Events for the 5th week of March
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EE-EP Faculty Candidate, Wei Bao, Monday, March 26th @12pm in EEB 132
Mon, Mar 26, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Wei Bao, University of California, Berkeley
Talk Title: Interacting Light with Semiconductor at the Nanoscale
Abstract: The ability to probe and control light-matter interaction at the nanometer scale not only advances frontiers of fundamental science, but also is a critical prerequisite to device applications in electronics, sensing, catalysis, energy harvesting, and more. Exploiting and enhancing the originally weak light-matter interactions via nanofabricated photonic structures; we will be able to sense chemical species at single molecule levels, to devise better imaging and manufacturing tools, to transfer data more efficiently at higher speed.
In this talk, I will first describe a simple and general nano-optical device developed during my Ph.D., called campanile probe, which lay groundwork for generally-applicable nano-optical studies. Two examples will be discussed, where we cross the boundary from insufficient to sufficient resolution beyond optical diffraction limit and perform optical hyperspectral imaging of luminescence heterogeneity along InP nanowires and synthetic monolayer MoS2, providing spectral information distinct from diffraction limited micro-PL spectral imaging. Following this, I will discuss the recent works using cavities to further enhance the strength of light-matter interaction into the strong coupling regime. The formation of coherently coupled cavity exciton-polariton in two-dimensional monolayer WS2 and the inorganic perovskite CsPbBr3 as well as the ultralow threshold optically pumped polariton lasing in perovskite cavities will be shown. Finally, I will conclude by presenting my vision of how these devices can enable a wide range of capabilities with relevance to multidimensional spectroscopy imaging, efficient solid-state lighting and even beyond.
Biography: Dr. Wei Bao is a postdoctoral researcher in Prof. Xiang Zhang's lab at the University of California, Berkeley. Previously he earned his B.A. in Physics (minor in Chemistry) at Peking University in 2009, and his M.S. in Mechanical Engineering (minor in Electrical Engineering) at UCLA in 2010. Wei then received his Ph.D. in Materials Science and Engineering (minor in Electrical Engineering) at University of California, Berkeley under the supervision of Prof. Miquel Salmeron and Prof. P. James Schuck in 2015. His Ph.D. work in nanoscale spectroscopic investigations of optoelectronic has led to several awards including: MRS Graduate Student Gold Award, Dorothy M. and Earl S. Hoffman Scholarships, Ross N. Tucker Memorial Award, as well as a R&D 100 Award 2013. His postdoc research currently focuses on polaritonics lasing devices, a scientific direction at the interface between low-dimensional semiconductor nanophotonics and quantum physics.
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, Mar 26, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: B. Ross Barmish, University of Wisconsin, Madison
Talk Title: From the Kelly-Shannon Collaboration to Stock Trading Based on Feedback Control
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: This talk begins with a description of some ideas related to gambling which originated at Bell Labs in the 1950s by John Kelly and Claude Shannon. With their work serving as motivation for this talk, I will provide an overview of my research on the development of new stock-trading algorithms. The most salient feature of my approach is that no model of any sort is used for the underlying stock-price dynamics. Instead, in the spirit of technical analysis, the size of the time-varying stock position is determined using some simple ideas involving the adaptive power of feedback control loops. This approach is said to be "reactive" rather than predictive and amounts to assigning high priority to sound money management. After the key ideas driving this research are explained, the back-testing of the trading algorithms using historical data will be addressed with attention paid to practical considerations such as transaction costs, leverage and margin. It is interesting to note that sometimes the simulations lead to unexpected results which were not contemplated during the course of the research.
Biography: B. Ross Barmish is Professor of Electrical and Computer Engineering at the University of Wisconsin, Madison. Prior to joining UW in 1984, he held faculty positions at Yale University and the University of Rochester. From 2001-2003, he served as Chair of the EECS Department at Case Western Reserve while holding the Nord endowed professorship. He received his Bachelor's degree in EE from McGill University and the M.S. and Ph.D. degrees, also in EE, from Cornell University.
