SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for March
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Mon, Mar 02, 2020 @ 12:00 PM - 01:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Tuba Yavuz, Electrical and Computer Engineering Department at the University of Florida
Talk Title: Improving IoT Reliability and Security using Automated Model Extraction and Guided Analysis
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The number of Internet of Things (IoT) devices has reached 26 billion in 2019. A typical IoT ecosystem consists of a variety of components including the cloud, mobile devices, edge devices, and constrained devices. Although each component in IoT comes with unique capabilities and challenges, the system software that runs on each type of IoT component forms an important part of the IoT attack surface. Therefore, the ability to perform precise and scalable analysis of system software and to detect deep system vulnerabilities throughout the IoT ecosystem are critical for IoT reliability and security. System software includes the firmware, operating system, device drivers, and libraries. Despite recent advances in program analysis techniques and decision procedures, the complexity of system software creates challenges in terms of scalability and precision.
In this talk, I will introduce Model Extraction and Model Guided Analysis as an approach for effective and scalable analysis of system software. The idea is to use extracted models as oracles in a client analysis, where the client analysis can become a model extraction step for another client analysis, and so on. I will present our experience with Model Extraction and Model Guided analysis in the context of USB and Bluetooth firmware and protocol stacks, Linux device drivers, cryptographic libraries, and SGX enclaves. I will specifically discuss the motivations, challenges, and our achievements using the tools and methodologies we have developed including FirmUSB, ProXray, MOXCAFE, and PROMPT. I will conclude with a vision and a roadmap for Model Extraction and Model Guided Analysis to support the reliable and secure development and evolution of IoT frameworks.
Biography: Dr. Tuba Yavuz is currently an Assistant Professor at the Electrical and Computer Engineering Department of University of Florida (UF). She is also affiliated with the Florida Institute of Cyber Security Research (FICS) andthe Nelms Institute for the Connected World at UF. She received her Ph.D. in computer science from the Computer Science Department of University of California, Santa Barbara in 2004.Her research areas include formal methods, software engineering, and system security. She has recently developed tools and techniques for detecting vulnerabilities and malicious behavior in system software.
Host: Chao Wang, wang626@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
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. -
ECE Seminar: A Cross-Stack, Network-Centric Architectural Design for Next Generation Datacenter
Tue, Mar 03, 2020 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mohammad Alian, PhD Candidate, ECE Dept, UIUC
Talk Title: A Cross-Stack, Network-Centric Architectural Design for Next Generation Datacenter
Abstract: In the light of technology scaling and data explosion trends, the long latency and limited bandwidth of transferring data within a computer and across computers have become a key bottleneck to the improvement of performance and energy efficiency. Tacking this critical challenge, researchers have proposed various near-data processing architectures in the form of in-network and near-memory computing to move computation closer to data. In this talk, first, I introduce a technique that leverages the potentials of in-network processing for efficient power-management of network-connected computers. Then I present Memory Channel Network (MCN), a memory module based, near-memory processing architecture that seamlessly unifies near-memory processing with distributed computing for the acceleration of data-intensive applications.
Biography: Mohammad Alian is a Ph.D. candidate at the Electrical and Computer Engineering Department of the University of Illinois Urbana Champaign. His research is at the intersection of computer architecture and networking where he proposed several cross-stack, near-memory, and in-network computing architectures. His work has been published in top computer architecture and systems venues and recognized by several best paper candidacies and one honorable mention in IEEE MICRO Top Picks 2017. Mohammad holds an M.Sc. degree in computer engineering from the University of Wisconsin-Madison.
