SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for March
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ECE Seminar: Protecting User Security and Privacy in Emerging Computing Platforms
Tue, Mar 01, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Yuan Tian, Assistant Professor, Department of Computer Science, University of Virginia
Talk Title: Protecting User Security and Privacy in Emerging Computing Platforms
Abstract: Computing is undergoing a significant shift. First, the explosive growth of the Internet of Things (IoT) enables users to interact with computing systems and physical environments in novel ways through perceptual interfaces (e.g., microphones and cameras). Second, machine learning algorithms collect huge amounts of data and make critical decisions on new computing systems. While these trends bring unprecedented functionality, they also drastically increase the number of untrusted algorithms, implementations, interfaces, and the amount of private data processed by them, endangering user security and privacy. To regulate these security and privacy issues, privacy regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) went into effect. However, there is a huge gap between the desired high-level security/privacy/ethical properties (from regulations, specifications, users' expectations) and low-level real implementations.
To bridge the gap, my work aims to change how platform architects design secure systems, assist developers by detecting security and privacy violation of implementations and build usable and scalable privacy-preserving systems. In this talk, I will present how my group designs principled solutions to ensure modern and emerging computing platforms' security and privacy. In this talk, I will introduce two developer tools we build to detect security and privacy violations. Using the tools, we found large numbers of policy violations in healthcare voice applications and security property violations in IoT messaging protocol implementations. Additionally, I will discuss our recent work on scalable privacy-preserving machine learning.
Biography: Yuan Tian is an Assistant Professor of Computer Science at the University of Virginia. Before joining UVA, she obtained her Ph.D. from Carnegie Mellon University in 2017 and interned at Microsoft Research, Facebook, and Samsung Research. Her research interests involve security and privacy and its interactions with computer systems, machine learning, and human-computer interaction. Her current research focuses on developing new computing platforms with strong security and privacy features, particularly in the Internet of Things and mobile systems. Her work has real-world impacts as countermeasures and design changes have been integrated into platforms (such as Android, Chrome, Azure, and iOS), and also impacted the security recommendations of standard organizations such as the Internet Engineering Task Force (IETF). She is a recipient of Google Research Scholar Award 2021, Facebook Research Award 2021, NSF CAREER award 2020, NSF CRII award 2019, Amazon AI Faculty Fellowship 2019, CSAW Best Security Paper Award 2019, and Rising Stars in EECS 2016. Her research has appeared in top-tier venues in security, machine learning, and systems. Her projects have been covered by media outlets such as IEEE Spectrum, Forbes, Fortune, Wired, and Telegraph.
Host: Host: Dr. Konstantinos Psounis, kpsounis@usc.edu
Webcast: https://usc.zoom.us/j/97735008231?pwd=WEJCcDJpdnZsaEZxczA0SEtaKzBJdz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/97735008231?pwd=WEJCcDJpdnZsaEZxczA0SEtaKzBJdz09
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: Full Stack Deep Learning at the Edge
Wed, Mar 02, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Amir Gholami, Research Scientist, RiseLab and BAIR at UC Berkeley
Talk Title: Full Stack Deep Learning at the Edge
Abstract: An important next milestone in machine learning is to bring intelligence to the edge without relying on the computational power of the cloud. This could lead to more reliable, lower latency, and privacy preserving AI for a wide range of applications. However, state-of-the-art NN models require prohibitive amounts of compute, memory, and energy resources which is often not available at the edge. Addressing these challenges without compromising on accuracy, requires a multi-faceted approach, including hardware-aware model compression and accelerator co-design.
In this talk, I will first discuss a novel hardware-aware method for neural network quantization and pruning that achieves optimal trade-off between accuracy, latency, and model size. In particular, I will discuss a new Hessian Aware Quantization (HAWQ) method that relies on second-order information to perform low precision quantization of the model with minimal generalization loss. I will present extensive testing of the method on different learning tasks including various models for image classification, object detection, natural language processing, and speech recognition showing that HAWQ exceeds previous baselines. I will then present a recent extension of this method which allows integer-only inference for the end-to-end computations, enabling efficient deployment on fixed-point hardware. Finally, I will discuss a full-stack hardware-aware neural network architecture and accelerator design, which enables adapting the model architecture and the accelerator parameters to achieve optimal performance.
Related paper:
ICML'21: HAWQ-V3: Dyadic Neural Network Quantization
ICML'21: I-BERT: Integer-only BERT Quantization
Biography: Amir Gholami is a research scientist in RiseLab and BAIR at UC Berkeley. He received his PhD from UT Austin, working on large scale 3D image segmentation, a research topic which received UT Austin's best doctoral dissertation award in 2018. He is a Melosh Medal finalist, the recipient of best student paper award in SC'17, Gold Medal in the ACM Student Research Competition, best student paper finalist in SC'14, as well as Amazon Machine Learning Research Award in 2020. He was also part of the Nvidia team that for the first time made low precision neural network training possible (FP16), enabling more than 10x increase in compute power through tensor cores. That technology has been widely adopted in GPUs today. Amir's current research focuses on efficient AI, AutoML, and scalable training of Neural Network models.
Host: Host: Dr. Massoud Pedram, pedram@usc.edu
Webcast: https://usc.zoom.us/j/95064180366?pwd=SVJ3VzZ3aGNRKzNLdmJQeGRhdzBUZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/95064180366?pwd=SVJ3VzZ3aGNRKzNLdmJQeGRhdzBUZz09
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. -
Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Mar 02, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dimos V. Dimarogonas, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology
Talk Title: Multi-robot Task Planning and Control Under Spatiotemporal Specifications
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Multi-robot task planning and control under temporal logic specifications has been gaining increasing attention in recent years due to its applicability among others in autonomous systems, manufacturing systems, service robotics and intelligent transportation. Initial approaches considered qualitative logics, such as Linear Temporal Logic, whose automata representation facilitates the direct use of model checking tools for correct-by-design control synthesis. In many real world applications however, there is a need to quantify spatial and temporal constraints, e.g., in order to include deadlines and separation assurance bounds. This led to the use of quantitative logics, such as Metric Interval and Signal Temporal Logic, to impose such spatiotemporal constraints. However, the lack of automata representations for such specifications hinders the direct use of model checking tools. Motivated by this, the use of transient control methodologies that fulfil the aforementioned qualitative constraints becomes evident. In this talk, we review some of our recent results in applying transient control techniques, and in particular Model Predictive Control, Barrier Certificates based design and Prescribed Performance Control, to distributed multi-robot task planning under spatiotemporal specifications. The results are supported by relevant experimental validations.
Biography: Dimos V. Dimarogonas received the Diploma in Electrical and Computer Engineering in 2001 and the Ph.D. in Mechanical Engineering in 2007, both from National Technical University of Athens (NTUA), Greece. Between 2007 and 2010, he held postdoctoral positions at the KTH Royal Institute of Technology, Dept of Automatic Control and MIT, Laboratory for Information and Decision Systems (LIDS). He is currently Professor at the Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, at KTH. His current research interests include multi-agent systems, hybrid systems and control, robot navigation and manipulation, human-robot-interaction and networked control. He serves in the Editorial Board of Automatica and the IEEE Transactions on Control of Network Systems and is a Senior Member of IEEE. He is a recipient of the ERC Starting Grant in 2014, the ERC Consolidator Grant in 2019, and the Knut och Alice Wallenberg Academy Fellowship in 2015.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxwLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw
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: Foundations of Trusted AI for Molecular Inference: the Role of Sparsity
Thu, Mar 03, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Amirali Aghazadeh, Postdoctoral Researcher, EECS Department, University of California, Berkeley
Talk Title: Foundations of Trusted AI for Molecular Inference: the Role of Sparsity
Abstract: Recent breakthroughs in artificial intelligence (AI) have enabled accurate prediction of protein structures from their sequences and have opened up new avenues for the engineering of proteins, drugs, and molecules with advanced and novel functional properties. However, despite their high predictive power, AI models do not provide a mechanistic understanding of interactions that give rise to many functional properties. Moreover, their generalization power has remained limited for novel and rapidly evolving molecules for which sufficient sequence data is not available.
In this talk, I will describe how I developed a foundation for trusted AI in molecular inference. Key to my approach is the observation that the combinatorial landscapes of molecular properties reside in low dimensional subspaces characterized by sparse high order non-linear interactions. I will show how we can leverage this sparsity prior and develop new algorithms rooted in signal processing, coding and graph theory to efficiently explain, regularize, and build molecular AI models. My algorithms have resulted in a drastic reduction in the number of sequences required to infer functional properties in proteins and an improved understanding of high order interactions in the DNA repair process. I will conclude by describing how my works set the computational and statistical foundation for engineering programmable molecular machines.
Biography: Amirali Aghazadeh is a postdoctoral researcher in the Electrical Engineering and Computer Science department at the University of California, Berkeley, working with Kannan Ramchandran. Prior to that, he was a postdoctoral researcher at Stanford University with David Tse after receiving his PhD degree in Electrical and Computer Engineering from Rice University with Richard Baraniuk. His research interest is at the interface of large-scale machine learning, signal processing, and molecular engineering. He is the recipient of the Hershel M. Rich Invention Award for his thesis on universal molecular diagnostics as well as the Texas Instruments Fellowship. He received his Bachelor's degree in Electrical Engineering from Sharif University of Technology.
Host: Dr. Sandeep Gupta, sandeep@usc.edu
Webcast: https://usc.zoom.us/j/98808075733?pwd=MktQYUc0Z2lhZ3NZd09uTURYUFBzUT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/98808075733?pwd=MktQYUc0Z2lhZ3NZd09uTURYUFBzUT09
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: New Generation Photoacoustic Imaging: From benchtop wholebody imagers to wearable sensors
Fri, Mar 04, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Lei Li, Postdoctoral Scholar, Department of Medical Engineering, California Institute of Technology
Talk Title: New Generation Photoacoustic Imaging: From benchtop wholebody imagers to wearable sensors
Abstract: Whole-body imaging has played an indispensable role in preclinical research by providing high-dimensional physiological, pathological, and phenotypic insights with clinical relevance. Yet, pure optical imaging suffers from either shallow penetration or a poor depth-to-resolution ratio, and non-optical techniques for whole-body imaging of small animals lack either spatiotemporal resolution or functional contrast. We have developed a dream machine, demonstrating that a stand-alone single-impulse panoramic photoacoustic computed tomography (SIP-PACT) mitigates these limitations by combining high spatiotemporal resolution, deep penetration, anatomical, dynamical and functional contrasts, and full-view fidelity. SIP-PACT has imaged in vivo whole-body dynamics of small animals in real time, mapped whole-brain functional connectivity, and tracked circulation tumor cells without labeling. It also has been scaled up for human breast cancer diagnosis. SIP-PACT opens a new window for medical researchers to test drugs and monitor longitudinal therapy without the harm from ionizing radiation associated with X-ray CT, PET, or SPECT. Genetically encoded photochromic proteins benefit photoacoustic computed tomography (PACT) in detection sensitivity and specificity, allowing monitoring of tumor growth and metastasis, multiplexed imaging of multiple tumor types at depths, and real-time visualization of protein-protein interactions in deep-seated tumors. Integrating the newly developed microrobotic system with PACT permits deep imaging and precise control of the micromotors in vivo and promises practical biomedical applications, such as drug delivery. In addition, to shape the benchtop PACT systems toward portable and wearable devices with low cost without compromising the imaging performance, we recently have developed photoacoustic topography through an ergodic relay, a high-throughput imaging system with significantly reduced system size, complexity, and cost, enabling wearable applications. As a rapidly evolving imaging technique, photoacoustic imaging promises preclinical applications and clinical translation.
Biography: Lei Li obtained his Ph.D. degree from the Department of Electrical Engineering at California Institute of Technology (Caltech) in 2019. He received his MS degrees at Washington University in St. Louis in 2016. He is currently a postdoctoral scholar in the Department of Medical Engineering at Caltech. His research focuses on developing next-generation medical imaging technology for understanding the brain better, diagnosing early-stage cancer, and wearable monitoring of human vital signs. He was selected as a TED fellow in 2021 and a rising star in Engineering in Health by Columbia University and Johns Hopkins University (2021). He received the Charles and Ellen Wilts Prize from Caltech in 2020 and was selected as one of the Innovators Under 35 by MIT Technology Review in 2019. He is also a two-time winner of the Seno Medical Best Paper Award granted by SPIE (2017 and 2020, San Francisco).
Host: Dr. Justin Haldar, jhaldar@usc.edu
Webcast: https://usc.zoom.us/j/97334155702?pwd=SFlvZ2Y0b3pHMEFxalhNdmxvdU5odz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/97334155702?pwd=SFlvZ2Y0b3pHMEFxalhNdmxvdU5odz09
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: Rethinking Hardware for Cryptography, Security, and Privacy
Tue, Mar 08, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Daniel Moghimi, Computer Science and Engineering, UC San Diego
Talk Title: Rethinking Hardware for Cryptography, Security, and Privacy
Abstract: Modern computers run on top of complex processors, but complexity is the worst enemy of security. Scientists and engineers have consistently tried to develop secure software systems for decades. However, my work shows that new classes of vulnerabilities in complicated processors can break the security guarantees provided by software systems, cryptographic protocols, and privacy technologies. In this talk, I will give an overview of my work on discovering, evaluating, and mitigating such vulnerabilities. First, I will talk about side-channel attacks on cryptographic implementations. Second, I will discuss vulnerabilities at the microarchitecture level. Finally, I highlight my future work on improving security and privacy through automated testing for hardware vulnerabilities and hardware-software co-design.
Biography: Daniel Moghimi (https://moghimi.org) is a postdoctoral fellow in Computer Science and Engineering at UCSD. Previously, he received his MS.c in CS and Ph.D. in ECE from Worcester Polytechnic Institute. He develops new techniques and tools to discover new classes of vulnerabilities in hardware, evaluate their impact on software, particularly cryptography, and defend against these vulnerabilities. His work has improved the security of commodity processors and cryptographic products used by billions of users worldwide. Several of his publications have been covered by the news media such as Forbes, Wired, and The Register.
Host: Dr. Salman Avestimehr, avestime@usc.edu
Webcast: https://usc.zoom.us/j/96000769674?pwd=ZzJXNmgyNTY1dmo4c21sWXZpSjFuQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/96000769674?pwd=ZzJXNmgyNTY1dmo4c21sWXZpSjFuQT09
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: Algebraic Neural Networks: Stability to Deformations
Wed, Mar 09, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alejandro Parada-Mayorga, Postdoctoral Researcher, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia
Talk Title: Algebraic Neural Networks: Stability to Deformations
Abstract: Convolutional architectures play a central role on countless scenarios in machine learning, and the numerical evidence that proves the advantages of using them is overwhelming. Theoretical insights have provided solid explanations about why such architectures work well. These analysis apparently different in nature, have been performed considering signals defined on different domains and with different notions of convolution, but with remarkable similarities in the final results, posing then the question of whether there exists an explanation for this at a more structural level. In this talk we provide an affirmative answer to this question with a first principles analysis introducing algebraic neural networks (AlgNNs), which rely on algebraic signal processing and algebraic signal models. In particular, we study the stability properties of algebraic neural networks showing that stability results for traditional CNNs, graph neural networks (GNNs), group neural networks, graphon neural networks, or any formal convolutional architecture, can be derived as particular cases of our results. This shows that stability is a universal property - at an algebraic level - of convolutional architectures, and this also explains why the remarkable similarities we find when analyzing stability for each particular type of architecture.
Biography: Alejandro Parada-Mayorga (alejopm@seas.upenn.edu) received his B.Sc. and M.Sc. degrees in electrical engineering from Universidad Industrial de Santander, Colombia, in 2009 and 2012, respectively, and his Ph.D. degree in electrical engineering from the University of Delaware, Newark, 2019. Currently, he is a postdoctoral researcher at the University of Pennsylvania, Philadelphia, under the supervision of Prof. Alejandro Ribeiro. His research interests include algebraic signal processing, algebraic neural networks, graph neural networks, graph signal processing, and applications of representation theory of algebras and category theory.
Host: Dr. Shri Narayanan, shri@ee.usc.edu
Webcast: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/92088625170?pwd=enhYNUpicEYvS0R5SEViVVBobjQ1dz09
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. -
Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Mar 09, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Swarat Chaudhuri, Computer Science Department, The University of Texas at Austin
Talk Title: Neurosymbolic Programming
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: I will speak about Neurosymbolic programming, an emerging research area that bridges the fields of deep learning and program synthesis. Like in classic machine learning, the goal here is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient learning. Compositional programming abstractions can also be a natural way of reusing learned modules across learning tasks.
In the talk, I will illustrate some of the potential benefits of research in this area. I will also categorize the main ways in which symbolic and neural learning techniques come together here. I will conclude with a discussion of the open technical challenges in the field.
Biography: Swarat Chaudhuri (http://www.cs.utexas.edu/~swarat) is an Associate Professor of Computer Science and the director of the Trishul laboratory at UT Austin. His research lies at the interface of programming languages, logic, and machine learning. Through a synthesis of ideas from these areas, he seeks to develop a new generation of intelligent systems that are designed to be reliable, transparent, secure, and that can solve complex procedural tasks beyond the scope of contemporary AI.
Host: Pierluigi Nuzzo
Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxwLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw
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 Variegated Study of 5G Services: Challenges, Opportunities, and Application Innovations
Thu, Mar 10, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Feng Qian, Associate Professor, Department of Computer Science and Engineering, University of Minnesota - Twin Cities
Talk Title: A Variegated Study of 5G Services: Challenges, Opportunities, and Application Innovations
Abstract: 5G is expected to support sub-millisecond latency as well as throughput of up to 20 Gbps -- a 100x improvement compared to 4G/LTE. However, there exists a vacuum in understanding how 5G performs "in the wild" and whether it can fulfill its promises. In this talk, I will describe our research thrust of 5G networks since early 2019, when Minneapolis became one of the first two U.S. cities that received commercial 5G deployment. Over the past 3 years, we have experimented with more than 100 TB of 5G data and traveled more than 8,000 km for drive tests. Our studies revealed a complete landscape of 5G across several key dimensions -- network performance, power characteristics, mobility management, application quality-of-experience (QoE), to name a few, with their critical tradeoffs quantitatively revealed. I will then talk about our development of a learning-based framework for accurate 5G performance prediction, and how we innovate emerging applications such as virtual/mixed reality (VR/MR) to improve their QoE on 5G networks.
Biography: As an experimental networking and system researcher, I design, engineer, deploy, evaluate real network systems, and make them yield real-world impact. I am particularly interested in mobile systems, AR/VR, mobile networking, wearable computing, real-world system measurements.
I received my Ph.D. from EECS at University of Michigan in 2012. I am honored to receive several awards including the AT&T Key Contributor Award (2014), NSF CRII Award (2016), Google Faculty Award (2016), ACM CoNEXT Best Paper Award (2016,2018), AT&T VURI Award (2017), NSF CAREER Award (2018), Trustees Teaching Award (2018), DASH-IF Excellence Award (2019), Cisco Research Award (2021), and ACM SIGCOMM Best Student Paper Award (2021). Some of my research prototypes such as mobile Application Resource Optimizer (ARO) have been commercialized and are widely used in academia and industry.
Host: Dr. Konstantinos Psounis, kpsounis@usc.edu
Webcast: https://usc.zoom.us/j/93770414634?pwd=SlBFL0JwL3QwR0RjK1p5bVMyM3duQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/93770414634?pwd=SlBFL0JwL3QwR0RjK1p5bVMyM3duQT09
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: Optics, Sensors & AI: Next-Generation Computational Imaging
Fri, Mar 11, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Vivek Boominathan, Postdoctoral Research Associate, Department of Electrical and Computer Engineering, Rice University
Talk Title: Optics, Sensors & AI: Next-Generation Computational Imaging
Abstract: Rapidly growing machine learning techniques such as deep learning have produced powerful computer vision algorithms. However, these algorithms usually apply to images and videos captured with traditional camera designs that have been principally unchanged for decades. Furthermore, real-world applications such as robotics, autonomous navigation, augmented/virtual reality, human-computer interaction, biomedical, and IoT need systems that adhere to fundamental constraints such as size, weight, power, and privacy. These fundamental constraints cannot be addressed by a software-only solution but demand a joint hardware-software solution. In my talk, I will present end-to-end computational imaging systems that execute "computation" at all stages of a physical vision system, from optics to sensors to algorithms. Novel optics such as diffractive and metamaterial optics provide new dimensions of light manipulation, while novel sensors such as SPADs offer new dimensions in light transduction. I will highlight algorithms and AI to explore these new dimensions and accessible nanofabrication techniques to realize novel optics and sensors. I will show applications from photographic 3D imaging to in vivo 3D imaging, achieved using compact coded aperture systems and ultraminiature lensless imaging systems. I will conclude by describing how my works set the stage for designing next-generation imaging systems for various future applications such as biomedical imaging, robotics, IoT, and human-computer interaction.
Biography: Dr. Vivek Boominathan is a postdoctoral research associate in the Department of Electrical and Computer Engineering at Rice University. He received his Ph.D. in 2019, advised by Prof. Ashok Veeraraghavan, and co-advised by Prof. Jacob Robinson and Prof. Richard Baraniuk. His research interests lie at the intersection of computer vision, machine learning, applied optics, and nanofabrication. His contributions have appeared in a broad spectrum of venues such as Science Advances, Nature BME, IEEE journals, optics journals, vision conferences, and circuits conferences. He has also published a review article, in Optica, around his Ph.D. topic of lensless imaging. His work has been covered by news media such as EurekAlert, NPR, Phys.org, and NDTV India. He has co-organized a tutorial on Computational Imaging and Machine Learning in CVPR 2019 and has served as the publication co-chair for ICCP since 2020. More details can be found at https://vivekboominathan.com/.
Host: Dr. Shri Narayanan, shri@ee.usc.edu
Webcast: https://usc.zoom.us/j/96039656028?pwd=RnVxeGx3aEZ3RTNsTW5PajFWakN2Zz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/96039656028?pwd=RnVxeGx3aEZ3RTNsTW5PajFWakN2Zz09
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-EP Seminar - Quntao Zhuang, Friday, March 11th at 2pm in EEB 248
Fri, Mar 11, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Quntao Zhuang, University of Arizona
Talk Title: Quantum Information Processing: From Fundamentals to Applications
Abstract: Quantum physics has changed the way we understand nature, and also the way we process information. Starting from the fundamental questions raised a century ago, we have now entered an era of quantum engineering. In this talk, I will introduce our recent results on quantum sensing and communication. Quantum sensing utilizes quantum effects such as coherence, squeezing and entanglement to boost measurement sensitivity. I will summarize the paradigm of distributed quantum sensing, which utilizes multi-partite entanglement to boost the measurement of an arbitrary function of local network parameters, generalizing the famous Heisenberg limit of quantum sensing; distributed quantum sensing has a wide range of applications, including dark matter search in different platforms and quantum machine learning. Then, I will briefly present our recent results on quantum radar and quantum spectroscopy. Finally, I will introduce our works on quantum communication. Claude Shannon established the famous classical capacity of communication channels---the ultimate rate at which classical physics allows us to communicate. Quantum physics has made things more interesting. To begin with, I will introduce our recent works in breaking the Shannon capacity for the first time, by utilizing quantum entanglement; Next, I will briefly summarize works on quantum information transmission, including quantum transduction and quantum repeaters.
Biography: Quntao Zhuang is an assistant professor in ECE and Optical Sciences at University of Arizona. He joined university of Arizona in 2019 after a brief postdoc at University of California, Berkeley. He got his PHD in physics from MIT in 2018. He received the NSF CAREER award in 2022, DARPA Young Faculty Award and Craig M. Berge Dean's Fellow in 2020.
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. -
ECE Seminar: The Role of Machine Learning in Electronic Design Automation
Mon, Mar 14, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Vidya A. Chhabria, Ph.D. Candidate, Electrical and Computer Engineering Department, University of Minnesota
Talk Title: The Role of Machine Learning in Electronic Design Automation
Abstract: For several decades, advances in hardware, accelerated by Moore's law and enabled by electronic design automation (EDA) tools, have sustainably met the demands for high computation at low energy and cost. However, emerging applications demand computing power far beyond today's system capabilities. Rapid advances in high-performance computing address the problem by using accelerators for specialized tasks such as machine learning (ML), increasing design diversity and system complexity. With Moore's law running out of steam, EDA tools now play a crucial role in meeting these computational demands. EDA tools are challenged to build chips that not only compensate for slow down in scaling, but also provide high performance for both ML and non-ML applications, which use a variety of new architectural techniques and operate under stringent performance constraints. Conventional EDA tools involve computationally expensive analysis and optimizations and are suboptimal as they often tradeoff speed for accuracy. ML promises to address these challenges as it has found tremendous success in solving these problems in classification, detection, and design space exploration problems in several different applications.
In this talk, I will show how leveraging ML techniques can revolutionize EDA tools by addressing the existing challenges. In particular, the talk will focus on tools that aid designers in (i) delivering power inside the chip without significant losses to meet power demands and (ii) sending the heat outside the chip to avoid high temperatures. The first section of the talk will show how a fast ML inference brings down several hours of runtime to a few milliseconds on industry-scale designs for these tasks. The second section will demonstrate how ML enables high-quality solutions through rapid optimizations. A key challenge with the proposed ML-based methods is the limited availability of open-source data and benchmarks for training and evaluation. The third section will show how ML can generate synthetic training sets and benchmarks for evaluating novel EDA solutions to these tasks. I will conclude by presenting avenues for future research in ML and EDA.
Biography: Vidya A. Chhabria is a Ph.D. candidate in the Electrical and Computer Engineering department at the University of Minnesota. She received her B.E. in Electronics and Communication from M. S. Ramaiah Institute of Technology, India, in 2016, and her M.S. in Electrical Engineering from the University of Minnesota in 2018. Her research interests are in the areas of electronic design automation, IC design, and machine learning. She has interned at Qualcomm Technologies, Inc. in the summer of 2017 and NVIDIA Corporation during the summers of 2020 and 2021. She received the ICCAD Best Paper Award in 2021, the University of Minnesota Doctoral Dissertation Fellowship in 2021, Louise Dosdall Fellowship in 2020, and Cadence Women in Technology Scholarship in 2020.
Host: Dr. Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09
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: Data efficient high-dimensional machine learning
Wed, Mar 16, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kamyar Azizzadenesheli, Assistant Professor, Department of Computer Science, Purdue University
Talk Title: Data efficient high-dimensional machine learning
Abstract: Traditional deep neural networks are maps between finite dimension spaces, and hence, are not suitable for modeling phenomena such as those arising from the solution of partial differential equations (PDE). In the first part of the talk, I introduce a new deep learning paradigm, called neural operators, that learns operators which are maps between infinite dimension spaces. I show that neural operators are universal approximators of operators and demonstrate a series of empirical successes of neural operators in natural sciences.
In the second part, I talk about the intersection of control theory and reinforcement learning and establish data-efficient learning and decision-making methods for generic dynamical systems. I conclude the talk by presenting empirical successes of these principled methods.
Biography: Kamyar Azizzadenesheli is an assistant professor at Purdue University, department of computer science, since Fall 2020. Prior to his faculty position, he was at the California Institute of Technology (Caltech) as a Postdoctoral Scholar at the Department of Computing + Mathematical Sciences. Before his postdoctoral position, he was appointed as a special student researcher at Caltech, working with ML and Control researchers at the CMS department and the Center for Autonomous Systems and Technologies. He is also a former visiting student researcher at Caltech. Kamyar Azizzadenesheli is a former visiting student researcher at Stanford University, and researcher at Simons Institute, UC. Berkeley. In addition, he is a former guest researcher at INRIA France (SequeL team), as well as a visitor at Microsoft Research Lab, New England, and New York. He received his Ph.D. at the University of California, Irvine.
Host: Dr. Salman Avestimehr, avestime@usc.edu
Webcast: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09
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: Machine Learning for Precision Health: A Holistic Approach
Thu, Mar 17, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ahmed Alaa, Postdoctoral Associate, Broad Institute of MIT & Harvard, MIT CSAIL
Talk Title: Machine Learning for Precision Health: A Holistic Approach
Abstract: Machine learning (ML) methods, combined with large-scale electronic health databases, could enable a personalized approach to healthcare by improving patient-specific diagnosis, prognostic predictions, and treatment decisions. If successful, this approach would be transformative for clinical research and practice. In this talk, I will describe a holistic approach to ML for precision health that comprises a three-step procedure: (1) data characterization and understanding, (2) model development and (3) model deployment. Next, I will demonstrate one instantiation of this approach in the context of developing ML models for predicting patient-level response to therapies using observational data. I will focus on a multi-task learning model that uses Gaussian processes to estimate the causal effects of a treatment on individual patients and discuss its application in various disease areas. Finally, I will discuss exciting avenues for future work, including ML methods for learning from unannotated clinical data, generating synthetic data and integrating clinical knowledge into data-driven modeling.