Throughout his career, he has served the IEEE Control Systems Society in many capacities and has been a consultant for a number of companies. Professor Barmish is the author of the textbook ``New Tools for Robustness of Linear Systems'' and is a Fellow of both the IEEE and IFAC for his contributions to robust control. He received two Best Journal Publication awards, each covering a three-year period, from the International Federation of Automatic Control and has given a number of keynotes and plenary lectures at major conferences. In~2013, he received the IEEE Control Systems Society Bode Prize.
While his earlier work concentrated on robustness of dynamical systems, his current university research involves building a bridge between feedback control theory and trading in complex financial markets. In addition to this academic pursuit, in his capacity as CEO of Robust Trading Solutions, his work involves transition of stock-trading algorithms from theory to practice and government sponsored research on the NASDAQ Limit Order Book.
Host: Petros Ioannou, ioannou@usc.edu
More Information: barmish.jpg (JPEG Image, 411 × 568 pixels).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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EE Seminar: Enabling Optical Methods for Next-Generation Neural Prostheses
Mon, Mar 26, 2018 @ 03:00 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Andrea Giovannucci, Research Scientist, Flatiron Institute, Simons Foundation
Talk Title: Enabling Optical Methods for Next-Generation Neural Prostheses
Abstract: Optical methods present interesting new opportunities for brain computer interfaces (BCIs) and closed-loop experiments because of their capability to densely monitor and stimulate in-vivo large neural populations across weeks with single cell resolution. For instance, combining optical methods for recording (two-photon imaging of calcium indicators) and perturbing (optogenetics) neural ensembles opens the door to exciting closed-loop experiments, where the stimulation pattern can be determined based on the recorded activity and/or the behavioral state. However, the adoption of such tools for BCIs is currently hindered by the lack of algorithms that track neural activity in real-time. In a typical closed-loop experiment, the monitored/perturbed regions of interest (ROIs) have been preselected by analyzing offline a previous dataset from the same field of view. Monitoring the activity of a ROI, which usually corresponds to a soma, typically entails averaging the fluorescence over the corresponding ROI, resulting in a signal that is only a proxy for the actual neural activity and which can be sensitive to motion artifacts and drifts, as well as spatially overlapping sources, background/neuropil contamination, and noise. Furthermore, by preselecting the ROIs, the experimenter is unable to detect and incorporate new sources that become active later during the experiment or track changes in neuronal morphology, which prevents the execution of truly closed-loop experiments.
In the first portion of this talk I will present an Online, single-pass, algorithmic framework for the Analysis of Calcium Imaging Data (OnACID). The framework is highly scalable with minimal memory requirements, as it processes the data in a streaming fashion one frame at a time, while keeping in memory a set of low dimensional sufficient statistics and a small minibatch of the last data frames. Every frame is processed in four sequential steps: i) The frame is registered against the previous denoised (and registered) frame to correct for motion artifacts. ii) The fluorescence activity of the already detected sources is tracked. iii) Newly appearing neurons are detected and incorporated to the set of existing sources. iv) The fluorescence trace of each source is denoised and deconvolved to provide an estimate of the underlying spiking activity. I will present the results of applying OnACID to several large-scale (90-350GB) mouse and zebrafish larvae in-vivo datasets. OnAcid can find and track tens of thousands of neurons faster than real-time, and outperforms state of the art algorithms benchmarked on multiple manual annotations using a precision-recall framework.
In the second portion of the talk, I will present an application of brain optical imaging to unveil coding properties and feedback mechanisms implemented by neurons in the cerebellum, a brain area implied in motor control and in the production of agile movement sequences. By monitoring across days the same neuronal populations of mice undergoing associative learning I will show that a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.
In the last section of the talk, I will discuss my plans to develop all-optical neural prostheses interfacing with the cerebellum to recover lost motor function in the central nervous system because of injury or disease.