Host: Professor Murali Annavaram
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. -
Medical Imaging Seminar
Tue, Mar 03, 2020 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Christian Pichot, Université Côte d'Azur, CNRS, LEAT, SophiaTech Campus
Talk Title: Microwave Tomographic Imaging of Brain Strokes
Series: Medical Imaging Seminar Series
Abstract: Brain strokes are one of the leading causes of disability and mortality in adults in developed countries. Ischemic stroke (85% of total cases) and hemorrhagic stroke (15%) must be treated with opposing therapies, and thus, the nature of the stroke must be determined quickly in order to apply the appropriate treatment. Recent studies in biomedical imaging have shown that strokes produce variations in the complex electric permittivity of brain tissues, which can be detected by means of microwave tomography. Here, we present some synthetic results obtained with an experimental microwave tomography-based portable system for demonstrating the feasibility of such a new imaging modality for the early detection and monitoring of brain strokes. The determination of electric permittivity requires the solution of a coupled forward-inverse problem. Iterative microwave tomographic imaging requires the solution of an inverse problem based on a minimization algorithm (e.g. gradient based) with successive solutions of a direct problem such as the accurate modeling of a whole-microwave measurement system. Synthetic data are obtained with electromagnetic simulations, which have been derived from measurements of an experimental microwave imaging system developed by EMTensor GmbH (Vienna, Austria). Results demonstrate the possibility to detect brain strokes, as well as for monitoring during the treatment, with a microwave system with reasonable running times for image reconstructions when applying the proposed reconstruction algorithm using state-of-the-art numerical modeling and massively parallel computing.
Biography: Christian Pichot is currently a Researcher Emeritus at the French National Center for Scientific Research (CNRS), at the Electronics, Antennas & Telecommunications Laboratory (LEAT), a joint Université Côte d'Azur and CNRS laboratory, 06900 Sophia Antipolis, France.He received the Ph.D. and the Doctor of Science (D.Sc.) degrees from the University of Paris-Sud 11 in 1977 and 1982, respectively.
From 2000 to 2011, he was the Director of the LEAT. From 2008 to 2013, he was the co-founder and co-director of CREMANT, a joint Antenna Research Center, supported by the University of Nice-Sophia Antipolis, CNRS and France Telecom Orange Labs. He received in 1983 the European Microwave Prize. He is an IEEE Fellow for "Contributions to Microwave Imaging and Antenna Design". He received the Medal of Honor of CNRS in 2018, and the Academy of Sciences URSI-France Medal in 2019.
His research activities are concerned with scattering and propagation of Electromagnetic Waves,radiation of antennas, inverse scattering (Microwave Imaging and Tomography, AntennaSynthesis, Complex Permittivity Reconstruction, Object Detection and Recognition) for applications in Radar, Civil engineering, non-destructive evaluation (NDE), non-destructive testing (NDT), geophysics engineering, security and military applications, antennas, telecommunications, and medical domain (biomedical engineering), VLF/LF frequencies, microwaves and millimetre waves.
Host: Krishna Nayak, knayak@usc.edu
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. -
Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Mar 04, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jonathan Sprinkle, University of Arizona
Talk Title: Cyber-Physical Systems for Vehicle-in-the-Flow Traffic Flow Control
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: This talk describes previous and ongoing research in traffic flow control that involve the University of Arizona CAT Vehicle Testbed. The focus of the research is real-time control of vehicle velocity in order to effect the velocity of other vehicles in the flow. Research and results are told through the lens of several physical validation experiments. The first experiment explores how to dampen emerging waves in traffic that are due to congestive effects. This experiment grew out of theory of how traffic flow could be improved through sparse velocity control (e.g., ~5% of the vehicles) in the flow. The second experiment examines an analogous case, where 100% of the vehicles are controlled, though this time using off-the-shelf (rather than customized) cruise control algorithms. The talk will examine the hypotheses, methods, and results of these experiments, and explore the theory and motivation for the research as a means to provide insights into the obtained results. The research was sponsored by the National Science Foundation under award CNS-1446435, the Department of Energy through contract DE-EE0008872, and is collaborative work with Benedetto Piccoli, Benjamin Seibold, Dan Work, and Alexandre Bayen.
