SUNMONTUEWEDTHUFRISAT
Events for November
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Photonics Seminar - Aydogan Ozcan, Tuesday, November 1 at 1:30pm in EEB 248
Tue, Nov 01, 2022 @ 01:30 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Aydogan Ozcan, UCLA
Talk Title: Diffractive Optical Networks & Computational Imaging Without a Computer
Series: Photonics Seminar Series
Abstract: I will discuss diffractive optical networks designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially-engineered surfaces. These diffractive processors designed by deep learning have various applications, e.g., all-optical image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components for spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) all-optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest in e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing.
Biography: Dr. Aydogan Ozcan is the Chancellor's Professor and the Volgenau Chair for Engineering Innovation at UCLA and an HHMI Professor with the Howard Hughes Medical Institute, leading the Bio- and Nano-Photonics Laboratory at UCLA School of Engineering and is also the Associate Director of the California NanoSystems Institute. Dr. Ozcan is elected Fellow of the National Academy of Inventors (NAI), holds>55 issued/granted patents, and is the author of one book and the co-author of >950 peer-reviewed publications in major scientific journals and conferences. Dr. Ozcan is the founder and a member of the Board of Directors of Lucendi Inc., Hana Diagnostics, Pictor Labs, as well as Holomic/Cellmic LLC, which was named a Technology Pioneer by The World Economic Forum in 2015. Dr. Ozcan is also a Fellow of the American Association for the Advancement of Science (AAAS), the International Photonics Society (SPIE), the Optical Society of America (OSA), the American Institute for Medical and Biological Engineering (AIMBE), the Institute of Electrical and Electronics Engineers (IEEE), the Royal Society of Chemistry (RSC), the American Physical Society (APS) and the Guggenheim Foundation, and has received major awards including the Presidential Early Career Award for Scientists and Engineers, International Commission for Optics (ICO) Prize, Joseph Fraunhofer Award & Robert M. Burley Prize (Optica), Biophotonics Technology Innovator Award (SPIE), Rahmi M. Koc Science Medal, International Photonics Society Early Career Achievement Award (SPIE), Army Young Investigator Award, NSF CAREER Award, NIH Director's New Innovator Award, Navy Young Investigator Award, IEEE Photonics Society Young Investigator Award and Distinguished Lecturer Award, National Geographic Emerging Explorer Award, National Academy of Engineering The Grainger Foundation Frontiers of Engineering Award and MIT's TR35 Award for his seminal contributions to computational imaging, sensing and diagnostics. Dr. Ozcan is also listed as a Highly Cited Researcher by Web of Science, Clarivate.
Host: Mercedeh Khajavikhan
More Information: Aydogan Ozcan Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Virtual seminar Causal Dependencies in Multivariate Time Series
Wed, Nov 02, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Sunil Kumar Vuppala, Director, Data Science, Ericsson
Talk Title: Causal Dependencies in Multivariate Time Series
Abstract: The talk starts with a motivation for casual analysis. Correlation does not imply causation. Similarly, lack of correlation does not imply lack of causation. How can we detect causality in complex systems such as telecom networks? The talk covers 3Wh of Causality (What, why, where). It covers taxonomy of time series casual discovery, an overview of the methods and techniques such as Granger causality, Convergent Cross Mapping, information theoretic approaches, graphical approaches and ML approaches. How can we connect any observed event to possibly a set of specific causal events? Why do we need counterfactual interventions for causal intensity? The talk describes the relevance of causality in telecom domain and cover a few use cases in telecom such as Key Performance Indicator (KPI) degradation and outlier root cause analysis and hardware failures. The speaker will share sample results from simulated experiments. He will introduce a few active research topics in this space.
Biography: Dr. Sunil Kumar Vuppala is the Director -“ Data Science, Ericsson Global AI Accelerator (GAIA), Bangalore. Dr. Vuppala has 18 years of industrial and research experience in Machine learning, Deep learning, Analytics, Internet of Things, and Automation. Sunil worked in Oracle, Infosys R&D, and Philips Research before joining Ericsson. He is the inventor of 35+ patents (6 US granted and 30+ published), has published 30+ papers, and delivered 100+ guest lectures. Dr. Vuppuala is a senior member of ACM, IEEE, and a fellow of IETE and IEI. He is one of the Top 10 data scientists in India for 2019, recipient of Zinnov Technical Role Model in Emerging Technology award 2020, IEEE TEMS Engineering Manager of the Year 2020, and ACM distinguished speaker. Dr. Vuppula is an alumnus of premier institutes in India. He is a visiting faculty at Case Western Reserve University, USA.