Biography: Andrea Giovannucci has a Ph.D. in computer science from Universitat Autònoma de Barcelona in Spain and a B.S. in electrical engineering from Politecnico di Milano in Italy. From 2008 to 2010 he was a postdoctoral fellow at Pompeu Fabra University (Barcelona), where he developed signal processing algorithms and circuit models for neuroprosthetic applications. From 2010 to 2015 he completed a postdoctoral fellowship at the Princeton Neuroscience Institute (PNI), Princeton University. At PNI, he pioneered the use of genetically encoded calcium indicators to image neurons in the cerebellum of awake learning mice, and applied them to investigate coding properties of cerebellar neurons during motor learning. Since 2015 Andrea Giovannucci is a research scientist at the Flatiron Institute, Simons Foundation, where he develops algorithms for the analysis of calcium imaging data, general-purpose neural networks and data-intensive computing projects. Dr. Giovannucci was the recipient of the First Prize for the Best Agent Service or Application in the Agent Technology Competition (IST Agentcities.net) in 2003, was shortlisted for the best Ph.D. thesis in artificial intelligence (ECCAI), and was the recipient of the prestigious Juan de La Cierva (Spain) and New Jersey Commission on Brain Injury Research (USA) fellowships. Andrea Giovannucci is the leader developer of the CaImAn open source software platform for calcium imaging analysis, currently used by hundreds of research laboratories worldwide.
Host: Maryam Shanechi, shanechi@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE Seminar: Statistical and Formal Methods in Hardware Security
Tue, Mar 27, 2018 @ 10:30 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yiorgos Makris, Professor, ECE Department, The University of Texas at Dallas
Talk Title: Statistical and Formal Methods in Hardware Security
Abstract: Partly because of design outsourcing and migration of fabrication to low-cost areas around the globe, and partly because of increased reliance on third-party intellectual property, the integrated circuit (IC) supply chain is now considered far more vulnerable than ever before. With electronics ubiquitously deployed in sensitive domains and critical infrastructure, such as wireless communications, industrial environments, as well as health, financial and military applications, understanding the corresponding risks and developing appropriate remedies have become paramount. To this end, in this presentation I will discuss the role that statistical and formal methods can play in ensuring security and trustworthiness of ICs and the systems wherein they are deployed, and I will introduce two solutions that my research group has contributed to the area of hardware security.
The first contribution, known as Statistical Side-Channel Fingerprinting, is a statistical method for assessing whether an integrated circuit originates from a known distribution or not, based on parametric measurements such as delay, power, electromagnetic emanations, temperature, etc. Effectiveness of this method in detecting ICs which have been subjected to malicious modifications (a.k.a. hardware Trojans) will be demonstrated using silicon measurements from a custom-designed wireless cryptographic IC. Solutions to the main challenges of statistical side-channel fingerprinting, namely the availability of a statistically significant trusted population and the detection of hardware Trojans which are activated after deployment, will also be discussed and demonstrated in silicon.
The second contribution, known as Proof-Carrying Hardware Intellectual Property, is a formal method for proving compliance of an electronic design acquired from a third-party vendor with a set of security properties. These properties, which are expressed as theorems with corresponding proofs in a formal proof management system (i.e., Coq) and which can be automatically checked by the consumer, outline the boundaries of trusted operation without necessarily specifying the exact functionality of the design. Effectiveness of this method in certifying secure instruction execution will be demonstrated on a popular microcontroller and its utility for data secrecy protection through fully-automated information flow tracking will be demonstrated on a cryptographic core.
I will conclude by revisiting the modus operandi of the hardware security research area as it enters its second decade of activity and I will emphasize the need for (i) intensified efforts towards statistical and formal methods which can offer risk bounds and provable security, and (ii) synergy platforms whereby hardware security can be seamlessly integrated with software security, network security and cryptography, towards developing holistic system-level solutions for both contemporary and emerging applications. In this context, I will also briefly review our recent efforts in mixed-signal and system-level proof-carrying hardware, covert wireless communications, machine learning-based malware detection and workload forensics, as well as in establishing an NSF Industry/University Cooperative Research Center on Hardware and Embedded System Security and Trust (CHEST).
