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Events for April
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ECE Seminar: Ultra-High-Throughput Computational Imaging: Towards A Trillion Voxels Per Second
Mon, Apr 01, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kevin C. Zhou, Postdoctoral Scholar | Schmidt Science Fellow | Department of EECS | UC Berkeley
Talk Title: Ultra-High-Throughput Computational Imaging: Towards A Trillion Voxels Per Second
Abstract: Traditional biomedical imaging techniques face throughput bottlenecks that limit our ability to study complex dynamic samples like cells, organoids, tissues, and organisms. In particular, hardware-only systems have inherent physical limitations preventing the simultaneous improvement of resolution, field of view, and frame rate. In this seminar, I propose that large-scale, machine learning-accelerated computational imaging will be the key to overcoming these throughput bottlenecks. I demonstrate a variety of examples from my research, ranging from resolution-enhanced, speckle-free tissue imaging with optical coherence refraction tomography, to camera array-based gigapixel microscopy and 4D fluorescence tomography of freely-behaving zebrafish and fruit flies. Critical to the computational scalability is the integration of physics-supervised deep learning into my reconstruction algorithms. Combined with scalable hardware designs, these high-performance computational imaging systems will continue the trend of my research towards ultra-high imaging throughputs, even approaching 1 trillion voxels per second, which will accelerate scientific discovery, big data generation, and tool development across a broad range of biomedical applications.
Biography: Kevin C. Zhou is a Schmidt Science Fellow and postdoctoral scholar at UC Berkeley, developing high-throughput computational imaging systems with Laura Waller and Hillel Adesnik. Before that, he received his PhD in biomedical engineering at Duke University, where he worked with Joseph Izatt, Warren Warren, Sina Farsiu, and Roarke Horstmeyer, and was supported by the NSF GRFP. He received his BS in biomedical engineering at Yale University, where he was supported by the Barry M. Goldwater Scholarship. Kevin's interdisciplinary research focuses on developing both the optical instrumentation and machine learning-driven algorithms for scalable, high-throughput computational optical imaging systems to advance discovery in biology and medicine.
Host: Dr. Justin Haldar, jhaldar@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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CSC/CommNetS-MHI Seminar: Prashant Mehta
Mon, Apr 01, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Prashant Mehta, Professor, Coordinated Science Laboratory | Department of Mechanical Science and Engineering | University of Illinois at Urbana-Champaign
Talk Title: Variational principles in control and the arrow of time
Series: CSC/CommNetS-MHI Seminar Series
Abstract: There is a certain magic in writing the variational form of the equations in physics and engineering. The most magical of these is Lagrange’s formulation of the Newtonian mechanics. An accessible modern take on this and more appears in the Feb 2019 Issue of The New YorkerI describe a new variational (optimal control-type) formulation of the nonlinear filtering problem, an important feature of which is that the arrow of time reverses. The reversal of time brings about all sorts of paradoxes involving causality. Scenes from Christopher Nolan's sci-fi movie Tenet may be shown for entertainment and educational purposes.
Apart from movie snippets, the talk will also include technical content. Specifically, I argue that certain foundational aspects of Control Theory – duality between estimation and control – are less than well- understood for nonlinear stochastic systems (hidden Markov models), in part because of the issue of time reversal. Based on the optimal control formulation, I will also discuss some new results on the asymptotic stability of the nonlinear filter.
Biography:
Prashant Mehta is a Professor in the Coordinated Science Laboratory (CSL) and the Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign (UIUC). He received his Ph.D. in Applied Mathematics from Cornell University in 2004. He was the co-founder and the Chief Science Officer of the startup Rithmio whose gesture recognition technology was acquired by Bosch Sensortec in 2017. Prior to his academic appointment at UIUC in 2005, he worked at United Technologies Research Center (UTRC) where he co-invented the symmetry-breaking solution to
suppress combustion instabilities. This solution — which helped solve a sixty-year old open problem — has since become an industry standard and is widely deployed in jet engines and afterburners sold by Pratt and Whitney.Prashant Mehta received the Outstanding Achievement Award at UTRC for his contributions to modeling and control of combustion instabilities in jet-engines. His students have received the Best Student Paper Awards at the IEEE Conference on Decision and Control in 2007, 2009, and most recently in 2019; and have been finalists for these awards in 2010 and 2012. He serves as a member of the IEEE Control Systems Society (CSS) Awards Board and as an Associate Editor for the IEEE Transactions on Automatic Control (2019-present). He is a Fellow of IEEE.
Host: Dr. Ketan Savla
More Info: https://csc.usc.edu/seminars/2024Spring/mehta.html
More Information: 2024.04.01 CSC Seminar - Prashant Mehta.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/mehta.html
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ECE-S Seminar - Francisco Romero
Tue, Apr 02, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Francisco Romero, PhD, Electrical Engineering | Stanford University
Talk Title: General Purpose and Interactive Video Analytics
Abstract: The availability of vast video datasets and the increasing accuracy of machine learning models have made exploration of video data an exciting opportunity. Asking complex questions like “Find cases where a car takes a left turn while a pedestrian is crossing the road on a rainy night” over terabytes of videos should be possible. Recent video analytics research expects users will manually reason about their query, combine optimizations, and occasionally train models to meet their performance and accuracy goals. This is a long way from the experience users have when exploring structured data. In this talk, I will present the design of a general purpose and interactive video analytics system. First, I will present how to automatically optimize multi-model, multi-predicate video queries with the VIVA video analytics system. VIVA allows users to express domain knowledge about model relationships. VIVA uses this knowledge to automate complex query optimization by deciding how and when it should be applied. Second, I will present how to efficiently execute video queries across heterogeneous hardware resources with INFaaS. INFaaS exposes a "model-less" interface that enables users to simply specify the performance and accuracy requirements for their applications without needing to specify a specific model-variant for each query. INFaaS efficiently navigates the large trade-off space of model-variants on behalf of users to meet application-specific objectives: (a) for each query, it selects a model, hardware architecture, and model optimizations, (b) it combines VM-level horizontal autoscaling with model-level autoscaling to reduce cost as query load varies. I will also briefly discuss how I extended INFaaS across DAGs of machine learning models with Llama: a serverless video processing framework. I will close by outlining future directions in multi-modal data analysis across heterogeneous hardware resources.