Host: Urbashi Mitra
More Info: https://usc.zoom.us/j/94255391488 pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09 Meeting ID: 942 5539 1488 Passcode: 114454
Location: https://usc.zoom.us/j/94255391488 pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09 Meeting ID: 942 5539 1488
Audiences: Everyone Is Invited
Contact: Susan Wiedem
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MHI ISSS Seminar - Dr. Omeed Momeni, Friday, Nov. 4th at 2pm in EEB 132
Fri, Nov 04, 2022 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Omeed Momeni, University of California
Talk Title: Scalable Standing Wave Integrated Circuits for Reconfigurable Power Generation, Radiation and Beam Steering at mm-Wave and Terahertz Spectrum
Series: Integrated Systems
Abstract: The power generation of transistors declines as the operation frequency increases. At the same time, the
free space propagation loss increases, demanding more radiated power from the system. The loss of passive
elements in the circuit increases as well, making functions such as oscillation or radiation even more challenging. In
order to boost the limited power, multiple sources need to be coupled together in an array structure. However, the
significant loss of the coupling circuitry and phase shifters at mm-wave and terahertz frequencies hinders the
implementation of large and efficient radiator and phased arrays. Scalable standing wave array structures are
proposed based on efficient low loss coupling schemes in order to boost the power and operation bandwidth.
Furthermore, a practical approach is proposed to maximize Equivalent Isotropic Radiated Power (EIRP) of the
source by optimizing influential parameters of the radiation apparatus. Finally, we demonstrate a new phase shifting
method based on combining standing and traveling waves and show how it can achieve significantly higher
reconfigurability, phase shifting range and bandwidth. Using all these methods we present coupled-oscillators,
scalable radiator arrays, and reconfigurable phased arrays that can produce high resolution images and achieve
record beam steering range, tuning range, and output power at mm-wave and terahertz frequencies.
Biography: Dr. Omeed Momeni (S'04-M'12-SM'18) received the B.Sc. degree from Isfahan University of
Technology, Isfahan, Iran, the M.S. degree from University of Southern California, Los Angeles,
CA, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical Engineering, in
2002, 2006, and 2011, respectively. He joined the faculty of Electrical and Computer
Engineering Department at University of California, Davis in 2011 and is currently an
Associate Professor. He was a visiting professor in Electrical Engineering and Computer
Science Department at University of California, Irvine from 2011 to 2012. From 2004 to 2006,
he was with the National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL) as a RFIC
designer. His research interests include mm-wave and terahertz integrated circuits and systems.
Prof. Momeni serves as an Associate Editor for The IEEE Microwave and Wireless Components Letters (MWCL) since
2021, and a Technical Program Committee (TPC) member of Radio Frequency Integrated Circuits (RFIC) Symposium
since 2018. He has also served as a Distinguished Lecturer for Solid-State Circuits Society (SSCS) in 2020-22, an
Associate Editor of Transactions on Microwave Theory and Techniques (TMTT) in 2018-20, a Steering Committee
Member (2020) and Technical Program Review Committee Member (2017-20) of the International Microwave
Symposium (IMS), an organizing committee member of IEEE International Workshop on Design Automation for
Analog and Mixed-Signal Circuits in 2013, and the chair of the IEEE Ithaca GOLD section in 2008-11. Prof. Momeni is
the recipient of UC Davis Graduate Program Advising and Mentoring Award in 2022, National Science Foundation
CAREER award in 2015, the Professor of the Year 2014 by IEEE at UC Davis, the Best Ph.D. Thesis Award from the
Cornell ECE Department in 2011, the Outstanding Graduate Award from Association of Professors and Scholars of
Iranian Heritage (APSIH) in 2011, the Best Student Paper Award at the IEEE Workshop on Microwave Passive
Circuits and Filters in 2010, the Cornell University Jacob's fellowship in 2007 and the NASA-JPL fellowship in 2003.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Information: Abstract and Bio-Nov 4-Momeni.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Photonics Seminar - Galan Moody, Tuesday, November 8th at 3pm in MCB 101
Tue, Nov 08, 2022 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Galan Moody, University of California Santa Barbara
Talk Title: Integrated Quantum Photonics Beyond Silicon
Abstract: Silicon-on-insulator has been an indispensable platform for classical and quantum photonics, but it is lacking in several aspects that are needed for the next generation of advanced quantum technologies. Beyond silicon, there is a spectrum of alternative crystalline materials-”such as compound semiconductors, lithium niobate, and 2D materials-”that have the potential to enable radically new device concepts and related quantum technologies. In this presentation, I will highlight my group's progress in developing several heterogeneous photonic platforms to address key challenges in quantum communications, networking, and distributed information processing. These include AlGaAs-on-insulator photonics for ultra-efficient entangled-pair generation, squeezing, and frequency-bin information processing; the integration of AlGaAs and 2D material single-photon sources with silicon photonics; and hybrid quantum dot devices with opto-electronic and opto-mechanical resonators for dynamic tuning and quantum transduction.
Biography: Professor Moody joined the Electrical and Computer Engineering Department at the University of California Santa Barbara in July 2019. Prior to moving to Santa Barbara, he was a Research Scientist (2015-2019) at the National Institute of Standards and Technology (NIST) in Boulder, Colorado, USA, a National Research Council postdoctoral fellow at NIST (2013-2015), and a postdoctoral associate at the University of Texas, Austin, USA (2013). He received his PhD Degree in Physics (2013) and his BSc Degree in Engineering Physics (2008) from the University of Colorado Boulder. He is a recipient of an Air Force Young Investigator Program award (2020) and an NSF CAREER award (2021) for research on integrated quantum photonic technologies. He serves as a thrust co-lead and on the executive committee for UCSB's Quantum Foundry (an NSF institute for quantum materials and related technologies), on the technical program committees for several conferences including CLEO, and on the editorial board for IOP's Journal of Physics: Photonics.
Host: Mercedeh Khajavikhan
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 09, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Quan Nguyen, Aerospace and Mechanical Engineering at the University of Southern California
Talk Title: Toward the Development of Highly Adaptive Legged Robots
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Deploying legged robots in real-world applications will require fast adaptation to unknown terrain and model uncertainty. Model uncertainty could come from unknown robot dynamics, external disturbances, interaction with other humans or robots, or unknown parameters of contact models or terrain properties. In this talk, I will first present our recent works on adaptive control and adaptive safety-critical control for legged locomotion adapting to substantial model uncertainty. In these results,
we focus on the application of legged robots walking rough terrain while carrying a heavy load. I will then talk about our solution on trajectory optimization that allows legged robots to adapt to a wide variety of challenging terrain. This talk will also discuss the combination of control, trajectory optimization and reinforcement learning toward achieving long-term adaptation in both control actions and trajectory planning for legged robots.
Biography: Quan Nguyen is an Assistant Professor of Aerospace and Mechanical Engineering at the University of Southern California. Prior to joining USC, he was a Postdoctoral Associate in the Biomimetic Robotics Lab at the Massachusetts Institute of Technology (MIT). He received his Ph.D. from Carnegie Mellon University (CMU) in 2017 with the Best Dissertation Award. His research interests span different control and optimization approaches for highly dynamic robotics including nonlinear control, trajectory optimization, real-time optimization-based control, robust and adaptive control. His work on the bipedal robot ATRIAS walking on steppingstones was featured on the IEEE Spectrum, TechCrunch, TechXplore and Digital Trends. His work on the MIT Cheetah 3 robot leaping on a desk was featured widely in many major media channels, including CNN, BBC, NBC, ABC, etc. Nguyen won the Best Presentation of the Session at the 2016 American Control Conference (ACC) and the Best System Paper Finalist at the 2017 Robotics: Science & Systems conference (RSS). Nguyen is a recipient of the 2020 Charles Lee Powell Foundation Faculty Research Award.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
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ISSS - Bahar Jalali-Farahani, Friday, Nov. 11th at 2pm in EEB 132
Fri, Nov 11, 2022 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Bahar Jalali-Farahani, Technical Lead, Cisco
Talk Title: Toward Tbps Optical and Wireline Communication: a Circuit Design Perspective
Series: Integrated Systems
Abstract: The demand for higher data rate communication has never been greater than today. Driven by
emerging technologies particularly IoT and cloud computing, higher capacity is required both in core
networking as well as computing applications. A report by the IEEE Ethernet Bandwidth Assessment ad
hoc group stated that "global demand for network bandwidth is growing at such an alarming rate that
terabit-speed networks will be the only way to support capacity, should current trends continue through
2015". This brings new challenges for circuit designer community as higher speed and better energy
efficiency are expected from building blocks of such communication systems.
This talk starts with an introduction to the two major category of optical communication; IMDD (Intensity
Modulated Direct Detect) vs Coherent detection. Pros, cons, and application of each are discussed and the
general architecture of receivers and transmitters in these systems are given. The talk then reviews the
latest trends in the design of high-speed transimpedance amplifiers and modulator drivers. Some examples
of co-design and co-optimization with optics are presented.
Biography: Bahar Jalali-Farahani received her PhD in electrical engineering from The Ohio
State University in 2005. During her PhD program, she was working with the data
converter research group at Freescale Semiconductor in Tempe, AZ where she was
responsible for developing digital calibration techniques for high resolution data
converters. She joined the department of electrical engineering at Arizona State
University in January 2006 and continued her research on digitally assisted high
performance analog circuits, and low-power circuit techniques. From 2011 to 2014
she was with Cisco Systems working on design of high-speed components for
Silicon-Photonics-based 100Gb Ethernet. In 2014 she joined Nokia Bell Labs in NJ
where she was a major contributor to the development of Nokia's Wavence products, multi-standard
microwave links used for long haul and short haul applications. Since September 2017 She has been with
Acacia Communications (now part of Cisco) working on millimeter-wave front ends for Silicon-Photonics
coherent receivers.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Information: Abstract and Bio-Nov 11-Jalali.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Photonics seminar speaker - Jeffrey Moses, Tuesday, November 15th at 3pm in MCB 102
Tue, Nov 15, 2022 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jeffrey Moses, Cornell University
Talk Title: Tackling longstanding challenges in ultrafast nonlinear optics via foreign but familiar physics
Series: Photonics Seminar Series
Abstract: Silicon-Optical nonlinearities have expanded the optics and photonics toolset for applications as diverse as high intensity laser science, quantum information processing, and the imaging and spectroscopy of biological systems. Key to many applications is use of the nonlinear polarizability of materials to couple photons between optical fields, giving rise to amplification and frequency conversion methods that expand the reach of lasers and other photon sources, both classical and non-classical. Other applications use light 'self-effects' to guide, switch, and modulate. However, optical nonlinearities are often small, and even when large enough, the spatiotemporal and spectral inhomogeneities in nonlinear optical systems can severely hamper the efficiency and bandwidth of power flow between waves.
Our group has been seeking ways to 'trick' nonlinear systems into modes of evolution that can avoid the normal limiting behaviors or to make use of unconventional nonlinear interactions. I'll discuss a few of these that possess familiar physics that are somewhat foreign to optical light pulses, such as rapid adiabatic passage in optical frequency conversion, oscillation damping in parametric (i.e., lossless) wave mixing, and nonlinear optical interactions involving coherent phonon coupling. And I will present some technologies that they can enable, including efficient parametric amplifiers, dispersion-free octave-spanning frequency up- and down-converters, strong cross-phase modulation, and the removal of spectral distinguishability.
Biography: Jeff Moses joined the faculty at Cornell University in 2014, where he leads the Ultrafast Phenomena and Technologies Group in the School of Applied and Engineering Physics. He received his B.S. from Yale and Ph.D. from Cornell, with both degrees in applied physics, and spent several years at the Optics & Quantum Electronics Group in the Research Laboratory of Electronics at MIT as a postdoctoral associate and research scientist. He has received the US National Science Foundation CAREER award and was an Air Force Office of Scientific Research Young Investigator.
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: Jeffery Moses Flyer.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 16, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hayk Martiros, Skydio
Talk Title: Frontiers of Autonomous Flight and Real-Time 3D Reconstruction
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: At Skydio, we ship autonomous robots that are flown at scale in unknown environments every day by our customers to capture incredible video, automate dangerous inspections, build digital twins, and protect the lives of soldiers and first responders. These robots operate intelligently and make decisions at high speed using their onboard cameras and algorithms. We've invested a decade of R&D into handling complex visual scenarios and building a robust pipeline for visual navigation, obstacle avoidance, and rapid trajectory planning. On top of that, we're building a rich ecosystem of real-time 3D reconstruction technology to enable 360 global localization and map building on our drones.