Biography: Dr. Jonathan Sprinkle is the Litton Industries John M. Leonis Distinguished Associate Professor of Electrical and Computer Engineering at the University of Arizona. In 2013 he received the NSF CAREER award, and in 2009, he received the UA's Ed and Joan Biggers Faculty Support Grant for work in autonomous systems. His work has an emphasis for industry impact, and he was recognized with the UA "Catapult Award" by Tech Launch Arizona in 2014, and in 2012 his team won the NSF I-Corps Best Team award. From 2017-2019 he served as a Program Director at the National Science Foundation in the division of Computer and Networked Systems. His research interests and experience are in cyber-physical systems control and engineering, and he teaches courses ranging from systems modeling and control to mobile application development and software engineering.
Host: Paul Bogdan, pbogdan@usc.edu
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. -
Xiaonan Hui - ECE-EP Seminar, Thursday, March 5th at 11am in EEB 248
Thu, Mar 05, 2020 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaonan Hui, Cornell University
Talk Title: Harmonic RF sensing from indoor localization to vital signs monitoring
Abstract: When wireless is perfectly applied, the whole earth will be converted into a huge brain, which in fact it is all things being particles of a real and rhythmic whole." For almost a century, electrical engineers are endeavoring to approach what Nikola Tesla predicted in 1926 for a "World Wireless System". However, as of today, many hurdles remain when we think of all things connected rhythmically with interaction and links between the cyber and the physical worlds, because sensing of the "things", especially "living things", is still heavily constrained. The location and shape of objects, as well as the vital signs of people and animals are critical information to the overall systems. In this talk, I will first highlight our solutions of highly reliable and accurate indoor RF ranging, localization and imaging. The demonstrated radio frequency (RF) localization method bypasses the Uncertainty-Principle mathematical model commonly seen in the radar-like system, so that the high temporal (kHz) and spatial (microns) resolutions can be achieved simultaneously with ~915 MHz signals which have deep penetration to many dielectrics of interests such as building materials and living tissues. Vital-sign monitoring is the second part of the talk, including the heartbeat dynamics, respiration, and blood pressures of both central and pulmonary circulations, with the new near-field coherent sensing (NCS) approach, which not only provides unparalleled RF vital-sign signal quality and sensing capability, but also does not require skin touch or motion restraint to greatly improve the applicability to people and animals. The systems in this talk can be implemented in the applications of high precision indoor locating, assisted living, RF bio-tomography, biometrics for security, wearable sensors, and clinical researches. The talk will include the supporting RF theory, the design methods and the hardware/software experimental system, but its content will be aimed for the general audience in engineering.
Biography: Xiaonan Hui is a Ph.D. candidate in the School of Electrical and Computer Engineering at Cornell University. He works with Prof. Edwin Kan and focuses on radio-frequency systems for Cyber Physical System (CPS) and Internet of Things (IoT) applications. His recent works on vital-sign acquisition for people as well as animals were published on high-impact journals and conferences, attracting not only more than 30 news agencies, but also generating broad industrial interests for automotive, medical, pharmaceutical, and digital agricultural applications. Moreover, his high-precision indoor localization works provide an innovative method for IoT tracking, robotic localization, and civil structure integrity monitoring. He is the principal investigator of Cornell Scale-up and Prototype Grants, the winner of Cornell ECE Outstanding Ph.D. Thesis Research Award, and other 3 fellowships. He also serves as the reviewer for Springer Nature, IEEE journals and conferences in the areas of electromagnetic systems, vital-sign sensing and wireless communications. More of his academic information can be found in his website: www.xiaonanhui.com
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
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 Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Thu, Mar 05, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Frederic Sala, Stanford Computer Science Department
Talk Title: Structure to the Rescue: Breaking Data Barriers in Machine Lear
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The current machine learning zeitgeist is that models are only as good as the data they are fed, so that limitations in the data---and especially mismatches with the ML algorithm---present fundamental barriers to model performance. However, for ML to continue its growth and be safely and widely deployed across domains with significant societal impact, such limitations must be minimized. In this talk, I will describe two ways to exploit structure in data to overcome apparent obstacles, with theoretical guarantees.
First, I will argue that geometry is a barrier to producing quality representations used by models. The root cause is a mismatch between the geometric structure of the data and the geometry of the model---but the issue can be resolved by adopting matching non-Euclidean geometries, relying on, for example, hyperbolic geometry for hierarchical data. Next, motivated by the fact that labeling large datasets is a major bottleneck in supervised learning, I will discuss a weak supervision framework for automating the process of labeling, overcoming the lack of hand-labeled data. This is done by encapsulating different aspects of manual labeling into heuristics whose structure is characterized by learnable accuracies and correlations. I will describe extensions of this framework to handle multitask, time-series, and other forms of structured data. This framework is widely used in industry, helping drive applications used by millions daily.
Biography: Frederic Sala is a postdoctoral scholar in the Stanford Computer Science Department, advised by Chris Ré. His research interests include machine learning, data-driven systems, and information and coding theory, and in particular problems related to the analysis and design of algorithms that operate on diverse and challenging forms of data. He received the Ph.D. and M.S. degrees in Electrical Engineering from UCLA, where he received the Distinguished Ph.D. Dissertation in Signals & Systems Award from the UCLA Electrical Engineering Department, the NSF graduate fellowship, and the Edward K. Rice Outstanding Master's Student Award.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
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. -
ECE Seminar: Collaborative Perception and Learning Between Robots and the Cloud
Thu, Mar 05, 2020 @ 02:15 PM - 03:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sandeep Chinchali, PhD Candidate, Dept of CS, Stanford University
Talk Title: Collaborative Perception and Learning Between Robots and the Cloud
Abstract: Augmenting robotic intelligence with cloud connectivity is considered one of the most promising solutions to cope with growing volumes of rich robotic sensory data and increasingly complex perception and decision-making tasks. While the benefits of cloud robotics have been envisioned long before, there is still a lack of flexible methods to trade-off the benefits of cloud computing with end-to-end systems costs of network delay, cloud storage, human annotation time, and cloud-computing time. To address this need, I will introduce decision-theoretic algorithms that allow robots to significantly transcend their on-board perception capabilities by using cloud computing, but in a low-cost, fault-tolerant manner.
Specifically, for compute-and-power-limited robots, I will present a lightweight model selection algorithm that learns when a robot should exploit low-latency on-board computation, or, when highly uncertain, query a more accurate cloud model. Then, I will present a collaborative learning algorithm that allows a diversity of robots to mine their real-time sensory streams for valuable training examples to send to the cloud for model improvement. The utility of these algorithms will be demonstrated on months of field data and experiments on state-of-the-art embedded deep learning hardware. I will conclude this talk by outlining a number of future research directions on the systems and theoretical aspects of networked system control, some of which extend beyond cloud robotics.
Biography: Sandeep Chinchali is a computer science PhD candidate at Stanford, advised by Sachin Katti and Marco Pavone. Previously, he was the first principal data scientist at Uhana, a Stanford startup working on data-driven optimization of cellular networks, now acquired by VMWare. His research on networked control has led to proof-of-concept trials with major cellular network operators and was a finalist for best student paper at Robotics: Science and Systems 2019. Prior to Stanford, he graduated from Caltech, where he worked on robotics at NASA's Jet Propulsion Lab (JPL). He is a recipient of the Stanford Graduate Fellowship and National Science Foundation (NSF) fellowships.
Host: Host: Professor Konstantinos Psounis
Location: Michelson Center for Convergent Bioscience (MCB) - 102
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. -
Medical Imaging Seminar
Fri, Mar 06, 2020 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rudolf Stollberger, Graz University of Technology, Institute of Medical Engineering
Talk Title: Variational Reconstruction of Highly Undersampled 3D Multiple Frame Acquisitions
Series: Medical Imaging Seminar Series
Abstract: Time dependent or quantitative multiple frame acquisitions are particular well suited for the combination of accelerated acquisition and sophisticated iterative reconstruction techniques with spatial-temporal regularization or model based approaches. In this presentation the potential of variational reconstruction for dynamic MRI, for ASL and for model based quantification is explored. Although the applications are quite different, some basic principles are common to all.