Biography: Francisco Romero works at the intersection of computer systems and architecture, databases, and machine learning, where his goal is to design systems that automatically make decisions on users’ behalf to optimize for their goals like cost, performance, accuracy, and resource efficiency. He recently received his PhD in Electrical Engineering at Stanford University, where his research spanned general machine learning inference, serverless computing, data systems, and datacenter scheduling. He has several publications in top-tier conferences, including a best paper at USENIX ATC 2021. His work has been deployed in production Microsoft Azure Functions and is being used for automated video analysis at a stealth company.
Host: Dr. Murali Annavaram, annavara@usc.edu
Webcast: https://usc.zoom.us/j/92881411147?pwd=SXNBdm9oa3ljYi9sdTNsR2puWmRrQT09More Information: 2024.04.02 ECE Seminar - Francisco Romero.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/92881411147?pwd=SXNBdm9oa3ljYi9sdTNsR2puWmRrQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Photonics Seminar - A. Douglas Stone, Tuesday, April 2nd at 2pm in EEB 248
Tue, Apr 02, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: A. Douglas Stone, Yale University
Talk Title: Time-reversing a laser: What it means and why it's important.
Series: Photonics Seminar Series
Abstract: Over a decade ago an overlooked symmetry of Maxwell's equations coupled to matter was recognized, a relationship between a laser at threshold and a perfectly absorbing resonator. The threshold condition for lasing is the point at which gain balances loss, and the system self-organizes to oscillate coherently at a specific frequency in the highest Q electromagnetic mode. At this special point the system supports a purely outgoing solution of the Maxwell wave equation at a real frequency but with negligible amplitude, heralding the turn-on of a steady-state source of coherent radiation. Time-reversing this threshold lasing equation maps the laser system to another physical realizable electromagnetic system, one in which the time-reflected lasing mode is incident on an identical resonator, except that absorption loss replaces gain, and the purely incoming wave is perfectly trapped by interference and eventually absorbed without scattering. This mapping implies that under very general conditions, any complex structure can be made to absorb perfectly at a specific frequency, if a specific adapted input wavefront is imposed and the loss is appropriately tuned, a phenomenon now known as Coherent Perfect Absorption (CPA). While CPA was proposed for classical electromagnetic waves, the effect occurs for all of the linear classical wave equations of physics, and has nonlinear generalizations as well. Moreover, while CPA describes perfect capture and transduction of waves, the theory pointed the way to an even more general theory of reflectionless scattering of appropriate adapted wavefronts ("reflectionless scattering modes", RSMs). This theory applies to quantum waves as well, and provides a new framework to explore the control and routing of waves via interference in guided and even open geometries. I will review a few dramatic experimental and technologically interesting applications of CPA and RSM.
Biography: A. Douglas Stone is Carl A. Morse Professor of Applied Physics, and Professor of Physics at Yale University, where he joined the faculty in 1986. Since becoming a full professor in 1990, he has served as Chair of Applied Physics (1997-2003, 2009-2015), Director of Yale's Division of Physical Sciences (2004-2009), and Deputy Director of the Yale Quantum Institute (2015-present).Stone is a theoretical physicist with research interests in condensed matter and optical physics. He has co-authored over 165 research publications, which have been cited over 28,000 times, with an h-index of 74 and holds four patents for optical devices. He was a pioneer in the field of mesoscopic physics, describing systems intermediate between bulk solids and individual atoms or molecules, where novel quantum effects appear. Subsequently he worked on problems relating to the effects of chaos in quantum and electromagnetic systems, and was the first to introduce and study lasers with ray-chaotic resonators. His current work continues to focus on lasers, and other photonic systems with complex geometry and gain and loss. He is a recipient of the McMillan Award of the University of Illinois at Urbana for "outstanding contributions to condensed matter physics" for his research demonstrating "universal conductance fluctuations" in mesoscopic conductors. He was awarded the 2015 Willis Lamb Medal for Laser Science for his work on random and chaotic lasers, in collaboration with his colleague Hui Cao. His group developed Steady-state Ab initio Laser Theory (SALT), which is the first general formulation of laser theory set up to deal with arbitrary spatial complexity in a lasing structure efficiently, assuming steady-state operation. In 2010 he pioneered the concept of the Coherent Perfect Absorber (the time-reversed or "anti-laser"), and has recently generalized this framework to encompass a general theory reflectionless scattering of all linear waves. He is a Fellow of the American Physical Society and of the Optical Society of America, and is an Honorary General Member of the Aspen Center for Physics. Stone earned his BA from Harvard in 1976, an MA from Balliol College, Oxford in 1978 (where he was a Rhodes Scholar), and a PhD from MIT in 1983 under the supervision of John Joannopoulos. He was a postdoc at IBM before coming to Yale.
