robotics.
Biography: Animesh is a Postdoctoral Researcher at Stanford University AI lab. Animesh is interested in problems at the intersection of optimization, machine learning, and design. He studies the interaction of data-driven Learning for autonomy and Design for automation for human skill-augmentation and decision support. Animesh received his Ph.D. from the University of California, Berkeley where he was a part of the Berkeley AI Research center and the Automation Science Lab. His research has been recognized with Best Applications Paper Award at IEEE CASE, Best Video at Hamlyn Symposium on Surgical Robotics, and Best Paper Nomination at IEEE ICRA 2015. And his work has also featured in press outlets such as New York Times, UC Health, UC CITRIS News, and BBC Click.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
2018 Viterbi Keynote Lecture
Thu, Apr 12, 2018 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: David Tse, Thomas Kailath and Guanghan Xu Professor, Stanford University
Talk Title: Maximum likelihood Genome Sequencing
Series: Viterbi Lecture
Abstract: Genome sequencing is one of the biggest breakthroughs in science in the past two decades. Modern sequencing methods use linking data at multiple scales to reconstruct pertinent information about the genome. Many such reconstruction problems can be formulated as maximum likelihood sequence decoding from noisy linking data. We discuss two in this talk: haplotype phasing, the problem of sequencing genomic variations on each of the maternal and paternal chromosomes, and genome scaffolding, the problem of finishing genome assembly using long-range 3D contact data. While maximum likelihood sequence decoding is NP-hard in both of these problems, spectral and linear programming relaxations yield efficient approximation algorithms that can provably achieve the information theoretic limits and perform well on real data. These results parallel the biggest success of information theory: efficiently achieving the fundamental limits of communication.
Biography: David Tse received the B.A.Sc. degree in systems design engineering from University of Waterloo in 1989, and the M.S. and Ph.D. degrees in electrical engineering from Massachusetts Institute of Technology in 1991 and 1994 respectively. From 1995 to 2014, he was on the faculty of the University of California at Berkeley. He received the Claude E. Shannon Award in 2017 and was elected member of the U.S. National Academy of Engineering in 2018. Previously, he received a NSF CAREER award in 1998, the Erlang Prize from the INFORMS Applied Probability Society in 2000 and the Frederick Emmons Terman Award from the American Society for Engineering Education in 2009. He received multiple best paper awards, and is the inventor of the proportional-fair scheduling algorithm used in all third and fourth-generation cellular systems.
Host: Sandeep Gupta, sandeep@usc.edu
More Info: https://minghsiehee.usc.edu/viterbi-lecture/
Webcast: https://bluejeans.com/401381224/More Information: 20180412 Tse Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://bluejeans.com/401381224/
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
Event Link: https://minghsiehee.usc.edu/viterbi-lecture/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Munushian Keynote Speaker - Dr. William Phillips - Nobel Laureate, Physics 1997, Friday, April 13th at 2pm in GER 124 Auditorium
Fri, Apr 13, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. William Phillips, Joint Quantum Institute, National Institute of Standards and Technology and University of Maryland
Talk Title: Quantum Information: a scientific and technological revolution for the 21st century
Abstract: Two of the great scientific and technical revolutions of the 20th century were the discovery of the quantum nature of the submicroscopic world, and the advent of information science and engineering. Both of these have had a profound effect not only on our daily lives but on our worldview. Now, at the beginning
of the 21st century, we see a marriage of quantum mechanics and information science in a new revolution: quantum information. Quantum computation and quantum communication are two aspects of this revolution.
The first is highly speculative: a new paradigm more different from today's digital computers than those computers are from the ancient abacus. The second is already a reality, providing information transmission whose security is guaranteed by the laws of physics. The JQI/NIST Laser Cooling and Trapping Group is studying the use of single, ultracold atoms as quantum bits, or qubits, for quantum information processing.
Biography: William D. Phillips was born in 1948, in Wilkes-Barre PA, and attended public primary and secondary schools in Pennsylvania. He received a B.S. in
Physics from Juniata College in 1970 and a Ph.D. from MIT in 1976. After two years as a Chaim Weizmann postdoctoral fellow at MIT, he joined the staff of the
National Institute of Standards and Technology (then the National Bureau of Standards) in 1978. He is currently leader of the Laser Cooling and Trapping Group in the Quantum Measurement Division of NIST's Physical Measurement Laboratory, and a Distinguished University Professor at the University of Maryland. He is a Fellow of the Joint Quantum Institute, a cooperative research organization of NIST and the University of Maryland that is devoted to the study of quantum coherent phenomena. At the JQI he is the co-director of an NSF-funded Physics Frontier Center focusing on quantum phenomena that span different subfields of physics.
The research group led by Dr. Phillips at NIST has been responsible for developing some of the main techniques now used for laser-cooling and cold-atom experiments in laboratories around the world, including the deceleration of atomic beams, magnetic trapping of atoms, the storage and manipulation of cold atoms with optical lattices, and the coherent manipulation of Bose-Einstein condensates. In 1988 the NIST group discovered that laser cooling could reach temperatures much lower than had been predicted by theory, a result that led to a new understanding of laser cooling and contributed to many of the subsequent developments in cold atomic gases. Early achievements included reaching laser-cooling temperatures within a millionth of a degree of Absolute Zero. Today, the group pursues research in laser cooling and trapping; Bose-Einstein condensation; atom optics; collisions of cold atoms; quantum information processing; cold atoms in optical lattices; production and transmission of non-classical light; and the study of cold-atom analogs to condensed matter systems. Phillips and colleagues demonstrated the first "atomic fountain" clock as proposed by Zacharias. Such clocks, as realized in other laboratories, have become the primary time standards for world timekeeping.
Dr. Phillips is a fellow of the American Physical Society and the American Academy of Arts and Sciences. He is a Fellow and Honorary Member of the Optical Society of America, a member of the National Academy of Sciences and the Pontifical Academy of Sciences, and a corresponding member of the Mexican Academy of Sciences. He is the recipient of the Gold Medal of the U. S. Department of Commerce (1993), the Michelson Medal of the Franklin Institute (1996), the Schawlow Prize of the American Physical Society (1998), and the Service to America Medal, Career Achievement Award 2006. In 1997, Dr. Phillips shared the Nobel Prize in Physics "for development of methods to cool and trap atoms with laser light."
Host: EE-Electrophysics
More Info: minghsiehee.usc.edu/about/lectures
Location: Ethel Percy Andrus Gerontology Center (GER) - 124
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: minghsiehee.usc.edu/about/lectures
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Apr 16, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: John Baras, The Institute for Systems Research, University of Maryland
Talk Title: Networked Cyber-Physical Systems (Net-CPS)
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: We describe recent results on foundational aspects of modeling, architecture and performance of networked cyber-physical systems. These include: multi-layer multigraph models, constrained coalitional games, analysis of trust and mistrust in collaboration, dynamics of signed graphs, distributed consensus with adversaries, new concepts of value of information and event-driven inference and decision making, non-commutative probability models. We conclude with directions for future research.
Biography: John Baras is with the University of Maryland College Park, where he holds he endowed Lockheed Martin Chair in Systems Engineering. He received the Diploma in Electrical and Mechanical Engineering from the National Technical University of Athens, Greece, 1970; the M.S. and Ph.D. degrees in Applied Mathematics from Harvard University 1971, 1973. Since 1973, he has been a faculty member in the Electrical and Computer Engineering Department, and in the Applied Mathematics, Statistics and Scientific Computation Program, at the University of Maryland College Park. Founding Director of the Institute for Systems Research (ISR), 1985 to 1991. Since 1992, Founding Director of the Maryland Center for Hybrid Networks (HYNET). Since 2013, Guest Professor at the Royal Institute of Technology (KTH), Sweden. IEEE Life Fellow, SIAM Fellow, AAAS Fellow, NAI Fellow, IFAC Fellow, AIAA Associate Fellow, and a Foreign Member of the Royal Swedish Academy of Engineering Sciences (IVA). Received the 1980 George Axelby Award from the IEEE Control Systems Society, the 2006 Leonard Abraham Prize from the IEEE Communications Society, the 2014 Tage Erlander Guest Professorship from the Swedish Research Council, and a three year (2014-2017) Senior Hans Fischer Fellowship from the Institute for Advanced Study of the Technical University of Munich, Germany. He was inducted in the A. J. Clark School of Engineering Innovation Hall of Fame (2016) of the University of Maryland and was awarded the 2017 IEEE Simon Ramo Medal, and the 2017 AACC Richard E. Bellman Control Heritage Award. His research interests include systems and control, optimization, communication networks, signal processing and understanding, robotics, computing systems, network security and trust, systems biology, healthcare management systems, model-based systems engineering.
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar - Controlling Dynamic Ensembles: from Cells to Societies
Tue, Apr 17, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jr-Shin Li, Das Family Distinguished Career Development Associate Professor of Systems Science and Mathematics, Washington University in St. Louis
Talk Title: Controlling Dynamic Ensembles: from Cells to Societies
Abstract: Natural and engineered systems that consist of populations of isolated or interacting dynamical components exhibit levels of complexity that are beyond human comprehension. These complex systems often require an appropriate excitation, an optimal hierarchical organization, or a periodic dynamical structure, such as synchrony, to function as desired or operate optimally. In many applications, the dynamics of such ensemble systems can only be regulated by the use of a single or sparsely distributed external inputs in order to alter their state configurations or dynamic patterns; for example, excitation of a large quantum ensemble using a sequence of electromagnetic fields in nuclear magnetic resonance spectroscopy and imaging, entrainment of a population of circadian cells by a light protocol in chronobiology, and desynchronization of a pathologically synchronized neuron ensemble with neurostimulation for the treatment of neurological disorders, such as Parkinson's disease or epilepsy, in brain medicine. This unconventional control paradigm gives rise to challenging problems regarding robust broadcast control and computation for underactuated dynamic populations. Moreover, valid and precise mathematical models for describing the dynamics of such complex systems are often elusive, while their measurement data are available. This talk will address theoretical and computational challenges for targeted coordination of both isolated and networked ensemble systems arising in diverse areas at different scales. Both data-driven and model-based approaches for learning, decoding, control, and computation of dynamic structures and patterns in ensemble systems will be presented. Practical control designs, including synchronization waveforms for pattern formation in nonlinear oscillatory networks and optimal pulses in quantum control will be illustrated along with their experimental realizations. Lastly, future directions and opportunities in Systems and Controls will be discussed.
Biography: Dr. Jr-Shin Li is currently Das Family Distinguished Career Development Associate Professor of Systems Science and Mathematics in the Department of Electrical and Systems Engineering at Washington University in St. Louis, where he also holds a joint appointment in the Division of Biology & Biomedical Sciences since he joined Washington University in 2006. Dr. Li received a B.S. and an M.S. from National Taiwan University, and a Ph.D. in Applied Mathematics from Harvard University in 2006. His research interests lie in the areas of systems, computational, and data sciences, and their applications to biology, neuroscience, quantum physics, brain medicine, and public health. He is a recipient of the NSF Career Award in 2008 and the AFOSR Young Investigator Award in 2010. He is currently Associate Editor of the SIAM Journal on Control and Optimization (SICON) and the IEEE Transactions on Control Systems Technology (TCST).
Host: Edmond Jonckheere, jonckhee@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar - From DC to Daylight: Harnessing Electromagnetic Fields for Bioelectronics, Wireless Communications, and Silicon Photonics
Wed, Apr 18, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Constantine Sideris, Postdoctoral Scholar, Caltech
Talk Title: From DC to Daylight: Harnessing Electromagnetic Fields for Bioelectronics, Wireless Communications, and Silicon Photonics
Abstract: Maxwell's equations are responsible for explaining the fundamental operating principles behind most of today's technology. In this talk, we will explore how understanding and controlling electromagnetic fields can lead to significant impact across a multitude of applications over a wide frequency range on the electromagnetic spectrum. Starting from the low-frequency end of the spectrum, I will present the design and implementation of a new integrated magnetic biosensor. The magnetic biosensor is fabricated in a standard CMOS foundry process without any post-fabrication processing and can perform in-vitro detection of DNA, proteins, and cells by utilizing magnetic nanoparticles as labels. We will discuss three different, improved sensor designs, which address sensor gain uniformity, enable multiplex target detection, and compensate sensor electrical and thermal drift based on spatial and temporal manipulations of the magnetic fields. I will present initial in-vitro biodetection experiments, and discuss future research directions moving towards in-vivo sensing with wearable and implantable devices, as well as actuation via targeted therapeutics. Next, we will look into the RF domain and develop maximal performance bounds for antennas. I will present a rapid simulation technique which, when coupled with heuristic optimization algorithms, can quickly and effectively produce new antenna structures de-novo with little or no manual intervention. The efficacy of these techniques will be shown in the context of a 3D printed coupling antenna for a dielectric waveguide communication link. Moving higher in frequency, we will explore the near-infrared (NIR) part of the spectrum in the context of silicon photonic device optimization. I will present on-going work in designing grating coupler and power splitting devices with arbitrary splitting ratios by using adjoint optimization and highly efficient integral equation techniques. We will also explore exciting future directions in these research areas, leveraging modern computation and efficient numerical algorithms as well as holistic co-design of circuits and electromagnetics.
Biography: Constantine Sideris received the B.S., M.S., and PhD degrees with honors from the California Institute of Technology in 2010, 2011, and 2016 respectively. He was a visiting scholar at UC Berkeley's Wireless Research Center from 2013 to 2014. He was a lecturer in the Electrical Engineering department for Caltech's popular machine learning project course in 2017. He is currently a postdoctoral scholar in the Electrical Engineering and Computational and Mathematical Sciences departments at Caltech. His research interests include RF and millimeter-wave integrated circuits and computational electromagnetics for biomedical applications, wireless communications, and silicon photonics. He was a recipient of an NSF graduate research fellowship in 2010, the Analog Devices Outstanding Student Designer Award in 2012, and the Caltech Leadership Award in 2017.
Host: Murali Annavaram, annarvarm@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar - Trustworthy Autonomy: Algorithms for Human-Robot Systems
Thu, Apr 19, 2018 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Katherine Driggs-Campbell, Postdoctoral Research Scholar, Stanford Intelligent Systems Laboratory
Talk Title: Trustworthy Autonomy: Algorithms for Human-Robot Systems
Abstract: Autonomous systems, such as self-driving cars, are becoming tangible technologies that will soon impact the human experience. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges humans present: People tend to defy expected behaviors and do not conform to many of the standard assumptions made in robotics. To design safe, trustworthy autonomy, we must transform how intelligent systems interact, influence, and predict human agents. In this work, we'll use tools from robotics, artificial intelligence, and control to explore and uncover structure in complex human-robot systems to create more intelligent, interactive autonomy.
In this talk, I'll present on robust prediction methods that allow us to predict driving behavior over long time horizons with very high accuracy. These methods have been applied to intervention schemes for semi-autonomous vehicles and to autonomous planning that considers nuanced interactions during cooperative maneuvers. I'll also present a new framework for multi-agent perception that uses people as sensors to improve mapping. By observing the actions of human agents, we demonstrate how we can make inferences about occluded regions and, in turn, improve control. Finally, I'll present on recent efforts on validating stochastic systems, merging deep learning and control, and implementing these algorithms on a fully equipped test vehicle that can operate safely on the road.
Biography: Katie is currently a Postdoctoral Research Scholar at the Stanford Intelligent Systems Laboratory in the Aeronautics and Astronautics Department. She received a B.S.E. with honors from Arizona State University in 2012 and an M.S. from UC Berkeley in 2015. In May of 2017, she earned her PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley, advised by Professor Ruzena Bajcsy. Her thesis was entitled "Tools for Trustworthy Autonomy: Robust Prediction, Intuitive Control, and Optimized Interaction," which contributed to the field of autonomy, by merging ideas robotics, transportation, and control to address problems associated with human-in-the-loop. Her work considers the integration of autonomy into human dominated fields, in terms of safe interaction, with a strong emphasis on novel modeling methods, experimental design, robust learning, and control frameworks. She received the Demetri Angelakos Memorial Achievement Award for her contributions to the community, has instigated many events and groups for women in STEM, including founding a group for Women in Intelligent Transportation Systems, and was selected for the Rising Stars in EECS program in 2017.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
The Explosion in Neural Network Chips
Fri, Apr 20, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Trevor Mudge, University of Michigan, Ann Arbor
Talk Title: The Explosion in Neural Network Chips
Abstract: Until recently the conventional wisdom was that proposing a new chip startup in the US was a bad bet. Recently that perception has changed. There are dozens of startups that have found funding for new chip architectures that perform neural network computations much faster while consuming less power than general purpose CPUs. In fact, over 1.5 billion dollars in venture funding has already been dispersed for such startups. There are several factors behind this change of heart. First has been a slowing of Moore's Law that has made application specific computers more attractive. Second is the existence of application specific computers that could easily be repurposed, as exemplified by Digital Signal Processors and Graphics Processors. Finally, the presence of independent foundries such as the Taiwan Semiconductor Manufacturing Company and the United Microelectronics Corporation removed the need for every chip startup to build its own multi-billion dollar fabrication facility. In this talk I will discuss the reasons for this explosion starting with an overview of the problems these machines are targeting. I will then examine the aforementioned factors in more detail. Lastly, I will outline the co-design process that has led to many of the existing solutions. My concluding remarks will discuss the barriers to the success of these new architectures.
Biography: Trevor Mudge received the Ph.D. in Computer Science from the University of Illinois, Urbana. He is now the Bredt Family Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor. He is author of numerous papers on computer architecture, programming languages, VLSI design, and computer vision. He has also chaired 54 theses in these areas. In 2014 he received the ACM/IEEE CS Eckert-Mauchly Award and the University of Illinois Distinguished Alumni Award. He is a Life Fellow of the IEEE, a Fellow of the ACM, and a member of the IET and the British Computer Society.
Host: Xuehai Qian, x04459, xuehai.qian@usc.edu
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Apr 23, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Steven Brunton, University of Washington
Talk Title: Data-Driven Discovery and Control of Nonlinear Systems
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power. There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts.
This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control of complex systems. Connections to modern Koopman operator theory are also discussed.
Biography: Steven L. Brunton is an Assistant Professor of Mechanical Engineering and a Data Science Fellow at the eScience Institute at the University of Washington in Seattle. He received a B.S. in Mathematics with a minor in Control and Dynamical Systems from Caltech in 2006, and received a Ph.D. in Mechanical and Aerospace Engineering from Princeton in 2012. His research interests include data-driven modeling and control, dynamical systems, sparse sensing and machine learning applied to complex systems in fluid dynamics, optics, neuroscience, bio-locomotion, and renewable energy.
Host: Eva Kanso, kanso@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Deterministic Random Matrices
Wed, Apr 25, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ilya Soloveychik, School of Engineering and Applied Sciences, Harvard University
Talk Title: Deterministic Random Matrices
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Random matrices have become a very active area of research in the recent years and have found enormous applications in modern mathematics, physics, engineering, biological modeling, and other fields. In this work, we focus on symmetric sign (+/-1) matrices (SSMs) that were originally utilized by Wigner to model the nuclei of heavy atoms in mid-50s. Assuming the entries of the upper triangular part to be independent +/-1 with equal probabilities, Wigner showed in his pioneering works that when the sizes of matrices grow, their empirical spectra converge to a non-random measure having a semicircular shape. Later, this fundamental result was improved and substantially extended to more general families of matrices and finer spectral properties. In many physical phenomena, however, the entries of matrices exhibit significant correlations. At the same time, almost all available analytical tools heavily rely on the independence condition making the study of matrices with structure (dependencies) very challenging. The few existing works in this direction consider very specific setups and are limited by particular techniques, lacking a unified framework and tight information-theoretic bounds that would quantify the exact amount of structure that matrices may possess without affecting the limiting semicircular form of their spectra.
From a different perspective, in many applications one needs to simulate random objects. Generation of large random matrices requires very powerful sources of randomness due to the independence condition, the experiments are impossible to reproduce, and atypical or non-random looking outcomes may appear with positive probability. Reliable deterministic construction of SSMs with random-looking spectra and low algorithmic and computational complexity is of particular interest due to the natural correspondence of SSMs and undirected graphs, since the latter are extensively used in combinatorial and CS applications e.g. for the purposes of derandomization. Unfortunately, most of the existing constructions of pseudo-random graphs focus on the extreme eigenvalues and do not provide guaranties on the whole spectrum. In this work, using binary Golomb sequences, we propose a simple completely deterministic construction of circulant SSMs with spectra converging to the semicircular law with the same rate as in the original Wigner ensemble. We show that this construction has close to lowest possible algorithmic complexity and is very explicit. Essentially, the algorithm requires at most 2log(n) bits implying that the real amount of randomness conveyed by the semicircular property is quite small.
Biography: Ilya Soloveychik received his BSc degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, Moscow, Russia in 2007, the MSc degree in Mathematics and the PhD degree in Electrical Engineering from the Hebrew University of Jerusalem, Israel in 2013 and 2016, respectively. He is currently a Fulbright postdoctoral fellow with the Harvard School of Engineering and Applied Sciences. His research interests include random matrix theory, high-dimensional statistics and signal processing, and graphical models. He received the Potanin Scholarship for excellence in studies in 2005, the Klein Prize and the Kaete Klausner Scholarship in 2011. In 2015 he was awarded the Feder Family Prize for outstanding research in the field of Communications Technology and in 2016 - the Wolf Foundation Prize for excellence in studies.
Host: Professor Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE Seminar: Cryptographic Primitives for Hardware Security
Thu, Apr 26, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ling Ren, MIT CSAIL
Talk Title: Cryptographic Primitives for Hardware Security
Abstract: Hardware plays a critical role in today's security landscape. Every protocol with security or privacy guarantees inevitably includes some hardware in its trusted computing base. The increasing number of vulnerability disclosures calls for a more rigorous approach to secure hardware designs. In this talk, I will present several cryptographic primitives to enhance the security of hardware.
I will first discuss the use of Physically Obfuscated Keys (POK) to strengthen the security of private keys. In particular, I will present a computational fuzzy extractor based on the Learning Parity with Noise (LPN) problem. Our construction uses stability information as a trapdoor to correct a constant fraction of POK errors efficiently. Next, I will describe our work on Oblivious RAM (ORAM), a cryptographic primitive to prevent access pattern leakage. I will present both architectural and algorithmic improvements to ORAM.
While hardware is often trusted as a line of defense, it can also be utilized by attackers. The advent of ASIC hash units calls into question the security of hash functions and proof-of-work protocols. I will describe bandwidth-hard functions to achieve ASIC resistance and briefly touch on my other projects in blockchains and consensus.
Biography: Ling Ren is a final year graduate student at Massachusetts Institute of Technology. He received his Master's degree from Massachusetts Institute of Technology and Bachelor's degree from Tsinghua University. His research interests span computer security, cryptography, computer architecture and distributed computing. He received the best student paper award at CCS 2013.
Host: Bhaskar Krishnamachari, bkrishna@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
EE-EP Seminar - Maysam Ghovanloo, Friday, April 27th at 2pm in EEB 132
Fri, Apr 27, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Maysam Ghovanloo, Georgia Institute of Technology
Talk Title: Cutting Edge Examples of Medical Device-on-a-Chip
Abstract: For medical devices that need to be implanted or positioned inside the human body to deliver a therapy, size and functionality are among the most important parameters, affecting key aspects of the device, such as feasibility, level of invasiveness, side effects, and safety, ability to reach the desired anatomical target, and efficacy in carrying out intended functions, such as imaging, recording biological parameters, delivering drugs, or applying stimuli, or a combination of these as part of a medical intervention. on the On the other hand, microelectronic devices, integrated circuit design, and system-level architectures have advanced to the point that combining multiple functions in a variety of domains from low noise analog readout, to on-chip digital processing, RF connectivity, power management, and precise control of physical outputs on a monolithic piece of silicon has become quite routine, in an approach referred to as the system-on-a-chip (SoC). In this talk, I will present a few examples of applying the well-established SoC technology towards design and development of cutting edge medical devices that are fit to be implanted or delivered inside the body, while being supported by system blocks outside of the body, to either create de novo medical interventions or significantly improve the existing therapies. I refer to these as the medical device-on-a-chip (MDoC) approach, and also propose the pathway towards design concept, preliminary steps, and evaluation plans for new MDoC technologies that would enable new therapies and interventions that are not feasible today.
Biography: Maysam Ghovanloo received the B.S. degree in electrical engineering from the University of Tehran in 1994, and the M.S. degree in biomedical engineering from the Amirkabir University of Technology, Tehran, Iran in 1997. He also received the M.S. and Ph.D. degrees in electrical engineering from the University of Michigan, Ann Arbor, in 2003 and 2004, respectively.
Dr. Ghovanloo developed the first modular Patient Care Monitoring System in Iran and started a company to manufacture research instruments for electrophysiology and pharmacology labs. From 2004 to 2007 he was an assistant professor in the Department of ECE at the North Carolina State University, Raleigh, NC. Since 2007 he has been with the Georgia Tech's School of Electrical and Computer Engineering, where he is a professor and the founding director of the GT-Bionics Lab. In 2012 he started Bionic Sciences Inc., a technology transfer company, where he serves as the CTO. He has authored or coauthored more than 200 peer-reviewed conference and journal publications on implantable microelectronic devices, integrated circuits and microsystems for medical applications, and modern assistive/rehabilitation technologies. He also holds 8 issued patents.
Prof. Ghovanloo was a recipient of the National Science Foundation CAREER Award, the Tommy Nobis Barrier Breaker Award for Innovation, and Distinguished Young Scholar Award from the Association of Professors and Scholars of Iranian Heritage. He is an Associate Editor of the IEEE Transactions on Biomedical Engineering and IEEE Transactions on Biomedical Circuits and Systems. He serves on the Senior Editorial Board of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS). He served as an Associate Editor of IEEE Transactions on Circuits and Systems, Part II, as well as a Guest Editor for the IEEE Journal of Solid-State Circuits and IEEE Transactions on Neural Systems and Rehabilitation Engineering. He chaired the IEEE Biomedical Circuits and Systems (BioCAS 2015) in Atlanta, GA, and currently co-chairs the technical program committee for BioCAS 2018 in Cleveland, OH. He is also serving on the Analog subcommittee of the Custom Integrated Circuits Conf. (CICC).
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Rapid, Efficient, and Robust Neuroimage Analysis with Deep Neural Networks
Mon, Apr 30, 2018 @ 11:30 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mert R. Sabuncu, Electrical and Computer Engineering, Cornell University
Talk Title: Rapid, Efficient, and Robust Neuroimage Analysis with Deep Neural Networks
Series: Medical Imaging Seminar Series
Abstract: Abstract: Neuroimaging is entering a new era of unprecedented scale and complexity. Soon, we will have datasets including brain images from more than 100,000 individuals. The fundamental challenges in analyzing and exploiting these data are going to be computational. Today, widely-used traditional neuroimage analysis tools, such as FreeSurfer or FSL, are computationally demanding and offer limited flexibility, while cutting-edge tools based on modern machine learning techniques require large amounts of annotated training data, and/or are untested at scale. In this talk, I will present our recent work on two fundamental image analysis problems: registration and segmentation. In image registration, I will introduce a novel framework that allows us to train a neural network that rapidly computes a smooth and invertible nonlinear (diffeomorphic) deformation that aligns two input images, in an unsupervised fashion (i.e. without using ground-truth registrations). I will show experiments on 7000+ brain MRI scans with state-of-the-art results. In the second part, I will present a new segmentation framework that flexibly handles multiple labeling protocols, and generalizes well to new datasets and new segmentation labels, with little additional training.
Biography: Mert R. Sabuncu is a faculty member of Cornel's School of Electrical Engineering and Computer Engineering.At Cornell, Mert directs a lab that focuses on biomedical image analysis for scientific (e.g. brain mapping) and clinical (e.g., computer-aided diagnosis) applications.Mert's research employs and contributes to the toolkits of machine learning, image processing, computer vision, and other modern computational methods.Mert has a Ph.D. from Princeton Electrical Engineering and was post-doc at MIT, where he worked with Polina Golland. Before joining Cornell, he was a faculty member at the A.A. Martinos Center for Biomedical Imaging (Harvard Medical School and Massachusetts General Hospital).
Host: Professor Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