For dynamic data iterative reconstruction with infimal convolution of total generalized variation (ICTGV) functionals has shown to allow temporal resolution below 1s for 3D measurements with 40 slices (3202) with excellent suppression of sub-sampling artifacts. This approach will be compared with a variational network for dynamic multi-coil cardiac data. Another example exists for accelerated time encoded CAIPIRINHA ASL data. For this application, the whole brain can be acquired within a single shot which increases the robustness against motion compared to standard segmented acquisition. A third application area consists in quantitative MRI. Model based reconstruction allow the determination of 3D isotropic T1 maps (1mm3) with an acquisition time of 1.8-“1.1 s/slice for the variable flip angle method (VFA). The variational techniques can process 4D array coil data, which is still a challenge for DL-based approaches. Reconstruction times start at about 4 minutes for 4D-ASL data and are somewhat longer for dynamic MRI, but can be many times longer for model-based reconstruction of 4D qMRI data with a nonlinear signal model like VFA.
Host: Krishna Nayak, knayak@usc.edu
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. -
Spring 2020 Joint CSC@USC/CommNetS-MHI Seminar Series
Mon, Mar 09, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Bruno Ribeiro, Purdue University
Talk Title: Unearthing the relationship between graph neural networks and matrix factorization
Abstract: Graph tasks are ubiquitous, with applications ranging from recommendation systems, to language understanding, to automation with environmental awareness and molecular synthesis. A fundamental challenge in applying machine learning to these tasks has been encoding (representing) the graph structure in a way that ML models can easily exploit the relational information in the graph, including node and edge features. Until recently, this encoding has been performed by factor models (a.k.a. matrix factorization embeddings), which arguably originated in 1904 with Spearman's common factors. Recently, however, graph neural networks have introduced a new powerful way to encode graphs for machine learning models. In my talk, I will describe these two approaches and then introduce a unifying mathematical framework using group theory and causality that connects them. Using this novel framework, I will introduce new practical guidelines to generating and using node embeddings and graph representations, which fixes significant shortcomings of the standard operating procedures used today.
Biography: Bruno Ribeiro is an Assistant Professor in the Department of Computer Science at Purdue University. He obtained his Ph.D. at the University of Massachusetts Amherst and did his postdoctoral studies at Carnegie Mellon University from 2013-2015. His research interests are in representation learning and data mining, with a focus on sampling and modeling relational and temporal data. He received an NSF CAREER award in 2020 and the ACM SIGMETRICS best paper award in 2016.
Host: Prof. Antonio Ortega, aortega@usc.edu
More Info: http://csc.usc.edu/seminars/2020Spring/ribeiro.html
More Information: 200309_Bruno Ribeiro_CSC Seminar.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Brienne Moore
Event Link: http://csc.usc.edu/seminars/2020Spring/ribeiro.html
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. -
ECE Seminar: Software-Hardware Systems for the Internet of Things
Tue, Mar 10, 2020 @ 10:45 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Omid Abari, School of Computer Science, University of Waterloo
Talk Title: Software-Hardware Systems for the Internet of Things
Abstract: Recently, there has been a huge interest in Internet of Things (IoT) systems, which bring the digital world into the physical world around us. However, barriers still remain to realizing the dream applications of IoT. One of the biggest challenges in building IoT systems is the huge diversity of their demands and constraints (size, energy, latency, throughput, etc.). For example, virtual reality and gaming applications require multiple gigabits-per-second throughput and millisecond latency. Tiny sensors spread around a greenhouse or smart home must be low-cost and batteryless to be sustainable in the long run. Today's networking technologies fall short in supporting these IoT applications with a hugely diverse set of constraints and demands. As such, they require distinct innovative solutions. In this talk, I will describe how we can design a new class of networking technologies for IoT by designing software and hardware jointly, with an understanding of the intended application. In particular, I will present two examples of our solutions. The first solution tackles the throughput limitations of existing IoT networks by developing new millimeter wave devices and protocols, enabling many new IoT applications, such as untethered high-quality virtual reality. The second solution tackles the energy limitations of IoT networks by introducing new wireless devices that can sense and communicate without requiring any batteries. I demonstrate how our solution is applicable in multiple, diverse domains such as HCI, medical, and agriculture. I will conclude the talk with future directions in IoT research, both in terms of technologies and applications.
Biography: Omid Abari is an Assistant Professor at the University of Waterloo, School of Computer Science. He received his Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 2018. His research interests are in the area of computer networks and mobile systems, with applications to the Internet of Things (IoT). He is currently leading the Intelligent Connectivity (ICON) Lab, where his team focuses on the design and implementation of novel software-hardware systems that deliver ubiquitous sensing, communication and computing at scale. His work has been selected for GetMobile research highlights (2018, 2019), and been featured by several media outlets, including Wired, TechCrunch, Engadget, IEEE Spectrum, and ACM Tech News.
Host: Professor Konstantinos Psounis
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. -
Microwave Inverse Imaging Meets Deep Learning
Tue, Mar 10, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Uday Khankhoje, Electrical Engineering at the Indian Institute of Technology Madras
Talk Title: Microwave Inverse Imaging Meets Deep Learning
Abstract: In this talk, I will start by motivating the area of inverse microwave imaging -- an area that brings together electromagnetics, signal processing, and data analytics. The objective here is to infer the electrical properties of an object by studying how it scatters electromagnetic fields -- all without making contact, i.e. remotely. The applications are diverse, from breast cancer imaging to microwave remote sensing. At the heart of this problem lies a challenging ill-posed nonlinear optimization problem. I will describe some of the contemporary methods of solving this problem and highlight the challenges faced. Subsequently, I will present some of our recent methods and results, where we have significantly pushed the state of the art by incorporating deep neural networks into existing physics-based algorithms.
Biography: Uday Khankhoje is an Assistant Professor of Electrical Engineering at the Indian Institute of Technology Madras, Chennai, India, since 2016. He received a B.Tech. degree from the Indian Institute of Technology Bombay, Mumbai, India, in 2005, an M.S. and PhD. degrees from the California Institute of Technology (Caltech), Pasadena, USA, in 2010, all in Electrical Engineering. He was a Caltech Postdoctoral Scholar at the Jet Propulsion Laboratory (NASA/Caltech) from 2011-2012, a Postdoctoral Research Associate in the Department of Electrical Engineering at the University of Southern California, Los Angeles, USA, from 2012-2013, and an Assistant Professor of Electrical Engineering at the Indian Institute of Technology Delhi from 2013-2016. His research interests are in the area of computational electromagnetics and its applications to remote sensing and inverse imaging.
Host: Prof. Constantine Sideris, csideris@usc.edu
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. -
ECE Seminar: Compiler and Runtime Systems for Homomorphic Encryption and Graph Analytics
Wed, Mar 11, 2020 @ 10:45 AM - 11:45 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Roshan Dathathri, PhD candidate, Dept of CS, University of Texas at Austin
Talk Title: Compiler and Runtime Systems for Homomorphic Encryption and Graph Analytics
Abstract: Distributed and heterogeneous architectures are tedious to program because devices such as CPUs, GPUs, FPGAs, and TPUs provide different programming abstractions and may have disjoint memories, even if they are on the same machine. In this talk, I present compiler and runtime systems that make it easier to develop efficient programs for privacy-preserving computation and graph analytics applications on such architectures.
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations on encrypted data without requiring a secret key. Recent cryptographic advances have pushed FHE into the realm of practical applications. However, programming these applications remains a huge challenge, as it requires cryptographic domain expertise to ensure correctness, security, and performance. I present CHET, a domain-specific optimizing compiler, that is designed to make the task of programming neural network inference applications using FHE easier. CHET automates many laborious and error prone programming tasks including encryption parameter selection to guarantee security and accuracy of the computation, determining efficient data layouts, and performing scheme-specific optimizations. Our evaluation of CHET on a collection of popular neural networks shows that CHET-generated programs outperform expert-tuned ones by an order of magnitude.