Host: Mercedeh Khajavikhan, Michelle Povinelli, Constantine Sideris; Hossein Hashemi; Wade Hsu; Mengjie Yu; Wei Wu; Tony Levi; Alan E. Willner; Andrea Martin Armani
More Information: Douglas Stone Seminar Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ECE-S Seminar - Dr. Stuart Oberman
Wed, Apr 03, 2024 @ 05:00 PM - 06:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Stuart Oberman, Vice President, GPU ASIC Engineering | NVIDIA
Talk Title: GPU Computing and the Rise of Generative AI
Abstract:
Generative AI is transforming industries, with its powerful ability to create text, images, videos, computer code, and more. The advent and growth of GenAI have been driven by the evolution of GPU computing. Innovations in NVIDIA's GPU architectures over the last two decades have transformed GPUs from 3D graphics accelerators to also powerful AI accelerators. This talk will present this GPU computing journey of hardware and architectural advances, and it will discuss current and future technology challenges and opportunities. It will also discuss strategies for deploying GenAI networks in large GPU datacenters, where hardware and software advancements are combined to meet the real-time requirements of various industries.
Biography:
Stuart Oberman is Vice President of GPU ASIC Engineering at NVIDIA. Since 2002, he has contributed to the design and verification of 12 GPU architectures. He currently directs multiple GPU design and verification teams. Stuart earned the BS degree in electrical engineering from the University of Iowa, and the MS and PhD degrees in electrical engineering from Stanford University, where he performed research in the Stanford Architecture and Arithmetic Group. He has coauthored one book and more than 20 technical papers. He holds more than 55 granted US patents.
Zoom Meeting ID: 955 2860 0978
Passcode: 988471
Host: Dr. Arash Saifhashemi
More Information: 2024.04.03 ECE Seminar - Stuart Oberman.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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ECE-EP Faculty Candidate - Liran Zheng, Thursday, April 4th at 3pm in EEB 248
Thu, Apr 04, 2024 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Liran Zheng, Tesla
Talk Title: Power Electronics for a Net-Zero Energy Future
Series: ECE-EP Seminar
Abstract: One of the most significant challenges that human society faces is producing and distributing clean and affordable energy. Electricity and transportation sectors are dominant sectors for US greenhouse gas emissions and account for 25% and 28% of total emissions in 2021, respectively. To enable a net-zero energy future, renewable energy and energy storage need to be integrated into smart power grids. A paradigm shift from fossil fuel to clean electricity as the energy source of land, sea, and air transportation is also necessary. Power electronics serve as the electronic interfaces between the smart power grids and resources including but not limited to renewable energy, energy storage, and electrified transportation. Recently, the advancement in wide-bandgap semiconductors ignited significant interests in emerging medium-voltage (MV) power electronics, especially solid-state transformers (SSTs). I will discuss my Ph.D. work on new current-source single-stage SST circuits and model-predictive priority-shifting control methods for new stacked low-inertia SSTs. The advantages include significantly reduced size, improved efficiency and reliability, and universality for different net-zero applications. Based on the proposed concepts and customized 3.3 kV silicon carbide reverse-blocking MOSFET modules, a 5 kV DC SST and a 7.2 kV AC SST have been built and tested for MV DC renewable-energy collector and MV AC electric vehicle fast-charging applications, respectively. It is the first time that current-source MV SSTs have been demonstrated and reported, which led to an IEEE Transactions on Power Electronics First Prize Paper Award. My patents from this work have been licensed under the GridBlock startup company for grid-connected transportation electrification and renewable energy products. Finally, I will discuss future research directions for a net-zero energy future.
Biography: Liran Zheng received the B.S. degree in control engineering from Tsinghua University in 2016, and the M.S. and Ph.D. degrees with the Center for Distributed Energy in electrical and computer engineering from Georgia Institute of Technology in 2018 and 2022, respectively.Liran is currently a Senior Engineer with Tesla. He previously held visiting positions with The University of Texas at Austin, the NSF ERC Center for Power Electronics Systems at Virginia Tech, the General Electric Global Research Center, and the Electric Power Research Institute. His research interests include power electronics and energy systems. Liran is the recipient of 4 IEEE Prize Paper Awards including an IEEE Transactions on Power Electronics First Prize Paper Award and Georgia Tech Best Ph.D. Thesis Award. He holds patents commercialized by GridBlock, a startup company out of Georgia Tech, for grid-connected transportation electrification and renewable energy products. He serves as an Associate Editor for the IEEE Transactions on Industry Applications.
Host: ECE-EP
More Information: Liran Zheng Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Munushian Seminar - Kwabena Boahen, Friday, April 5th at 3:30pm in EEB 132
Fri, Apr 05, 2024 @ 03:30 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kwabena Boahen, Stanford University
Talk Title: Scaling Knowledge Processing: 2D Chips versus 3D Brains
Series: Munushian Visiting Seminar Series
Abstract: Artificial intelligence (AI) now advances by performing twice as many multiplications every two months, but the semiconductor industry tiles twice as many multipliers on a chip every two years. Moreover, the returns from tiling these multipliers ever more densely in two dimensions (2D) now diminish because signals must travel relatively farther and farther. Although travel can be shortened by stacking multipliers to process knowledge in three dimensions (3D), such a solution acutely reduces the available surface area for dissipating heat. My recent dendrocentric reconception of the biological brain's fundamental units of computation and communication removes this 3D thermal roadblock. Current AI uses dot-products to emulate synaptic weighting. This six-decade-old synaptocentric conception posits that the brain weights inputs across an entire dendrite to detect a spatial pattern of activations. The dendrocentric conception posits that the brain orders inputs meticulously along a short stretch of dendrite to detect a spatiotemporal pattern of spikes. My group has now realized this dendrocentric conception of the learning brain with a string of ferroelectric transistors. Moving away from synaptocentric to dendrocentric learning would enable AI to run not with megawatts in the cloud but rather with watts on a phone.
Biography: Kwabena Boahen is a Professor of Bioengineering, Electrical Engineering, and by courtesy Computer Science at Stanford University; an investigator in Stanford's Bio-X Institute, System X Alliance, and Wu Tsai Neurosciences Institute; and the founding director of Stanford's Brains in Silicon Lab. His group models the nervous system computationally to elucidate principles of neural design at the cellular, circuit, and systems levels; and synthesizes neuromorphic electronic systems that scale energy-use with size as efficiently as the brain does. His interest in neural networks developed soon after he left his native Ghana to pursue undergraduate studies in Electrical and Computer Engineering at Johns Hopkins University, Baltimore, in 1985. He went on to earn a doctorate in Computation and Neural Systems at the California Institute of Technology in 1997. From 1997 to 2005 he was on the faculty of University of Pennsylvania, Philadelphia PA, where he was the inaugural holder of the Skirkanich Term Junior Chair. His research has resulted in over a hundred publications, including a cover story in Scientific American featuring his lab's work on a silicon retina and a silicon tectum that "wire together" automatically (May 2005). He has been invited to give over a hundred seminar, plenary, and keynote talks, including a 2007 TED talk, "A computer that works like the brain", with over seven hundred thousand views. He has received several distinguished honors, including a Packard Fellowship for Science and Engineering (1999) and a National Institutes of Health Director's Pioneer Award (2006). He was elected a fellow of the American Institute for Medical and Biological Engineering (2016) and of the Institute of Electrical and Electronic Engineers (2016) in recognition of his lab's work on Neurogrid, an iPad-size platform that emulates the cerebral cortex in biophysical detail and at functional scale, a combination that hitherto required a supercomputer. He has led several multi-university, multi-investigator research efforts, including one that raised the level of abstraction at which neuromorphic chips are 'programmed' by co-designing hardware and software (Brainstorm Project). A spin-out from his Stanford lab, Femtosense Inc (2018), is commercializing this breakthrough. He teaches graduate courses in computational neuroscience and neuromorphic computing, has trained over twenty graduate students, and mentored four postdoctoral researchers, including the designer of NeuraLink's first implantable chip.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Kwabena Boahen Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ECE Seminar: White-Box Computational Imaging: Measurements to Images to Insights
Tue, Apr 09, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Sara Fridovich-Keil, Postdoctoral Fellow | Department of Electrical Engineering | Stanford University
Talk Title: White-Box Computational Imaging: Measurements to Images to Insights
Abstract: Computation and machine learning hold tremendous potential to improve the quality and capabilities of imaging methods used across science, medicine, engineering, and art. Despite their impressive performance on benchmark datasets, however, deep learning methods are known to behave unpredictably on some real-world data, which limits their trusted adoption in safety-critical domains. Accordingly, in this talk I will describe white-box, interpretable methods for photorealistic volumetric reconstruction that match or exceed the performance of black-box neural alternatives. I will also present recent theoretical results that guarantee correct and efficient reconstruction using our white-box approach in nonlinear computed tomography.
Biography: Sara Fridovich-Keil is a postdoctoral fellow at Stanford University, where she works with Mert Pilanci and Gordon Wetzstein on foundations and applications of machine learning and signal processing in computational imaging. She is currently supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship. Sara received her PhD in electrical engineering and computer sciences in May 2023 from UC Berkeley, where she was advised by Ben Recht and supported by an NSF GRFP fellowship. Sara received her BSE in electrical engineering from Princeton University in 2018, where she was advised by Peter Ramadge and supported, in part, by a Barry Goldwater Scholarship.
Host: Drs. Antonio Ortega (aortega@usc.edu) and Mihailo Jovanovic (mihailo@usc.edu)
Webcast: https://usc.zoom.us/j/93797502146?pwd=UzZWRjJieFdIMVU2b3VlckFtQUd3QT09 - (USC NetID Login Required)Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/93797502146?pwd=UzZWRjJieFdIMVU2b3VlckFtQUd3QT09 - (USC NetID Login Required)
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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AAI-CCI-MHI Seminar on CPS, Wed., April. 10, 2:00 pm, EEB 248: Gioele Zardini
Wed, Apr 10, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Gioele Zardini, Postdoctoral Researcher
Talk Title: Co-Design of Complex Systems: From Autonomy to Future Mobility
Series: EE598 Seminar Series
Abstract: When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, while insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles as part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
Biography: Gioele Zardini is a Postdoctoral Scholar in the Department of Aeronautics and Astronautics at Stanford University and an incoming faculty at MIT in Fall 2024.
He received his BSc., MSc., and Ph.D. in Mechanical Engineering with a focus on Robotics, Systems, and Control from ETH Zurich in 2017, 2019, and 2023 respectively. He spent time in Singapore as a researcher at nuTonomy (then Aptiv, now Motional), at Stanford University (working with Marco Pavone), and at MIT (in 2020 working with David Spivak, and in 2023 with Munther Dahleh).
Driven by societal challenges, the goal of his research is to develop efficient computational tools and algorithmic approaches to formulate and solve complex, interconnected system design and autonomous decision-making problems. His research interests include the co-design of sociotechnical systems, compositionality in engineering, applied category theory, decision and control, optimization, and game theory, with applications to intelligent transportation systems, autonomy, and complex networks and infrastructures. He is the creator of Autonomy Talks (an International seminar series promoting a diverse research exchange on autonomy), as well as a lead organizer for the seminal workshops “Compositional Robotics: Mathematics and Tools”, and “Co-Design and Coordination of Future Mobility Systems” at IEEE ICRA and ITSC, respectively. He is the recipient of a paper award at the 4th Applied Category Theory Conference and of the Best Paper Award (1st Place) at the 24th IEEE International Conference on Intelligent Transportation Systems (ITSC). For more details, check out his webpage: https://gioele.science
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Ariana Perez
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AAI-CCI-MHI Seminar on CPS, Wed., April. 10, 2:00 pm, EEB 248: Gioele Zardini
Wed, Apr 10, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Gioele Zardini, Postdoctoral Researcher
Talk Title: Co-Design of Complex Systems: From Autonomy to Future Mobility
Abstract: When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, while insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles as part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
Biography: Gioele Zardini is a Postdoctoral Scholar in the Department of Aeronautics and Astronautics at Stanford University and an incoming faculty at MIT in Fall 2024. He received his BSc., MSc., and Ph.D. in Mechanical Engineering with a focus on Robotics, Systems, and Control from ETH Zurich in 2017, 2019, and 2023 respectively. He spent time in Singapore as a researcher at nuTonomy (then Aptiv, now Motional), at Stanford University (working with Marco Pavone), and at MIT (in 2020 working with David Spivak, and in 2023 with Munther Dahleh). Driven by societal challenges, the goal of his research is to develop efficient computational tools and algorithmic approaches to formulate and solve complex, interconnected system design and autonomous decision-making problems. His research interests include the co-design of sociotechnical systems, compositionality in engineering, applied category theory, decision and control, optimization, and game theory, with applications to intelligent transportation systems, autonomy, and complex networks and infrastructures. He is the creator of Autonomy Talks (an International seminar series promoting a diverse research exchange on autonomy), as well as a lead organizer for the seminal workshops “Compositional Robotics: Mathematics and Tools”, and “Co-Design and Coordination of Future Mobility Systems” at IEEE ICRA and ITSC, respectively. He is the recipient of a paper award at the 4th Applied Category Theory Conference and of the Best Paper Award (1st Place) at the 24th IEEE International Conference on Intelligent Transportation Systems (ITSC). For more details, check out his webpage: https://gioele.science
Host: Pierluigi Nuzzo
Location: 248
Audiences: Everyone Is Invited
Contact: Ariana Perez
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ECE-S Seminar - Dr. Raghavendra Pothukuchi
Thu, Apr 11, 2024 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Raghavendra Pothukuchi, Associate Research Scientist, Department of Computer Science, Yale University
Talk Title: Building the Infinite Brain
Abstract: Interfacing brains and computers helps advance our understanding of the brain and mind, treat their disorders, and when combined with artificial cognitive frameworks, can push the frontier of human ability. Realizing this goal requires new computer architectures—both close to the brain to process and stimulate neural activity, and far from the brain, to run more complex cognitive frameworks, working together. In this talk, I will present my research on the first distributed brain-computer interfacing architecture, SCALO, which processes neural activity from multiple regions of the brain in real time, while being safe for implantation. SCALO is a template for distributed multi-accelerator systems that must meet extreme design constraints. I will also describe the research into accelerating computationally hard models of human cognition, how this might require leveraging novel accelerators like quantum computers, and outline an end-to-end design connecting these with brain interfaces. Meeting the challenging constraints of brain interfacing and complex cognitive modeling required novel system design, leading to fundamental contributions to computer architecture, and sparking a virtuous cycle of innovation between computer architecture and the brain sciences.
Biography: Raghavendra (Raghav) Pothukuchi is an Associate Research Scientist at Yale University. He is an NSF/CRA Computing Innovation Fellow with Profs. Abhishek Bhattacharjee and Jonathan D. Cohen (Princeton, neuroscience). He received his Ph. D. in Computer Science (CS) from the University of Illinois at Urbana-Champaign (UIUC) with Prof. Josep Torrellas. His research is on brain-computer interfaces, quantum and classical frameworks to accelerate cognitive models, and biologically inspired computer architectures. He also has interdisciplinary work on building intelligent and secure computer systems using control theory and machine learning. Raghav has been selected as a young researcher at the Heidelberg Laureate Forum, rising star in computer architecture, and his work has been recognized with a best paper award at ISCA, two IEEE Micro Top Picks selection, a best paper nomination at PACT, and other honors.
Host: Dr. Massoud Pedram
More Info: https://usc.zoom.us/j/98497384750?pwd=TE5kS1JBWklIcFIwYjdkeThUYkcrQT09
Webcast: https://usc.zoom.us/j/98497384750?pwd=TE5kS1JBWklIcFIwYjdkeThUYkcrQT09More Information: 2024.04.11 ECE Seminar - Raghavendra Pothukuchi.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/98497384750?pwd=TE5kS1JBWklIcFIwYjdkeThUYkcrQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://usc.zoom.us/j/98497384750?pwd=TE5kS1JBWklIcFIwYjdkeThUYkcrQT09
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ECE Seminar: Computational Imaging with Photon Streams
Mon, Apr 15, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Sotiris Nousias, Postdoctoral Fellow | Department of Computer Science | University of Toronto
Talk Title: Computational Imaging with Photon Streams
Abstract: Among the yearly iterations on smartphone cameras and advances in sensor design, a key question emerges: what does the future's ultimate camera look like? In this presentation, I will explore the advanced capabilities of single-photon cameras, which have evolved from specialized scientific tools to being integral components in consumer devices like the iPhone. These cameras are not just sensitive; they are exceptionally precise, capable of detecting individual photons and their arrival times to the trillionth of a second. Traditionally, research in this field has focused on simple methods like counting or creating histograms of photons. My work, however, focuses on the raw 'photon stream' output – a sequence of photon arrival times that offers a rich, yet underexplored, source of data. By developing innovative mathematical models and algorithms, I open up exciting new possibilities, such as capturing high-speed videos at standard (30 Hz) and extremely high frame rates (>200 GHz), unveiling hidden 3D structures using multiply scattered light, and achieving micrometer-scale 3D imaging. I will discuss the remarkable capabilities of single-photon cameras and their potential to revolutionize various fields.