Biography: Dr. Ahmed Alaa is a postdoctoral associate at Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard University. Previously, he was a joint postdoctoral scholar at Cambridge University, Cambridge Center for AI in Medicine and the University of California, Los Angeles (UCLA). He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he was also a recognized (visiting) Ph.D. student at Oxford University. His research focuses on developing machine learning (ML) methods that can leverage healthcare data to enable a patient-centric approach to medicine, whereby ML models can inform disease diagnosis, prognosis and treatment decisions based on the characteristics of individual patients. He is the recipient of the (school-wide) 2021 Edward K. Rice Outstanding Doctoral Student Award at UCLA.
Host: Dr. Ashutosh Nayyar, ashutosn@usc.edu
Webcast: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09
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-EP Seminar - Mehdi Kiani, Thursday, March 17 at 2pm in EEB 248
Thu, Mar 17, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mehdi Kiani, Pennsylvania State University
Talk Title: Wireless Hybrid Electrical-Acoustic Systems for Body-Machine Interface
Abstract: We have already witnessed significant efforts towards the research and development of neurotechnologies to radically enhance our understanding of the extremely complex central and peripheral nervous systems (CNS and PNS) by modulating and imaging their activities. These technologies can eventually be utilized in establishing body-machine interfaces (BMIs) with the CNS and PNS to offer effective, minimally invasive, and long-term solutions for neurological disorders and chronic disabilities such as spinal cord and brain injuries, stroke, Parkinson's disease, epilepsy, rheumatoid arthritis, and diabetes, to name a few. Despite all the developments over the past decade, closed-loop BMIs with minimally invasive high-spatiotemporal-resolution recording and stimulation capabilities from the large-scale distributed CNS/PNS circuits is still one of the grand challenges of the neuroscience research in the 21st century. In this talk, I will present our recent efforts (and future work) towards the development of advanced minimally invasive BMIs for modulating and sensing neural and electrophysiological activities with high spatiotemporal resolution at large scale. These BMIs are enabled by innovative integrated circuits, ultrasound, and wireless power/data (with different modalities such as ultrasound and magnetoelectric) technologies. I will particularly present two projects that leverage ultrasound beam focusing and steering with electronic beamforming to enable wireless implantable technologies for high-resolution, large-scale brain neuromodulation and gastric electrical-wave mapping.
Biography: Dr. Kiani received his Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology in 2014. He joined the faculty of the School of Electrical Engineering and Computer Science at the Pennsylvania State University in August 2014 where he is currently an Associate Professor. His research interests are in the multidisciplinary areas of analog, mixed-signal, and power-management integrated circuits; ultrasound; and wireless power/data transfer and energy harvesting for wireless implantable medical devices and neural interfaces. He was a recipient of the 2020 NSF CAREER Award. He is currently an Associate Editor of the IEEE Transactions on Biomedical Circuits and Systems and IEEE Transactions on Biomedical Engineering. He also serves as a Technical Program Committee member of the IEEE International Solid-State Circuits Conference (ISSCC) in the IMMD subcommittee.
Host: ECE-Electrophysics
More Information: Mehdi Kiani Flyer.pdf
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. -
ECE-EP Seminar - Najme Ebrahimi, Friday, March 18th at 10am in EEB 248
Fri, Mar 18, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Najme Ebrahimi, University of Florida
Talk Title: Next Generation Intelligent and Secured Wireless World: From IoT and Sensors to Wideband and Multi-band Scalable Circuit and System
Abstract: The future intelligent and secured wireless world needs connectivity at any time anywhere and under extreme conditions with over one trillion sensors and Internet-of-Things (IoT) devices connected to the network. To this end, the autonomous, and yet connected, wireless world is envisioned to provide sensing and high-data-rate communications, accurate localization and ranging, and resiliency. The major challenges to attain these goals are latency and energy efficiency requirements, that are largely affected by interference, multi-path, and channel fading. To tackle these challenges, wideband high frequency scalable arrays are desired to provide high data-rate communications and directional beams for interference cancellation. Furthermore, wideband/multiband circuits and systems are needed for accurate localization in the presence of severe multipath and fading in ultra-dense environments in IoT networks.
In this talk, firstly, I will present novel techniques to overcome the challenges for future wideband/multiband scalable transceiver arrays, including power-efficient local oscillator distribution and phase shifting, image selection architecture, and novel compact antenna-IC integration. I will then discuss our ongoing work towards the wideband/multiband signal generation and modulation for 6G and beyond as well as heterogonous integration of different technologies and modules for extending the Moore's law. Secondly, I will present multi-band circuit generation for IoT and sensor nodes to be employed in dense wireless networks. More specifically, I will present the first bidirectional circuitry for IoT transponder that reciprocally generates harmonics and subharmonics, covering two communication frequency bands interchangeably, which makes it a premier tool for localization and sensing protocols. I will also discuss future directions on advanced multi-band reconfigurable architecture for wireless sensors and IoTs compatible with network and physical layer protocols for security, communications, and localization.
Biography: Najme Ebrahimi is an Assistant Professor of Electrical and Computer Engineering at the University of Florida. Her research focuses on Mm-Wave/THz Scalable Array for high data rate communications and sensing as well as the security and connectivity of the next generation of distributed Internet-of-Things (IoT) networks. She was a post-doctoral research fellow at the University of Michigan- Ann Arbor from 2017 to 2020 under the departmental fellowship and earned her Ph.D. from the University of California, San Diego in June 2017. She was selected as a Rising Star by MIT EECS Rising Star program in 2019 and by ISSCC Rising Star program of the IEEE Solid-State Circuits Society in 2020. She is a member of the Microwave and Mm-Wave Integrated Circuits committee (MTT-14) and serves in the IMS2022 Technical Paper Review Committee (TPRC). She is the recipient of the 2021 DARPA Young Faculty Award (YFA).