During the talk, I will discuss the technology and impact of our core navigation stack and 3D Scan technology, and what research frontiers lie ahead. I plan to share visual examples of the algorithms in action, and connect to how these products solve pressing global challenges and enable next-generation operations across multiple industries. I will also introduce SymForce, our library for fast symbolic computation, code generation, and nonlinear optimization. This library powers many of our algorithms, and we have just published and open-sourced it as a contribution to the robotics community.
Biography: Hayk is a roboticist leading the autonomy group at Skydio, building robust visual autonomy to enable the positive impact of drones. Hayk has worked at Skydio since 2015 and was one of its first employees, where he contributed to all of Skydio's core autonomy systems. He now focuses on technical management of world-class engineers and researchers. Hayk's technical interests are in computer vision, deep learning, nonlinear optimization, systems architecture, and symbolic computation. His previous works include novel hexapedal robots, collaboration between robot arms, micro-robot factories, solar panel farms, and self-balancing motorcycles. Hayk was born in Yerevan, Armenia and grew up in Fairbanks, Alaska. He did his undergraduate study at Princeton University and graduate study at Stanford University.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia Whtie
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ECE Seminar: Learning Efficiently in Data-Scarce Regimes
Fri, Nov 18, 2022 @ 01:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Mohammad Rostami, Research Assistant Professor, Dept of CS / Research Lead, USC-ISI
Talk Title: Learning Efficiently in Data-Scarce Regimes
Abstract: The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and avoid learning redundant information. Inspired from learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. In this talk, we explain how the challenges of knowledge transfer can be addressed through embedding spaces that capture and store hierarchical knowledge.
We first focus on the problem of cross-domain knowledge transfer. We explore the problem of zero-shot image classification, where the goal is to identify images from unseen classes using semantic descriptions of these classes. We train two coupled dictionaries that align visual and semantic domains via an intermediate embedding space. We then extend this idea by training deep networks that match data distributions of two visual domains in a shared cross-domain embedding space.
We then investigate the problem of cross-task knowledge transfer in sequential learning settings. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across the tasks. We first address the problem of zero-shot learning in a lifelong machine learning setting, where the goal is to learn tasks with no data using high-level task descriptions. Our idea is to relate high-level task descriptors to the optimal task parameters through an embedding space. We then develop a method to overcome the problem of catastrophic forgetting within a continual learning setting of deep neural networks by enforcing the tasks to share the same distribution in the embedding space.
Finally, we focus on current research directions to expand past progress and plans for future research directions. Through this talk, we demonstrate that despite major differences, problems within the above learning scenarios can be tackled using a unifying strategy that allows transferring knowledge effectively.
Biography: Mohammad Rostami is a research assistant professor at the USC CS department and a research lead at the USC Information Sciences Institute. He received Ph.D. degree in Electrical and Systems Engineering from the University of Pennsylvania in August 2019. He also received an M.S. degree in Robotics and M.A. degree in Philosophy at Penn. Before Penn, he obtained an M.Sc. degree in Electrical and Computer Engineering from the University of Waterloo, and his B.Sc. degree in Electrical Engineering and B.Sc. degree in Mathematics from the Sharif University of Technology. His current research area is machine learning in time-dependent and data-scarce regimes.
Host: Dr. Richard M. Leahy
Webcast: https://usc.zoom.us/j/97552157471?pwd=RnVGWm10RlRORFU0cG5RYWVWU0R0Zz09More Information: Seminar Announcement-Rostami-111822.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
WebCast Link: https://usc.zoom.us/j/97552157471?pwd=RnVGWm10RlRORFU0cG5RYWVWU0R0Zz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Tue, Nov 22, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mihaela van der Schaar, University of Cambridge
Talk Title: AI for Science: Discovering Diverse Classes of Equations in Medicine and Beyond
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Artificial Intelligence (AI) offers the promise of revolutionizing the way scientific discoveries are made and significantly accelerating their pace. This is important for numerous fields of study, including medicine. In this talk, I will present our research on AI for science over the past few years. I will start by briefly showing how we can discover closed-form prediction functions from cross-sectional data using symbolic metamodels. Then, I will introduce a new method, called D-CODE, which discovers closed-form ordinary differential equations (ODEs) from observed trajectories (longitudinal data).This method can only describe observable variables, yet many important variables in medical settings are often not observable. Hence, I will subsequently present the latent hybridisation model (LHM) that integrates a system of ODEs with machine-learned neural ODEs to fully describe the dynamics of the complex systems. However, ODEs are fundamentally inadequate to model systems with long-range dependencies or discontinuities. To solve these challenges, I will then present Neural Laplace, with which we can learn diverse classes of differential equations in the Laplace domain. I will conclude by presenting next research frontiers, including recent work on discovering partial differential questions from data (D-CIPHER). While these works are applicable in numerous scientific domains, in this talk I will illustrate the various works with examples from medicine, ranging from understanding cancer evolution to treating Covid-19. This work is joint work with Zhaozhi Qian, Krzysztof Kacprzyk and Sam Holt.