Biography: Sotiris Nousias is a Postdoctoral Fellow at the University of Toronto, advised by Kyros Kutulakos. His research interests lie in the fields of computational imaging, computer vision, and signal processing, with a focus on single-photon imaging. His aim is to push the boundaries of imaging, developing novel systems and algorithms that can reveal the world in entirely new ways. He earned his PhD from University College London, advised by Christos Bergeles. His research has been recognized with best paper awards at two of the leading computer vision conferences: CVPR in 2019 and ICCV in 2023.
Host: Dr. Justin Haldar, jhaldar@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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ECE-S Seminar - Dr. Zili Meng
Mon, Apr 15, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Zili Meng, Assistant Professor, Hong Kong University of Science and Technology (HKUST)
Talk Title: Transport Layer Innovations in Ultra-Low Latency Video Streaming
Abstract: In the post-pandemic era, ultra-low latency video streaming is increasingly popular nowadays. Numerous applications like videoconferencing, cloud gaming, virtual reality, remote driving are coming or have come to our daily life. However, these interactive video streaming applications require ultra-low and consistent latency to ensure the interactive experience, which challenges how the sender is transmitting packets and reacting to fluctuations nowadays.
My research rethinks the latency in interactive video streaming in an end-to-end way and tries to share some preliminary thoughts on what the network and related communities should do to enable the wide deployment of these applications. In this talk, I will present our work on how to achieve a consistent low latency for interactive video streaming. Specifically, I will talk about how we control the tail latency on the transport layer from the perspective of congestion control (Zhuge, SIGCOMM'22) and loss recovery (Hairpin, NSDI'24).
Biography: Zili Meng is an assistant professor at HKUST. He received his B.Eng. (Hons) and Ph.D. (Hons) from Tsinghua University. His current research interest focuses on ultra-low latency interactive streaming from all layers. He is the recipient of the Doctoral Dissertation Awards from ACM China and China Institute of Electronics. a Microsoft PhD Fellowship (Asia), the Gold Medal of SIGCOMM 2018 SRC, and some best paper awards. His research has been used in many industry companies.
Host: Dr. Feng Qian
More Info: https://usc.zoom.us/j/95686725906?pwd=elFld2ZQZXM4a3ZjTkR0MTZoSDV4QT09
Webcast: https://usc.zoom.us/j/95686725906?pwd=elFld2ZQZXM4a3ZjTkR0MTZoSDV4QT09More Information: 2024.04.15 ECE Seminar - Zili Meng.pdf
Location: Kaprielian Hall (KAP) - KAP 209
WebCast Link: https://usc.zoom.us/j/95686725906?pwd=elFld2ZQZXM4a3ZjTkR0MTZoSDV4QT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://usc.zoom.us/j/95686725906?pwd=elFld2ZQZXM4a3ZjTkR0MTZoSDV4QT09
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute for Electrical & Computer Engineering Joint Seminar Series
Wed, Apr 17, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mauricio Castillo-Effen, Fellow at Lockheed Martin Advanced Technology Laboratories
Talk Title: New Vistas in Autonomy Assurance
Series: EE598 Seminar Series
Abstract: Achieving trustworthiness is a major challenge preventing autonomous technologies from realizing their full potential in applications without an "undo" option for undesirable consequences. This talk argues that some obstacles to attaining assurance in autonomy result from applying traditional engineering and safety-critical system certification views and practices that are incompatible with the unique nature and potential use cases of modern autonomous systems. These systems are typically deployed in highly variable environments, which easily lead to violations of design assumptions. Additionally, autonomous systems consist of components that use hard-to-assure technologies, such as machine learning, and are also often built from components sourced from complex supply chains. This talk introduces assurance as an epistemic endeavor, emphasizing its value in designing and developing systems that are fit for purpose and use. We will then discuss the concept of agility and its role in reinterpreting the use and application of assurance cases for continuous assurance. This discussion will include the interpretation of context and operational domains. We will also describe a socio-technical solution, potentially enhanced by generative AI, to introduce assurance early in the system's lifecycle. Real-world examples from autonomous systems and applications studied at the Lockheed Martin Advanced Technology Laboratories will be used to illustrate these concepts throughout the presentation. Finally, we will highlight promising assurance technologies and identify gaps that require attention from the research community. By acknowledging these gaps, we hope to encourage further research and collaboration to address the challenges of assuring autonomous systems.
Biography: Mauricio Castillo-Effen is a Fellow at Lockheed Martin Advanced Technology Laboratories (LM ATL), where he leads the research area in Trustworthy AI and Autonomy (TAA). His team focuses on developing solutions for deploying complex decision-making technologies such as autonomy and artificial intelligence in high criticality applications. He collaborates closely with Lockheed Martin’s Business Areas to address challenges related to verification, validation, testing, evaluation, and certification. Previously, at General Electric, he led R&D efforts in aviation, aerial autonomy, and mining robotics, which led to the establishment of the robotics research laboratory and a spin-off company focused on robotic inspection, repair, and replacement. He has served as Principal Investigator and contributor for multiple R&D programs funded by DARPA, AFRL, NASA, and DHS, advancing the fields of autonomy, assurance, and certification in the aerospace industry. He has a background in systems theory, control and estimation, cyber-physical systems, embedded systems, and robotics. He has also taught controls and mechatronics at multiple universities worldwide. He holds more than twenty patents in robotics, autonomy, and aviation. Mauricio received his Ph.D. in Electrical Engineering from the University of South Florida
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Ariana Perez
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Munushian Keynote Lecture, Nobel Laureate (2023 Physics) - Ferenc Krausz, Wednesday, April 17th at 2:30pm in EEB 132 & Zoom
Wed, Apr 17, 2024 @ 02:30 PM - 04:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ferenc Krausz, Max Planck Institute of Quantum Optics, Garching, Germany
Talk Title: SUB-ATOMIC MOTIONS From capturing electrons to probing human health
Series: Munushian Visiting Seminar Series
Abstract: Born at the dawn of the new millennium, attosecond "photography" has opened the door for capturing sub-atomic motions as they evolve in time. Control of the oscillating electric field of light has permitted the attosecond control of electrons with unprecedented precision in space and time. Fundamental quantum phenomena, such as electron tunnelling and dipole oscillations in atoms or light-electron energy exchange in solids as well as fundamental classical phenomena, such as the field oscillations of visible light, became accessible to human observation in slow-motion replay. These capabilities open new avenues for 21st-century science, technology and medicine. Some of them emerge from the ability to sample light fields with attosecond precision. Possible implications of these advances include hundred thousand times faster electronics and cost-effective monitoring of human health.