Applications in several areas like machine learning, bioinformatics, and security need to process and analyze very large graphs. Distributed clusters are essential in processing such graphs in reasonable time. I present a novel approach to building distributed graph analytics systems that exploits heterogeneity in processor types, partitioning policies, and programming models. The key to this approach is Gluon, a domain-specific communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. Systems built using Gluon outperform previous state-of-the-art systems and scale well up to 256 CPUs and 64 GPUs.
Biography: Roshan is a Ph.D. candidate advised by Prof. Keshav Pingali in the University of Texas at Austin. He works on domain-specific programming languages, compilers, and runtime systems that make it easy to develop efficient sparse computation and privacy-preserving computation on large-scale distributed clusters, while utilizing heterogeneous architectures. He has built programming systems for distributed and heterogeneous graph analytics and privacy-preserving neural network inferencing. He received his masters from Indian Institute of Science advised by Prof. Uday Bondhugula, where he worked on automatic parallelization of affine loop nests for distributed and heterogeneous architectures.
Host: Professor Massoud Pedram
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. -
ECE Seminar: Safe Deep Learning in the Feedback Loop: A Robust Control Approach
Mon, Mar 23, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mahyar Fazlyab, Postdoctoral Researcher, Dept of ESE, University of Pennsylvania
Talk Title: Safe Deep Learning in the Feedback Loop: A Robust Control Approach
Abstract: Despite high-profile advances in various decision-making and classification tasks, Deep Neural Networks (DNNs) face several fundamental challenges that limit their adoption in physical or safety-critical domains. In particular, DNNs can be vulnerable to adversarial attacks and input perturbations. This issue becomes even more pressing when DNNs are used in closed-loop systems, where a small perturbation (caused by, for example, noisy measurements, uncertain initial conditions, or disturbances) can substantially impact the system being controlled. Therefore, it is of utmost importance to develop tools that can provide useful certificates of stability, safety, and robustness for DNN-driven systems.
In this talk, I will present a new framework, rooted in convex optimization and robust control, for safety verification and robustness analysis of DNNs based on semidefinite programming. The main idea is to abstract the original, nonlinear, hard-to-analyze neural network by a Quadratically-Constrained Linear Network (QCLN), in which the nonlinear components (e.g., the activation functions) are described by the quadratic constraints that all their input-output instances satisfy. This abstraction allows us to analyze various properties of DNNs (safety, local and global robustness, etc.) using semidefinite programming.
Biography: Mahyar Fazlyab received the Bachelor's and Master's degrees in mechanical engineering from Sharif University of Technology, Tehran, Iran, in 2010 and 2013, respectively. He earned a Master's degree in statistics and a Ph.D. degree in Electrical and Systems Engineering (ESE) from the University of Pennsylvania (UPenn), Philadelphia, PA, USA, in 2018. Currently, he is a Postdoctoral Researcher at UPenn. His research interests are at the intersection of optimization, control, and machine learning. His current work focuses on developing optimization-based methods for safety verification of learning-enabled control systems. Dr. Fazlyab won the Joseph and Rosaline Wolf Best Doctoral Dissertation Award in 2019, awarded by the ESE Department at UPenn.
Host: Mihailo Jovanovic, mihailo@usc.edu, 213.740.4474
Webcast: https://usc.zoom.us/j/871407253WebCast Link: https://usc.zoom.us/j/871407253
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. -
ECE Seminar: Reliability, Equity, and Reproducibility in Modern Machine Learning
Tue, Mar 24, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yaniv Romano, Postdoctoral Scholar, Dept of Statistics, Stanford University
Talk Title: Reliability, Equity, and Reproducibility in Modern Machine Learning
Abstract: Modern machine learning algorithms have achieved remarkable performance in a myriad of applications, and are increasingly used to make impactful decisions in the hiring process, criminal sentencing, healthcare diagnostics and even to make new scientific discoveries. The use of data-driven algorithms in high-stakes applications is exciting yet alarming: these methods are extremely complex, often brittle, notoriously hard to analyze and interpret. Naturally, concerns have raised about the reliability, fairness, and reproducibility of the output of such algorithms. This talk introduces statistical tools that can be wrapped around any "black-box" algorithm to provide valid inferential results while taking advantage of their impressive performance. We present novel developments in conformal prediction and quantile regression, which rigorously guarantee the reliability of complex predictive models, and show how these methodologies can be used to treat individuals equitably. Next, we focus on reproducibility and introduce an operational selective inference tool that builds upon the knockoff framework and leverages recent progress in deep generative models. This methodology allows for reliable identification of a subset of important features that is likely to explain a phenomenon under-study in a challenging setting where the data distribution is unknown, e.g., mutations that are truly linked to changes in drug resistance.