Host: ECE-Electrophysics
More Information: Najme Ebrahimi Flyer.pdf
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. -
ECE-EP Seminar - Volker Sorger, Monday, March 21st @ 2pm in EEB 248
Mon, Mar 21, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Volker Sorger, George Washington University
Talk Title: Devices & ASICs for Machine Intelligence and Post-Quantum Cryptography
Abstract: The high demand for AI services in conjunction with a dramatic chip shortage along with technology leaps such as 5/6G networks, cybersecurity threats, and quantum algorithms have resurrected a R&D push for advanced devices, information processing, and computing capability. To address this demand and explore novel technology, unique opportunities exist, for example, given by algorithmic parallelism of mixed-signal non-van Neuman accelerators. Especially electronic-photonic ASIC compute paradigms hold promise to enable non-iterative O(1) runtime complexity, ps-short latency, and TOPS/W throughputs. This opens prospects for next-generation hardware both for AI cloud services but also for accelerating edge computing such as enabled by compact and efficient PIC-CMOS co-designs pushing the SWAP envelope. As both a professor and a co-founder of a venture, in this seminar I will share my latest insights on fundamental complexity scaling and algorithm-hardware homomorphism on the one hand, and device- circuit- and system-level demonstrations on the other. I will introduce a novel memristive photonic RAM capable of zero-static power consumption suitable for AI edge applications and highlight our photonic tensor core ASIC demonstration leveraging parallelism including a software stack. Beyond matrix-matrix multiplication acceleration, I will show our Convolution Theorem-based accelerator enabling 1000x1000 matrix-size convolutions at 100us latency, or about 10x faster than today's GPUs. At the device level I will share advanced optoelectronics and quantum matter including a 50Gbps ITO-based modulator being 1,000x more compact than Silicon PDK solutions, discuss strainoptronic detectors with high gain-bandwidth-product, a 100GHz fast VCSEL, and share a path for an electrically-driven quantum source. Finally, having solved the complex-signal convolution I will show a Montgomery Multiplier for a data-center RSA public-key cryptosystem, and conclude by highlighting our recent post-quantum secure-hash-algorithm (SHA) system accelerating blockchain operations. I will conclude with an R&D outlook for the next decade and share examples of my passion supporting values and programs on diversity & inclusion.
Biography: Volker J. Sorger is an Associate Professor in the Department of Electrical and Computer Engineering and the Director of the Institute on AI & Photonics, the Head of the Devices & Intelligent Systems Laboratory at the George Washington University. His research areas include devices & optoelectronics, AI/ML accelerators, mixed-signal ASICs, quantum matter & processors, and cryptography. For his work, Dr. Sorger received multiple awards including the Presidential PECASE Award, the AFOSR YIP Award, the Emil Wolf Prize, and the National Academy of Sciences award of the year. Dr. Sorger is an Associate editor for OPTICA, serves on the board of Chip, and was the former editor-in-chief of Nanophotonics. He is a Fellow of Optica (former OSA), a Fellow of SPIE, a Fellow of the German National Academic Foundation, and a Senior Member of IEEE. He is a co-founder of Optelligence Company.
Host: ECE-Electrophysics
More Information: Volker Sorger Flyer.pdf
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. -
ECE-EP Seminar - Dejan Markovic, Thursday, March 24th at 10am in EEB 248 & via Zoom
Thu, Mar 24, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dejan Markovic, UCLA
Talk Title: The Future of Computing and Neuromodulation
Abstract: This talk will discuss future technologies addressing unmet needs in science, medicine, and engineering. Data-driven attentive computing requires runtime flexible and efficient hardware and software. Simple hardware leads to complex software (e.g. FPGA) and simple software leads to complex hardware (e.g. CPU). Runtime reconfigurable arrays (RTRAs) balance hardware and software to enable spatial and temporal flexibility for dynamic or uncertain environments. RTRA features multi-program tenancy, multi-size compile, and priority handling for >100x compute capacity gains over FPGA, and within 5x of (inflexible) hardware accelerators, as shown on a blind signal classification use case. Medical implants also require efficiency and flexibility, with heavily constrained size, weight and power, for novel clinical research and therapeutic systems. Despite notable clinical successes (e.g. Parkinson's disease), limitations in existing devices prevent them from expanding to other indications such as mental health or Alzheimer's disease. I will discuss the Neuro-stack, a versatile closed-loop system, verified in human subject experiments, towards miniaturized neural duplex of the future. These applications also reveal opportunities in system-level design automation to address design productivity and system assembly challenges.
Biography: Dejan MarkoviÄ is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA). He is also affiliated with UCLA Bioengineering Department, Neuroengineering field. He completed the Ph.D. degree in 2006 at the University of California, Berkeley, for which he was awarded 2007 David J. Sakrison Memorial Prize. His current research is focused on implantable neuromodulation systems, domain-specific compute architectures, and design methodologies. Dr. MarkoviÄ co-founded Flex Logix Technologies, a semiconductor IP startup, in 2014, and helped build foundational technology of Ceribell, a medical device startup. He received an NSF CAREER Award in 2009. In 2010, he was a co-recipient of ISSCC Jack Raper Award for Outstanding Technology Directions. He also received 2014 ISSCC Lewis Winner Award for Outstanding Paper. Prof. Markovic is a Fellow of the IEEE.
Host: ECE-Electrophysics
More Information: Dejan Markovic Flyer.pdf
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. -
ECE Seminar: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains
Fri, Mar 25, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Mohammad Ali Maddah-Ali, Research Scientist, Department of Electrical Engineering, Stanford University
Talk Title: Distributed Systems: Rigorous Theoretical Foundations Unlock Promising Gains
Abstract: Over the last twenty years, we have witnessed several revolutionary technologies, from communication networks to learning platforms to blockchains, that have profoundly changed our daily lives. Often, these platforms are modeled, designed, and operated based on intuition and folk wisdom. In this talk, we challenge some of those common beliefs. We show that by meticulously elaborating the key performance bottlenecks from first principles, we can propose counterintuitive solutions grounded in rigorous analysis that unlock considerable scaling gains in several areas:
1) In wireless communications, the delay in acquiring channel information is a significant bottleneck in supporting multiple users at a time. Contrary to popular belief, we demonstrate that even completely outdated channel information can be used for interference management and enabling simultaneous communications, thus alleviating the bottleneck of channel training.