Biography: Ferenc Krausz graduated in electrical engineering from the Budapest University of Technology and completed his studies in theoretical physics at the Eötvös Loránd University in 1985. He earned his doctorate in laser physics from the Technische Universität Wien (1991), where he became professor in 1998. In 2003-2004, he was appointed director at the Max-Planck-Institute of Quantum Optics in Garching and chair of experimental physics - laser physics at the Ludwig-Maximilians-Universität and established "Attoworld" at these two sites (attoworld.de).In a series of experiments performed between 2001 and 2004 his team succeeded in producing and measuring isolated attosecond pulses of light and applying them to observe sub-atomic motions. Attoworld has been fostering the proliferation of the emerging field, attosecond science, and - since 2015 - exploring its utility for probing human health. For his contributions to establishing the field of Attosecond Science, Ferenc Krausz has been awarded the King-Faisal International Prize for Science (2013), the Wolf-Prize in Physics (2022), the BBVA Frontiers of Knowledge Award (2023) and the 2023 Nobel Prize in Physics.
Host: ECE-EP
More Information: Ferenc Krausz Keynote Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ISSS - Dr. Shanthi Pavan, Thursday, April 18th at 10am in EEB 132
Thu, Apr 18, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Shanthi Pavan, IIT Madras
Talk Title: Continuous-Time Pipelined Analog-to-Digital Converters -“ Where Filtering Meets Analog-to-Digital Conversion
Series: Integrated Systems
Abstract: If someone told you that the power, noise, distortion, and area of a mixed-signal block could be reduced all at the same time, you'd probably think that this was a lie. It turns out that it is indeed possible sometimes - and this talk will present an example called the continuous-time pipeline (CTP) ADC. The CTP is an emerging technique that combines filtering with analog-to-digital conversion. Like a continuous-time delta-sigma modulator (CTDSM), a CTP has a "nice" input impedance that is easy to drive and has inherent anti-aliasing. However, unlike a CTDSM, a CTP does not require a high-speed feedback loop to be closed. As a result, it can achieve significantly higher bandwidth (like a Nyquist ADC). After discussing the operating principles behind the CTP, we describe the fundamental benefits of the CTP over a conventional signal chain that incorporates an anti-alias filter and a Nyquist-rate converter. We will then show design details and measurement results from a 100MHz 800MS/s CTP designed in a 65nm CMOS process.
Biography: Shanthi Pavan received the B.Tech. degree in electronics and communication engineering from IIT Madras, Chennai, India, in 1995, and the M.S. and D.Sc. degrees from Columbia University, New York, NY, USA, in 1997 and 1999, respectively. From 1997 to 2000, he was with Texas Instruments, Warren, NJ, USA, where he worked on high-speed analog filters and data converters. From 2000 to June 2002, he worked on microwave ICs for data communication at Bigbear Networks, Sunnyvale, CA, USA. Since July 2002, he has been with IIT Madras, where he is currently the NT Alexander Institute Chair Professor of Electrical Engineering. He is the author of Understanding Delta-Sigma Data Converters (second edition, with Richard Schreier and Gabor Temes), which received the Wiley-IEEE Press Professional Book Award for the year 2020. His research interests are in the areas of high-speed analog circuit design and signal processing. Dr. Pavan is a fellow of the Indian National Academy of Engineering, and the recipient of several awards, including the IEEE Circuits and Systems Society Darlington Best Paper Award in 2009. He has served as the Editor-in-Chief of the IEEE Transactions on Circuits and Systems-I: Regular Papers. He has been a Distinguished Lecturer of the Solid-State Circuits and Circuits-and-Systems Societies. He currently serves as the Vice-President of Publications of the IEEE Solid-State Circuits Society, on the Technical Program Committee of the International Solid-State Circuits Conference (ISSCC), and on the editorial board of the IEEE Journal of Solid-State Circuits. He is an IEEE Fellow.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Information: Shanthi Pavan Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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ECE Seminar: Dr. Yuejie Chi, "Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models"
Wed, Apr 24, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Yuejie Chi, Sense of Wonder Group Endowed Professor in AI Systems | Department of Electrical and Computer Engineering | Carnegie Mellon University
Talk Title: Solving Inverse Problems with Generative Priors: From Low-rank to Diffusion Models
Abstract: Generative priors are effective countermeasures to combat the curse of dimensionality, and enable efficient learning and inversion that otherwise are ill-posed, in data science. This talk begins with the classical low-rank prior, and introduces scaled gradient descent (ScaledGD), a simple iterative approach to directly recover the low-rank factors for a wide range of matrix and tensor estimation tasks. ScaledGD provably converges linearly at a constant rate independent of the condition number at near-optimal sample complexities, while maintaining the low per-iteration cost of vanilla gradient descent, even when the rank is overspecified and the initialization is random. Going beyond low rank, the talk discusses diffusion models as an expressive data prior in inverse problems, and introduces a plug-and-play posterior sampling method (Diffusion PnP) that alternatively calls two samplers, a proximal consistency sampler solely based on the forward model, and a denoising diffusion sampler solely based on the score functions of data prior. Performance guarantees and numerical examples will be demonstrated to illustrate the promise.
Biography: Dr. Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with courtesy appointments in the Machine Learning department and CyLab. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, decision making, and generative AI. Among others, Dr. Chi is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing, and multiple paper awards including the SIAM Activity Group on Imaging Science Best Paper Prize and IEEE Signal Processing Society Young Author Best Paper Award. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.
Host: Dr. Richard Leahy, leahy@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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AAI-CCI-MHI Seminar on CPS
Wed, Apr 24, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rose Faghih, Associate Professor New York University
Talk Title: Smartwatch-Brain Interface Architectures for Mental Well-Being and Productivity
Series: EE598 Seminar Series
Abstract: Smartwatch-like wearables have enabled seamless tracking of vital signs and physical activities. An unexploited capability is that the pulsatile physiological time series collected by wrist-worn wearable devices can be used for recovering internal brain dynamics. We design algorithms for a closed-loop brain-aware wearable architecture called MINDWATCH for tracking and regulating neurobehavioral states of arousal and performance. This closed-loop smartwatch-brain interface framework includes (1) an inference engine for recovering arousal-related autonomic nervous system (ANS) activations, (2) Bayesian state-space decoders for tracking cognitive arousal and performance states, (3) control algorithms for maintaining these neurobehavioral states within desired ranges, and (4) neurofeedback experiments for closing the loop via safe actuation. The methods are validated by analyzing experimental data as well as simulation studies. Results demonstrate a promising approach for tracking and regulating neurocognitive arousal and performance states through wearable devices. Since smartwatches can be used conveniently in one’s daily life, smartwatch-brain interface architectures have a great potential to monitor and regulate one’s neurocognitive stress seamlessly in real-world situations.
Biography: Rose T. Faghih is an associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory within the NYU Langone Health's Tech4Health Institute. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT). She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Rose is the recipient of various awards including a 2023 National Institutes of Health (NIH) Maximizing Investigators' Research Award for Early-Stage Investigators, a 2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a 2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. She is on the editorial board of PNAS Nexus by the National Academy of Sciences and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Her research interests include wearable technologies, and medical cyber-physical systems, as well as neural and biomedical signal processing.
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Ariana Perez
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AAI-CCI-MHI Seminar on CPS
Fri, Apr 26, 2024 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Joerg Lahann, Professor University of Michigan
Talk Title: Protein Nanoparticles as multifunctional drug delivery carriers
Series: EE598 Seminar Series
Abstract: Precise control of the physical and biochemical properties of nanoparticle-based drug delivery vehicles is a
prerequisite for effective transport of drugs across a range of biological barriers. To date, the range of biodegradable
macromolecular systems with appropriate biocompatibility, low levels of immunogenicity and extended structural stability
that can be prepared at scale remains rather limited. Towards that end, nanoparticles comprised of protein/polymer
conjugates offer a range of unique features, such as biodegradability and extended in vivo stability, active targeting and
stimuli-responsiveness, or the potential for delivery of small-molecule drugs and biopharmaceuticals.
Electrohydrodynamic (EHD) co-jetting, an adaptive manufacturing process that involves transferring two or more
capillary needles in a side-by-side configuration, can be used to create a wide range of multicompartmental protein/
polymer nanoparticles. The protein nanoparticles combine the processability of synthetic polymers with the biological
properties of proteins. In the context of glioblastoma multiforme, protein nanoparticles have been devised that enable
systemic delivery of RNAi to intracranial brain tumors. Protein nanoparticles that can enable controlled release of
combination drugs from the same nanoparticle will also be discussed
Biography: Joerg Lahann is the Wolfgang Pauli Collegiate Professor of Chemical Engineering. Since 2012, he has been the
founding director of the University of Michigan Biointerfaces Institute. Prof. Lahann is a co-author of more than 325
publications including papers in Science, Nature Materials, Nature Biotechnology, or PNAS and has contributed to 50
patents and patent applications. He is an elected fellow of the National Academy of Inventors (NAI), the American
Association for the Advancement of the Sciences (AAAS), and the American Institute for Medical and Biological
Engineering (AIMBE). He has been selected by Technology Review as one of the top 100 young investigators and the
recipient of the 2007 Nanoscale Science and Engineering Award, a NSF-CAREER award, and both a single-PI and a
team Idea award (2006 & 2011) from the US Department of Defense. Prof. Lahann has contributed to the development
of 3D printing methods for ultraporous, precisely engineered organoids, multicompartmental nanoparticles for drug
delivery applications, and vapor-based polymer coatings.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Ariana Perez
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3rd Annual Symposium USC-Amazon Center on Trustworthy AI
Tue, Apr 30, 2024 @ 09:00 AM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Workshops & Infosessions
Join us in conversation with distinguished keynote speakers, technical presentations from the USC-Amazon Center’s innovative research projects, and talks by Amazon ML Ph.D. fellows.
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Ariana Perez
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USC-Amazon Center on Trustworthy AI - 3rd Annual Symposium
Tue, Apr 30, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Join us in conversation with distinguished keynote speakers, technical presentations from the USC-Amazon Center's innovative research projects, and talks by Amazon ML Ph.D. fellows.,
Talk Title: USC-Amazon Center on Trustworthy AI
Host: Dr. Salman Avestimehr, avestime@usc.edu
More Info: https://trustedai.usc.edu
More Information: USC-Amazon Center Symposium Flyer.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - MCB 101
Audiences: Everyone Is Invited
Contact: Ariana Perez
Event Link: https://trustedai.usc.edu