Biography: Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).
Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.
Host: Urbashi Mitra and Pierluigi Nuzzo
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
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Virtual Efficient Estimation of Treatment Effect in Online Experiments
Wed, Nov 30, 2022 @ 10:00 AM - 11:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Congshan Zhang, Meta , Core Data Science Team at Meta
Talk Title: Efficient Estimation of Treatment Effect in Online Experiments
Abstract: Randomized controlled trials are commonly used by tech companies to draw causal conclusions on various product changes. The confidence intervals from these experiments, however, are usually too large due to reasons such as limited number of users, heavy-tailed outcome variables and small treatment effects. Improving estimation efficiency for randomized controlled trials is not only a scientifically interesting but also a practically relevant area of research. In this talk, I will go over a few prominent techniques in statistics to improve estimation efficiency. Basic techniques such as CUPED and more advanced methodologies based on ML and synthetic controls will be introduced.
Biography: Congshan Zhang is a research scientist on Core Data Science Team at Meta. Congshan is interested in various topics in statistics and econometrics including causal inference, machine learning and time series. Congshan holds Ph.D. in economics from Duke University. Before joining Meta, Congshan did research on financial econometrics, with a focus on nonparametric and semi-parametric inference using high-frequency data and on testing models of financial markets. His work appears in top journals of econometrics such as Journal of Econometrics and Annals of Applied Probability.
Host: Urbashi Mitra
More Info: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 Passcode: 586135
More Information: ECE Seminar Announcement_Nov21.docx
Location: https://usc.zoom.us/j/96927080167?pwd=Vk9MOEpOSUx3V1hlZFc3U0tmOTNsUT09 Meeting ID: 969 2708 0167 P
Audiences: Everyone Is Invited
Contact: Susan Wiedem
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 30, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Vikas Sindhwani, Google Brain
Talk Title: Foundation Models for Robotics
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Trained on internet-scale datasets, large language and vision models demonstrate breakthrough capabilities which until recently were thought to still be decades away in technological feasibility. Does this imply a paradigm shift in Robotics as well? If so, what is the bridge from symbols and tokens on the internet to actions in the physical world? Through a few illustrative vignettes of robotic manipulation and navigation research at Google, I will propose speculative paths towards making robots useful in human-centric spaces.
Biography: Vikas Sindhwani is Senior Staff Research Scientist in the Google Brain team in New York where he leads a research group focused on solving a range of planning, perception, learning, and control problems arising in Robotics. His interests are broadly in core mathematical foundations of statistical learning, and in end-to-end design aspects of building large-scale, robust machine intelligence systems. He received the best paper award at Uncertainty in Artificial Intelligence (UAI) 2013, the IBM Pat Goldberg Memorial Award in 2014, and was finalist for Outstanding Planning Paper Award at ICRA-2022. He serves on the editorial board of Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Pattern Analysis and Machine Intelligence; he has been area chair and senior program committee member for NeurIPS, International Conference on Learning Representations (ICLR) and Knowedge Discovery and Data Mining (KDD). He previously led a team of researchers in the Machine Learning group at IBM Research, NY. He has a PhD in Computer Science from the University of Chicago and a B.Tech in Engineering Physics from Indian Institute of Technology (IIT) Mumbai. His publications are available at: http://vikas.sindhwani.org/.
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3QLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_ySGInGwKRKKHX7NHJwTk3Q
Audiences: Everyone Is Invited
Contact: Talyia White