2) In content delivery networks, folk wisdom design is to maximize the likelihood of serving a request from the local cache (hit rate); thus, the performance is bottlenecked by the size of an individual cache. We propose a fundamentally new approach with a gain that scales with the sum of the cache sizes in the network, rather than an individual cache size.
3) In distributed learning, we demonstrate that training with combined data samples (i.e., erasure-coded samples), rather than raw samples, can significantly improve the reliability and convergence rate. Moreover, we highlight the surprising role of approximation theory in circumventing a major bottleneck in designing practical coded training procedures.
We conclude with promising directions for further investigation: in particular, the challenges in adding decentralized trust and accountability to these systems, to place control over them back in the hands of individuals rather than big corporations.
Biography: Mohammad Ali Maddah-Ali received the B.Sc. degree from the Isfahan University of Technology, the M.Sc. degree from the University of Tehran, and the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Waterloo, Canada. From 2008 to 2010, he was a Postdoctoral Fellow in the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley. From 2010 to 2020, he was working at Bell Labs, Holmdel, NJ, as a communication network research scientist. He also worked as a faculty member at the Department of Electrical Engineering, Sharif University of Technology. Currently, he is a research scientist at the Department of Electrical Engineering, Stanford University.
Dr. Maddah-Ali is a recipient of several awards including the IEEE International Conference on Communications (ICC) Best Paper Award in 2014, the IEEE Communications Society and IEEE Information Theory Society Joint Paper Award in 2015, and the IEEE Information Theory Society Paper Award in 2016. He is currently serving as an associate editor for the IEEE Transactions on Information Theory and a lead editor for The IEEE Journal on Selected Areas in Information Theory.
Host: Dr. Keith Chugg, chugg@usc.edu
Webcast: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/98149159985?pwd=cWFsVnRkZXRKcTlWYllMcy9Rempmdz09
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 Announcement: Accelerating Chip-Building Design Cycles for Future Generations of Computing
Mon, Mar 28, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Christopher Torng, Postdoctoral Researcher, Stanford University
Talk Title: Accelerating Chip-Building Design Cycles for Future Generations of Computing
Abstract: The chip building industry is a major cornerstone of the global economy. As a result, addressing the causes behind a multi-year global chip shortage is important for both near and long term futures. Unfortunately, one major challenge is that it is difficult to produce high-quality designs quickly and at low cost using traditional hardware design flows. This means that the industry wastes valuable fabrication slots learning painful design lessons rather than meeting economic demands.
My research focuses on building new architectures, systems, and design tools to accelerate chip building design cycles for future generations of computing systems. To support this goal, my research spans across the computing stack, ranging from applications, compilers, architectures, and down to chip implementation. In this talk, I will first present a set of vertically integrated techniques (compiler, architecture, and VLSI) that significantly reduces the design effort for extremely fine-grain power control in spatial architectures. Next, I will introduce my work on a new generation of open and agile hardware flow tools that leverage modern programming language features to increase code reuse in physical design. Finally, I will discuss recent work on Amber SoC, a coarse-grained reconfigurable array designed with an end-to-end agile accelerator-compiler co-designed flow. I will conclude with my future directions in supporting chip building for the next generation of computing.
Biography: Christopher Torng is a postdoctoral researcher at Stanford University. He received his Ph.D. degree, M.S. degree, and B.S degree (2019, 2016, 2012) in Electrical and Computer Engineering from Cornell University. His projects target the development of architectures and tools to accelerate building chips and complex hardware systems. His tools have achieved use across multiple universities to support over ten academic tapeouts in technologies ranging from 180nm to 16nm. His activities have resulted in a selection as a Rising Star in Computer Architecture (2018) by Georgia Tech and an IEEE MICRO Top Pick from Hot Chips (2018).
Host: Dr. Peter Beerel, pabeerel@usc.edu
Webcast: https://usc.zoom.us/j/99531222900?pwd=S1VDR2pRU2lyZ2hORmtObE1PcFh6Zz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/99531222900?pwd=S1VDR2pRU2lyZ2hORmtObE1PcFh6Zz09
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. -
Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Mar 30, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nir Piterman, Department of Computer Science and Engineering, University of Gothenburg, Sweden
Talk Title: Synthesis From Temporal Specifications
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In this talk I will present the GR[1] approach to synthesis, the automatic production of designs from their temporal logic specifications. We are interested in reactive systems, systems that continuously interact with other programs, users, or their environment and specifications in linear temporal logic. Classical solutions to synthesis use either two player games or tree automata. I will give a short introduction to the technique of using two player games for synthesis.
The classical solution to synthesis requires the usage of deterministic automata. This solution is 2EXPTIME-complete, is quite complicated, and does not work well in practice. I will present a syntactic approach that restricts the kind of properties users are allowed to write. It turns out that this approach is general enough and can be extended to cover many properties written in practice.
Time permitting, I will present results that support the usage of synthesis in model-driven development and robot control.
Biography: Nir Piterman is a professor of computer science at the University of Gothenburg in Sweden. Before that he was an associate professor at the University of Leicester, held postdoctoral research positions at Imperial College London and the Ecole Polytechnique Federal de Lausanne, and completed his PhD at the Weizmann Institute of Science. His research interests include formal verification and automata theory. Particularly, he has worked on model checking, temporal logic, reactive synthesis, and game solving. His current research is funded by the European Research Council (ERC), the Swedish Research Council (VR), and the Wallenberg Autonomous Systems Program (WASP(. He is currently the editor in chief of the journal Formal Methods in System Design.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Location: Online
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.