Biography: Yaniv Romano is a postdoctoral scholar in the Department of Statistics at Stanford University, advised by Prof. Emmanuel Candes. He earned his Ph.D. and M.Sc. degrees in 2017 from the Department of Electrical Engineering at the Technion-”Israel Institute of Technology, under the supervision of Prof. Michael Elad. Before that, in 2012, Yaniv received his B.Sc. from the same department. His research spans the theory and practice of selective inference, sparse approximation, machine learning, data science, and signal and image processing. His goal is to advance the theory and practice of modern machine learning, as well as to develop statistical tools that can be wrapped around any data-driven algorithm to provide valid inferential results. Yaniv is also interested in image recovery problems: the super-resolution technology he invented together with Dr. Peyman Milanfar is being used in Google's flagship products, increasing the quality of billions of images and bringing significant bandwidth savings. In 2017, he constructed with Prof. Michael Elad a MOOC on the theory and practice of sparse representations, under the edX platform. Yaniv is a recipient of the 2015 Zeff Fellowship, the 2017 Andrew and Erna Finci Viterbi Fellowship, the 2017 Irwin and Joan Jacobs Fellowship, the 2018-2020 Zuckerman Postdoctoral Fellowship, the 2018-2020 ISEF Postdoctoral Fellowship, the 2018-2020 Viterbi Fellowship for nurturing future faculty members, Technion, and the 2019-2020 Koret Postdoctoral Scholarship, Stanford University. Yaniv was awarded the 2020 SIAG/IS Early Career Prize.
Host: Salman Avestimehr, avestime@usc.edu
Webcast: https://usc.zoom.us/j/782728120WebCast Link: https://usc.zoom.us/j/782728120
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. -
ECE Seminar: Label-free Optical Imaging of Living Biological Systems
Mon, Mar 30, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sixian You, PhD, Bioengineering, UIUC
Talk Title: Label-free Optical Imaging of Living Biological Systems
Abstract: Label-free optical imaging of living biological systems offers rich information that can be of immense value in biomedical tasks such as diagnosing cancer or assessing the tumor microenvironment. Despite the exceptional theoretical potential, current label-free nonlinear microscopy platforms are challenging for real-world clinical and biological applications. The major obstacles include the lack of flexible laser sources, limited contrast, and lack of molecular specificity for diseases.
In this talk, I will present a new optical imaging platform and methodology that will address these challenges. By generating and tailoring coherent supercontinuum from photonic crystal fibers, single-source single-shot metabolic and structural imaging can be achieved, enabling Simultaneous Label-free Auto-fluorescence Multi-harmonic (SLAM) contrast in living cells and tissues. These capabilities further motivate development of analytical tools for tissue assessment and diagnosis, showing broad potential of this label-free imaging technology in discovering new metabolic biomarkers and enabling real-time point-of-procedure applications.
Biography: Sixian You received her Ph.D. in 2019 from the University of Illinois, Urbana-Champaign (UIUC), under the guidance of Prof. Stephen A. Boppart. Her primary research interest is in developing innovative optical imaging solutions for biomedicine. She is particularly interested in developing next-generation label-free multiphoton imaging technologies to study the tumor microenvironment. Sixian was awarded the Microscopy Innovation Award by the Microscopy Society of America and McGinnis Medical Innovation Graduate Fellowship by UIUC.
Host: Justin Haldar, jhaldar@usc.edu
Webcast: https://usc.zoom.us/j/402440976WebCast Link: https://usc.zoom.us/j/402440976
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.