Biography: Yiorgos is a professor of Electrical and Computer Engineering at The University of Texas at Dallas, where he leads the Trusted and RELiable Architectures (TRELA) Research Laboratory. Prior to joining UT Dallas in 2011, he spent a decade as a faculty of Electrical Engineering and of Computer Science at Yale University. He holds a Ph.D. (2001) and an M.S. (1997) in Computer Engineering from the University of California, San Diego, and a Diploma of Computer Engineering and Informatics (1995) from the University of Patras, Greece. His main research interests are in the application of formal and machine learning-based methods in the design of trusted and reliable integrated circuits and systems, with particular emphasis in the analog/RF domain. He is also investigating hardware-based malware detection, forensics and reliability methods in modern microprocessors, as well as on-die learning and novel computational modalities using emerging technologies. His research activities have been supported by NSF, SRC, ARO, AFRL, DARPA, Boeing, IBM, LSI, Intel, Advantest, AMS and TI. Yiorgos is as an associate editor of the IEEE Transactions on Information Forensics and Security, the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the IEEE Design & Test periodical and the Springer Journal of Electronic Testing: Theory and Applications. He served as the 2016-2017 general chair and the 2013-2014 program chair of the IEEE VLSI Test Symposium, and as a topic coordinator and/or program committee member for several IEEE and ACM conferences. He is a Senior Member of the IEEE, a recipient of the 2006 Sheffield Distinguished Teaching Award and a recipient of the Best Paper Award from the 2013 Design Automation and Test in Europe (DATE'13) conference and the 2015 VLSI Test Symposium (VTS'15).
Host: Peter Beerel, beerel@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE Seminar: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications
Wed, Mar 28, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jiantao Jiao, Stanford University
Talk Title: Statistical Interference of Properties of Distribution: Theory, Algorithms, and Applications
Abstract: Modern data science applications - ranging from graphical model learning to image registration to inference of gene regulatory networks - frequently involve pipelines of exploratory analysis requiring accurate inference of a property of the distribution governing the data rather than the distribution itself. Notable examples of properties include Shannon entropy, mutual information, Kullback-Leibler divergence, and total variation distance, among others.
This talk will focus on recent progress in the performance, structure, and deployment of near-minimax-optimal estimators for a large variety of properties in high-dimensional and nonparametric settings. We present general methods for constructing information theoretically near-optimal estimators, and identify the corresponding limits in terms of the parameter dimension, the mixing rate (for processes with memory), and smoothness of the underlying density (in the nonparametric setting). We employ our schemes on the Google 1 Billion Word Dataset to estimate the fundamental limit of perplexity in language modeling, and to improve graphical model and classification tree learning. The estimators are efficiently computable and exhibit a "sample size boosting" phenomenon, i.e., they attain with n samples what prior methods would have needed n log(n) samples to achieve.
Biography: Jiantao Jiao is a Ph.D. student in the Department of Electrical Engineering at Stanford University. He received the B.Eng. degree in Electronic Engineering from Tsinghua University, Beijing, China in 2012, and the M.Eng. degree in Electrical Engineering from Stanford University in 2014. He is a recipient of the Presidential Award of Tsinghua University and the Stanford Graduate Fellowship. He was a semi-plenary speaker at ISIT 2015 and a co-recipient of the ISITA 2016 Student Paper Award. He co-designed and co-taught the graduate course EE378A (Statistical Signal Processing) at Stanford University in 2016 and 2017, with his advisor Tsachy Weissman. His research interests are in statistical machine learning, high-dimensional and nonparametric statistics, information theory, and their applications in medical imaging, genomics, and natural language processing. He is a co-founder of Qingfan (www.qingfan.com), an online platform that democratizes technical training and job opportunities for anyone with access to the internet.
Host: Salman Avestimehr, avestimehr@gmail.com
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE Seminar: Innovating Secure IoT Solutions for Extreme Environments
Thu, Mar 29, 2018 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rabia Yazicigil, Massachusetts Institute of Technology
Talk Title: Innovating Secure IoT Solutions for Extreme Environments
Abstract: The Internet of Things (IoT) is redefining how we interact with the world by supplying a global view based not only on human-provided data but also human-device connected data. For example, in Health Care, IoT will bring decreased costs, improved treatment results, and better disease management. However, the connectivity-in-everything model brings heightened security concerns. Additionally, the projected growth of connected nodes not only increases security concerns, it also leads to a 1000-fold increase in wireless data traffic in the near future. This data storm results in a spectrum scarcity thereby driving the urgent need for shared spectrum access technologies. These security deficiencies and the wireless spectrum crunch require innovative system-level secure and scalable solutions.
This talk will introduce energy-efficient and application-driven system-level solutions for secure and spectrum-aware wireless communications. I will present a novel ultra-fast bit-level frequency-hopping scheme for physical-layer security. This scheme utilizes the frequency agility of devices in combination with novel radio frequency architectures and protocols to achieve secure wireless communications. To address the wireless spectrum crunch, future smart radio systems will evaluate the spectrum usage dynamically and opportunistically use the underutilized spectrum; this will require spectrum sensing for interferer avoidance. I will discuss a system-level approach using band-pass sparse signal processing for rapid interferer detection in a wideband spectrum to convert the abstract improvements promised by sparse signal processing theory, e.g., fewer measurements, to concrete improvements in time and energy efficiency.
The tightly-coupled system solutions derived at the intersection of electronics, security, signal processing, and communications extend in applications beyond the examples provided here, enabling innovative IoT solutions for extreme environments.
Biography: Rabia Yazicigil is currently a Postdoctoral Associate at MIT. She received her PhD degree in Electrical Engineering from Columbia University in 2016. She received the B.S. degree in Electronics Engineering from Sabanci University, Istanbul, Turkey in 2009, and the M.S. degree in Electrical and Electronics Engineering from Ãcole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland in 2011.
Her research interest lies at the interface of electronics, security, signal processing and communication to innovate system-level solutions for future energy-constrained Internet of Things applications. She has been a recipient of a number of awards, including the "Electrical Engineering Collaborative Research Award" for her PhD research on Compressive Sampling Applications in Rapid RF Spectrum Sensing (2016), the second place at the Bell Labs Future X Days Student Research Competition (2015), Analog Devices Inc. outstanding student designer award (2015) and 2014 Millman Teaching Assistant Award of Columbia University. She was selected among the top 61 female graduate students and postdoctoral scholars invited to participate and present her research work in the 2015 MIT Rising Stars in Electrical Engineering Computer Science.
Host: Peter Beerel, pabeerel@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 - Mercedeh Khajavikhan, Friday, March 30th @ 2pm in EEB 132
Fri, Mar 30, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mercedeh Khajavikhan, University of Central Florida
Talk Title: Non-Hermitian Photonics: Optics at an Exceptional Point
Abstract: In recent years, non-Hermitian degeneracies, also known as exceptional points (EPs), have emerged as a new paradigm for engineering the response of optical systems. At such points, an N-dimensional space can be represented by a single eigenvalue and an eigenvector. As a result, these points are associated with abrupt phase transition in parameter space. Among many different non-conservative photonic configurations, parity-time (PT) symmetric systems are of particular interest since they provide a powerful platform to explore and consequently utilize the physics of exceptional points in a systematic manner. In this talk, I will review some of our recent works in the area of non-Hermitian (mainly PT-symmetric) active photonics. For example, in a series of works, we have demonstrated how the generation and judicial utilization of these points in laser systems can result in unexpected dynamics, unusual linewidth behavior, and improved modal response. On the other hand, biasing a photonic system at an exceptional point can lead to orders of magnitude enhancement in sensitivity- an effect that may enable a new generation of ultrasensitive optical sensors on chip. Non-Hermiticity can also be used as a means to promote or single out an edge mode in photonic topological insulator lattices. This effect has been recently utilized to demonstrate the first magnetic free topological insulator laser. In this talk, I will also discuss other topological behaviors in non-Hermitian systems, especially those associated with encircling an exceptional point in parameter space.
Biography: Mercedeh Khajavikhan received her Ph.D. in Electrical Engineering from the University of Minnesota in 2009. Her dissertation was on coherent beam combining for high power laser applications. In 2009, she joined the University of California in San Diego as a postdoctoral researcher where she worked on the design and development of nanolasers, plasmonic devices, and silicon photonics components. Since August 2012, she is an assistant professor in the College of Optics and Photonics (CREOL) at the University of Central Florida (UCF), working primarily on novel phenomena in active photonic systems. She received the NSF Early CAREER Award in 2015, the ONR Young Investigator Award in 2016, and the University of central Florida Reach for the Stars Award in 2017.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski