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Conferences, Lectures, & Seminars
Events for October
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Six Sigma Black BeltTue, Oct 04, 2022 @ 08:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Everyone Is Invited
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
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CS Colloquium: Jonathan Kelly (University of Toronto) - Keeping Your Distances: A Distance-Geometric Perspective on Inverse Kinematics
Tue, Oct 04, 2022 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jonathan Kelly, University of Toronto
Talk Title: Keeping Your Distances: A Distance-Geometric Perspective on Inverse Kinematics
Series: Computer Science Colloquium
Abstract: In this talk, I will discuss recent work in my group on the problem of inverse kinematics (IK): finding joint angles that achieve a desired robot manipulator end-effector pose. A wide range of IK solvers exist, the majority of which operate on joint angles as parameters. Because the problem is highly nonlinear, these solvers are prone to local minima (among other troubles). I will introduce an alternate formulation of IK based on distance geometry, where a robot model is defined in terms of distances between rigidly-attached points. This alternative geometric description of the kinematics reveals an elegant equivalence between IK and the problem of low-rank Euclidean distance matrix completion. We use this connection to implement two novel solutions to IK for various articulated robots. The first is a Riemannian optimization-based approach which leverages the structure of the EDM manifold. The second solves a series of convex semidefinite relaxations of the distance-geometric problem. Both methods outperform many existing solvers on a variety of IK problems, some of which incorporate collision avoidance and joint limit constraints. Finally, I will describe a learned IK solver we have recently developed that is able to quickly generate sets of diverse approximate IK results for many different manipulators.
Prof. Kelly will give his talk in person at RTH 115 and we will also host the talk over Zoom.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_ShCRcm1iTrq6ubaaY-8P5Q
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Prof. Jonathan Kelly leads the Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies. His group carries out research primarily in the areas of robotic perception, planning, and manipulation. Prof. Kelly holds a Canada Research Chair (Tier II) in Collaborative Robotics and was Dean's Catalyst Professor (an early-career award for research excellence) from 2018 to 2021. Prior to joining the University of Toronto, he was a postdoctoral fellow in CSAIL at MIT, working with Prof. Nick Roy. He received his PhD degree in 2011 from the University of Southern California under the supervision of Prof. Gaurav Sukhatme. At USC, he was a member of the first cohort of Annenberg Fellows. Although he lives in Toronto, he still sneaks back to Los Angeles to go scuba diving whenever he can.
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_ShCRcm1iTrq6ubaaY-8P5QLocation: Ronald Tutor Hall of Engineering (RTH) - 115
WebCast Link: https://usc.zoom.us/webinar/register/WN_ShCRcm1iTrq6ubaaY-8P5Q
Audiences: Everyone Is Invited
Contact: Department of Computer Science
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Epstein Institute - ISE 651 Seminar
Tue, Oct 04, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hanzhang Qin, Postdoctoral Scientist, Amazon
Talk Title: A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility
Host: Dr. John Carlsson
More Information: October 4, 2022.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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MFD Seminar: Mathematical Design of Energy Materials
Tue, Oct 04, 2022 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Professor Ananya Balakrishna , WiSE Gabilan Assistant Professor and Assistant Professor of Aerospace and Mechanical Engineering, University of Southern California
Talk Title: MFD Distinguished Lecture Series: Mathematical Design of Energy Materials
Abstract: "We live in a world in constant need of better materials for energy conversion and storage. We need to not only discover energy materials with enhanced properties, but we need to do so urgently - that is, there is a need to establish a quantitative design framework to accelerate materials development. Finding design principles to improve material properties is the focus of my research group. In my talk, I will present our research on, first, how microstructural instabilities and fundamental material constants contribute to hysteresis in soft magnets. We have developed a coercivity tool that, for the first time, combines micromagnetics and nonlinear stability analysis to predict hysteresis in magnetic alloys and, thereby, provides crucial insights into the longstanding
permalloy problem in soft magnets. Second, I will share some of our work on crystallographically designing microstructures to mitigate degradation in intercalation materials. While suppressing chemo-mechanical degradation has been a longstanding problem, our ongoing work initiates a new line of research by drawing inspiration from shape memory alloys to crystallographically design structural transformations. Finally, I will conclude by showing some ongoing work on designing microstructures in photo-induced phase transformation materials. These materials undergo extreme deformations on exposure to light and are an emerging class of phase transformation materials with applications in remote actuation. Throughout, I will emphasize how developing multi-physics and multi-scale mathematical methods (e.g., phase-field methods, phase-field crystal methods) allows my group to answer new questions about how microstructures affect material properties."
Biography: Ananya Renuka Balakrishna joined the Department of Aerospace and Mechanical Engineering at USC as an Assistant Professor in Fall 2020. Prior to joining USC, she pursued postdoctoral research as a Lindemann Fellow at MIT (Department of Materials Science), and at the University of Minnesota (Aerospace Engineering and Mechanics). Ananya received her PhD in Solid Mechanics and Materials Engineering from the University of Oxford. Broadly, her research focuses on developing mathematical models to investigate the links between material instabilities, microstructures, and properties in energy-storage and functional materials.
Host: Mork Family Department of Chemical Engineering and Materials Science
More Information: Ananya Balakrishna Seminar Flyer 10.4.22.pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: Anthony Tritto
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Six Sigma Black Belt
Wed, Oct 05, 2022 @ 08:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Everyone Is Invited
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
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AME Seminar
Wed, Oct 05, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Beverly McKeon, Caltech
Talk Title: What Makes Turbulence Tick?
Abstract: In this lecture, I will utilize the classical approaches and tools of the modern day -“ theoretical analysis and data-driven methods, respectively -“ together with novel laboratory experiments to illuminate key features of nonlinear interactions in the Navier-Stokes equations. Focusing on a spatio-temporal representation of turbulence near walls -“ an omnipresent phenomenon in large-scale transport and transportation - interscale interactions are identified and quantified, then reduced to key elements responsible for sustaining turbulence. Methods to obtain data-driven representations of both linear and nonlinear dynamics will be discussed, along with some implications for the modeling of wall turbulence. The work has benefited from funding by the US ONR and AFOSR over a period of years, which is gratefully acknowledged.
Biography: Beverley J. McKeon is the Theodore von Karman Professor of Aeronautics at the Graduate Aerospace Laboratories at Caltech (GALCIT) and former Deputy Chair of the Division of Engineering & Applied Science. Effective January 2023, she will be a Professor of Mechanical Engineering at Stanford University. She received her B.A., M.A. and M.Eng. from the University of Cambridge in the United Kingdom, and an M.A. and Ph.D. in Mechanical and Aerospace Engineering from Princeton University under the supervision of Lex Smits. She completed postdoctoral research and a Royal Society Dorothy Hodgkin Fellowship at Imperial College London before arriving at Caltech in 2006. Her research interests include interdisciplinary approaches to manipulation of boundary layer flows using morphing surfaces, fundamental investigations of wall turbulence and the influence of the wall at high Reynolds number, the development of resolvent analysis for modeling turbulent flows, and assimilation of experimental data for efficient low-order flow modeling. Prof. McKeon is a Fellow of the APS and the AIAA and the recipient of a Vannevar Bush Faculty Fellowship from the DoD in 2017, the Presidential Early Career Award (PECASE) in 2009 and an NSF CAREER Award in 2008 as well as Caltechs Shair Program Diversity Award, Graduate Student Council Excellence in Mentoring Award and Northrop Grumman Prize for Excellence in Teaching. She currently serves as co-Lead Editor of Physical Review Fluids, as Physical Sciences co-captain on the National Academies Decadal Survey on Biological and Physical Sciences Research in Space 2023-32, and on the editorial board of the Annual Review of Fluid Mechanics, and is a past editor-in-chief of Experimental Thermal and Fluid Science. She is the current Chair, and APS representative, of the US National Committee on Theoretical and Applied Mechanics.
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Seaver Science Library (SSL) - 202
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Six Sigma Black Belt
Thu, Oct 06, 2022 @ 08:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Everyone Is Invited
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
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NL Seminar-Acquiring and Understanding Cross Task Generalization with Diverse NLP Tasks
Thu, Oct 06, 2022 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Qinyuan Ye, USC/ISI
Talk Title: Acquiring and Understanding Cross Task Generalization with Diverse NLP Tasks
Series: NL Seminar
Abstract: REMINDER
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
In person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.
Humans can learn and perform a new language task more efficiently than machines, when they are provided with either task instructions or only a few examples for that task. We believe such learning efficiency is partly achieved by accumulating past learning experiences, i.e., learning to learn with previously seen tasks. We refer to such capability as cross task generalization and envision it to be an integral piece towards generalist NLP systems.
In this talk, I will present our recent efforts in acquiring and understanding cross task generalization with diverse NLP tasks 1. To build a learning environment for acquiring and evaluating cross-task generalization, we construct NLP Few shot Gym, a repository of 160 few shot tasks collected from open access NLP datasets, converted to a unified text to text format, and covering diverse formats, goals and domains. We further introduce CrossFit, a few shot learning challenge that systematically evaluates an algorithms ability to quickly learn new tasks. With these resources, we conduct an empirical analysis with multi task learning and meta learning approaches, which provides fresh insights. 2. To better understand how models learn transferable skills to achieve cross task generalization, we develop task level mixture of expert models that explicitly emulates the behavior of accumulating skills and recomposing them when encountering a new task. Our empirical results suggest that training task level mixture of experts can alleviate negative transfer and achieve better few shot performance on unseen tasks; further we find that the learned routing decisions and experts partially rediscover human categorization of NLP tasks.
Biography: Qinyuan Ye is a fourth year CS Ph.D. student at University of Southern California, advised by Prof. Xiang Ren. Her research interests lie in natural language processing. In particular she is interested in approaches that reduce human annotation efforts, including methods leveraging distant supervision, high level human supervision for example explanations, instructions, and metalearning. Prior to USC, she was an undergraduate student at Tsinghua University, majoring in Automation.
Host: Jon May and Meryem M'hamdi
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=hpIohClvinsLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://www.youtube.com/watch?v=hpIohClvins
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Six Sigma Black Belt
Fri, Oct 07, 2022 @ 08:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Everyone Is Invited
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
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MHI ISSS Seminar - Dr. Elad Alon, Friday, October 7 at 2pm in EEB 132 and Zoom
Fri, Oct 07, 2022 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Elad Alon, CEO, Blue Cheetah Analog Design
Talk Title: Reshaping the A/MS Design Industry with Generators
Series: Integrated Systems
Abstract: The semiconductor industry is in the midst of multiple technological, societal, and market trends - including a resurgence of customized silicon solutions, global capacity shortages, and the end of scaling derived reductions in cost per transistor - that are all aligning to drive surging demand for IC
designs. Accordingly, demand has never been higher for the A/MS components / sub-systems underlying all modern chips, and yet the supply of A/MS design engineers has at best remained flat over the last ~10- 20 years. In this talk I will describe how generator-based design - where we algorithmically codify and
capture expert engineers' methodologies - allow us to "force multiply" the efforts of expert designers and rapidly realize customized, process-portable A/MS designs.
Biography: Dr. Elad Alon is the CEO and co-founder of Blue Cheetah Analog Design, which is leveraging generator-based design methodologies to rapidly deliver tailored A/MS designs to meet our customers' diverse needs. He is an Adjunct Professor at UC Berkeley in the EECS Department, where he was a Full Professor until Jul. 2021. He has served as an advisor or consultant to many semiconductor and electronics companies, including Lion Semiconductor (acquired by Cirrus Logic), Ayar Labs, Intel, Xilinx, Cadence, Wilocity (acquired by Qualcomm), and Cadence. He has been recognized with multiple best-paper (from the ISSCC, VLSI, and CICC conferences)
as well as teaching awards, has led several multi-institutional research programs, and was elevated to the rank of IEEE Fellow in 2020.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Info: Meeting ID: 947 5819 7738, Passcode: 062790
More Information: Abstract and Bio-Oct 7-Alon.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: Meeting ID: 947 5819 7738, Passcode: 062790
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Epstein Institute - ISE 651 Seminar
Tue, Oct 11, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Krishnakumar Balasubramanian, Assistant Professor, Department of Statistics, University of California, Davis
Talk Title: High-dimensional Inference with Stochastic Approximation Algorithms
Host: Prof. Suvrajeet Sen
More Information: October 11, 2022.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Machine Learning Center Seminar: Furong Huang (University of Maryland) - Trustworthy Machine Learning in Complex Environments
Wed, Oct 12, 2022 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Furong Huang, University of Maryland
Talk Title: Trustworthy Machine Learning in Complex Environments
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: With the burgeoning use of machine learning models in an assortment of applications, there is a need to rapidly and reliably deploy models in a variety of environments. These trustworthy machine learning models must satisfy certain criteria, namely the ability to: (i) adapt and generalize to previously unseen worlds although trained on data that only represent a subset of the world, (ii) allow for non-iid data, (iii) be resilient to (adversarial) perturbations, and (iv) conform to social norms and make ethical decisions.
In this talk, towards trustworthy and generally applicable intelligent systems, I will cover some reinforcement learning algorithms that achieve fast adaptation by guaranteed knowledge transfer, principled methods that measure the vulnerability and improve the robustness of reinforcement learning agents, and ethical models that make fair decisions under distribution shifts.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Furong Huang is an Assistant Professor of the Department of Computer Science at University of Maryland. She works on statistical and trustworthy machine learning, reinforcement learning, graph neural networks, deep learning theory and federated learning with specialization in domain adaptation, algorithmic robustness and fairness. Furong is a recipient of the NSF CRII Award, the MLconf Industry Impact Research Award, the Adobe Faculty Research Award and three JP Morgan Faculty Research Awards. She is a Finalist of AI in Research - AI researcher of the year for Women in AI Awards North America 2022. She received her Ph.D. in electrical engineering and computer science from UC Irvine in 2016, after which she completed postdoctoral positions at Microsoft Research NYC.
Host: Yan Liu
Location: Seeley Wintersmith Mudd Memorial Hall (of Philosophy) (MHP) - 101
Audiences: Everyone Is Invited
Contact: Department of Computer Science
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EE599: Causal Learning course: Casual Bandits
Wed, Oct 12, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ali Tajer, Rensselaer Polytechnic Institute
Talk Title: Causal Bandits
Abstract: In this talk, we provide an overview of the causal bandit problems. The purpose of causal bandit settings is to formalize theoretically-principled frameworks for the experimental design when the experiments involve an array of parameters that causally affect one another. The key objective of causal bandits is to leverage causal relationships to design effective experiments judiciously. Designing causal bandit algorithms critically hinges on the extent of information available about the (i) causal structure and (ii) the interventional distributions. Based on the availability of information on each of these two dimensions, there are, broadly, four possible model combinations. The existing literature, for the most part, focuses on settings in which the interventional distributions are known (with or without knowing the causal structure). First, we provide an overview of the existing literature on the existing literature. Secondly, motivated by the fact that acquiring the interventional distributions is often infeasible, we address the following question: is it possible to achieve the optimal regret scaling rates without knowing the interventional distributions? We address this question affirmatively in the case of linear structural equation models when the causal structure is known. We discuss the design and performance of algorithms for the frequentist and Bayesian settings.
Biography: Ali Tajer received the B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology in 2002 and 2004, respectively. During 2007-2010 he was with Columbia University, where he received an M.A degree in Statistics and a Ph.D. degree in Electrical Engineering, and during 2010-2012 he was with Princeton University as a Postdoctoral Research Associate. He is currently an Associate Professor of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute. His research interests include mathematical statistics, statistical signal processing, and network information theory, with applications in wireless communications and power grids. His recent publications include an edited book entitled Advanced Data Analytics for Power Systems (Cambridge University Press, 2021). He received an NSF CAREER award in 2016 and AFRL Faculty Fellowship in 2019. He is currently serving as an Associate Editor for the IEEE Transaction on Information Theory and an Associate Editor for the IEEE Transactions on Signal Processing. In the past, he has served as an Editor for the IEEE Transactions on Communications, a Guest Editor for the IEEE Signal Processing Magazine, an Editor for the IEEE Transactions on Smart Grid, an Editor for the IET Transactions on Smart Grid, and as a Guest Editor-in-Chief for the IEEE Transactions on Smart Grid -“ Special Issue on Theory of Complex Systems with Applications to Smart Grid Operations.
Host: Urbashi Mitra; Password for link: 114454
More Info: https://usc.zoom.us/j/94255391488?pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09
Location: Virtual
Audiences: Everyone Is Invited
Contact: Susan Wiedem
Event Link: https://usc.zoom.us/j/94255391488?pwd=cGoyOVoxWnc3K1RTeVcvYjlWOEJPQT09
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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, Oct 12, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Byron Boots, Paul G. Allen School of Computer Science and Engineering at the University of Washington
Talk Title: Machine Learning for Agile Off-Road Autonomous Driving
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The main goal of this talk is to illustrate how machine learning can start to address some of the fundamental challenges involved in designing intelligent robots. I'll start by discussing off-road driving tasks that require impressive sensing, speed, and agility to complete. I will focus on how machine learning can be combined with prior knowledge and structure to build effective solutions to robotics control problems in this domain. Along the way I'll introduce new tools from reinforcement learning and online learning and show how theoretical insights help us to overcome some of the practical challenges involved in learning on real-world platforms.
Biography: Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. Byron's group performs fundamental and applied research in machine learning, artificial intelligence, and robotics with a focus on developing theory and systems that tightly integrate perception, learning, and control. His work has been applied to a range of problems including localization and mapping, motion planning, robotic manipulation, quadrupedal locomotion, and high-speed navigation. Byron has received several awards including "Best Paper" Awards from ICML, AISTATS, RSS, and IJRR. He is also the recipient of the RSS Early Career Award, the DARPA Young Faculty Award, the NSF CAREER Award, and the Outstanding Junior Faculty Research Award from the College of Computing at Georgia Tech. Byron received his PhD from the Machine Learning Department at Carnegie Mellon University
Host: Somil Bansal, somilban@usc.edu
Webcast: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/98083929768?pwd=SUJreHk0N0ZXbk5QZ1ZPUkRlM3FmZz09
Audiences: Everyone Is Invited
Contact: Talyia White
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ECE Seminar: High-Assurance Design Methods for Trustworthy Autonomous Cyber-Physical Systems
Tue, Oct 18, 2022 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Pierluigi Nuzzo, Ming Hsieh Dept of ECE, USC Viterbi School of Engineering
Talk Title: High-Assurance Design Methods for Trustworthy Autonomous Cyber-Physical Systems
Abstract: Correctness and safety assurance is of utmost importance in mission-critical systems for various applications, for example, in avionics, automobiles, robotics, and manufacturing. In these systems, increasingly more sophisticated tasks that were previously allocated to humans are expected to be performed by software, including modern artificial intelligence (AI) methods. One of the biggest challenges to full autonomy is arguably in showing that these AI and autonomous software functions will still satisfy the stringent safety and correctness requirements of mission-critical systems in uncertain or unpredictable environments. In this talk, I will introduce our approach toward enhancing design-time assurance for trustworthy autonomous cyber-physical systems. I will present synthesis methods for correct-by-construction design of optimal control and reinforcement learning policies in uncertain and unknown environments with provable guarantees on the satisfaction of complex missions, expressed by temporal logic specifications. I will then introduce the rich specification formalism of stochastic assume-guarantee contracts for compositional, quantitative requirement analysis and system verification under uncertainty. Finally, I will discuss how stochastic contracts can provide the semantic foundation for the automated construction of assurance cases, structured arguments about system dependability, which can accelerate system certification and help transition from a process-driven to a property-driven certification approach.
Biography: Pierluigi Nuzzo is an Assistant Professor and the Kenneth C. Dahlberg Early Career Chair in the Department of Electrical and Computer Engineering at USC, where he is also the Associate Director of the Center for Autonomy and Artificial Intelligence. He received the PhD in Electrical Engineering and Computer Sciences from UC Berkeley, and BS and MS degrees in Electrical and Computer Engineering from the University of Pisa and the Sant'Anna School of Advanced Studies in Pisa, Italy. Before joining UC Berkeley, he held research positions at the University of Pisa and IMEC, Leuven, Belgium, working on analog and mixed-signal circuit design. His interests focus on methodologies and tools for high-assurance design of cyber-physical systems and systems-on-chip, including the application of formal methods and optimization theory to problems in embedded and cyber-physical systems, electronic design automation, requirement engineering, security, and artificial intelligence. He received the 2022 Early-Career Award from the IEEE Technical Committee on Cyber-Physical Systems, the DARPA Young Faculty Award in 2020, the NSF CAREER Award in 2019, and best paper and design competition awards from the International Conference on Formal Methods and Models for System Design (MEMOCODE), the International Conference on Cyber-Physical Systems (ICCPS), the Design Automation Conference (DAC) and the International Solid-State Circuit Conference (ISSCC). His awards also include the IBM PhD Fellowship, the UC Berkeley Outstanding Instructor Award, and the UC Berkeley EECS David J. Sakrison Memorial Prize for his doctoral research.
Host: Professor Richard M. Leahy (leahy@sipi.usc.edu)
Webcast: https://usc.zoom.us/j/91207739138?pwd=aDVQOXRwNUZyMm5DYXhvTTM5K0Z1dz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/91207739138?pwd=aDVQOXRwNUZyMm5DYXhvTTM5K0Z1dz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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***NO EPSTEIN INSTITUTE - ISE 651 SEMINAR (DUE TO INFORMS)***
Tue, Oct 18, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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MFD Seminar: Mechanisms for Diffusion Dependent Interfacial Strain: New Insights for Ultrahigh Temperature In Situ TEM
Tue, Oct 18, 2022 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Professor Shen J. Dillon, Professor of Materials Science and Engineering, University of California, Irvine
Talk Title: Mechanisms for Diffusion Dependent Interfacial Strain: New Insights for Ultrahigh Temperature In Situ TEM
Abstract: Series: MFD Distinguished Lecture Series
Polycrystals can exhibit high-temperature interface-mediated strain in response to an externally applied stress, such as creep, superplasticity, or hot-press sintering, or internal stress, such as stress relaxation at interfaces during thermal cycling or oxide scale growth, fission bubble growth, and densification during sintering. Such problems have mostly been analyzed and treated in the context of purely diffusional models. The diffusional flux, however, is one of only three necessary steps or conditions required for diffusional dependent interfacial strain, the other two include interfacial dislocation nucleation and the emission and absorption of point defects at the interfacial dislocations. The prevalence of diffusional rate limited models results from somewhat unsubstantiated assumptions within the early literature along with the non-uniqueness of the various rate-limiting kinetic models, i.e., disparate models often fit isothermal kinetic data obtained from polycrystalline experiments equally well.
Our group developed a laser heating-based approach for ultrahigh temperature in situ transmission electron microscopy (TEM) during small-scale mechanical testing. This approach enables a more direct characterization of interfacial strain kinetics and thermodynamics at individual grain boundaries, which provides an improved basis for evaluating the high-temperature deformation mechanisms. This talk will present experiments that reveal grain boundary dislocation nucleation limits interfacial strain kinetics in many systems up to relatively large stresses. Based on the experimental observations, new models for sintering and grain boundary creep are developed to account for the appropriate mechanism. These are demonstrated to fit experimental data well, predict broad trends in the literature, and provide explanations for several poorly understood phenomena within the sintering and creep literature. The talk will conclude by discussing the broader implications of the new scientific understanding.
Biography: Shen J. Dillon is a Professor in the Department of Materials Science and Engineering at the University of California Irvine. He received his B.S. and then Ph.D. in Materials Science and Engineering from Lehigh University in 2007. He began as an Assistant Professor at the University of Illinois at Urbana-Champaign in 2009 and joined the faculty at UC Irvine in 2021. His scientific interests relate to understanding the key role played by inorganic interfacial structure-property relationships in affecting the performance of systems in extreme environments. Much of his recent work relates to developing and applying novel in situ characterization techniques that can be applied to understanding the dynamic properties of materials and their interfaces. He is the author of over 100 articles and was a recipient of the 2011 Department of Energy Early Career Award, the 2013 National Science Foundation CAREER Award, and the 2015 American Ceramic Society's Robert L. Coble Award for Young Scholars.
Host: Professor Branicio, Mork Family Department of Chemical Engineering and Materials Science
More Information: Shen Dillon Seminar Flyer 10.18.22 (1).pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: Anthony Tritto
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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, Oct 19, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Abhishek Cauligi, Jet Propulsion Laboratory
Talk Title: Enabling Long Range Autonomy for the Next Generation of Spacecraft Robotic Missions
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Surface rovers have a rich history of use for planetary body exploration, but current rover missions are limited to low operational speeds and require significant ground-in-the-loop management and teleoperation to compute safe paths for the rovers to follow. However, the next generation of proposed planetary surface rover missions require significantly faster operating speeds in order to accomplish the mission tasks and objectives, thereby making autonomy a key enabling technology for such missions. This talk will discuss the challenges ahead in developing, validating, and safely deploying autonomy algorithms for the next generation of spacecraft robotic missions. The first half of this talk will focus on the autonomy architecture for NASA's Cooperative Autonomous Distributed Robotic Explorers (CADRE) mission, a technology demonstration mission that will deliver a team of autonomous rovers to the Moon's Reiner Gamma region in 2024. The latter half of the talk will focus on how recent advances in bridging data-driven approaches with nonlinear optimization can allow for embedding sophisticated planning and decision making capabilities on resource-constrained autonomous systems.
Biography: Abhishek Cauligi is a Robotics Technologist with the Surface Mobility Group within the Robotics section of NASA's Jet Propulsion Laboratory. He received his B.S. in Aerospace Engineering from the University of Michigan - Ann Arbor in 2016 and his PhD. in Aeronautics and Astronautics from Stanford University under the supervision of Prof. Marco Pavone in 2021, where he was a recipient of the NASA Space Technology Research Fellowship (NSTRF/NSTRGO). His research interests lie in leveraging recent advances in nonlinear optimization, machine learning, and control theory towards planning and control for complex spacecraft robotic systems.
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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AME Seminar
Wed, Oct 19, 2022 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Simon Park, University of Calgary
Talk Title: Sensing and Monitoring using Nanocomposite Sensors and Hybrid Copper Conductive Inks
Abstract: Highly accurate, miniaturized components that consist of a variety of materials will play key roles in the future development of a broad spectrum of products, such as wearable devices, lab-on-chips, subminiature actuators and sensors. With the advent of the Internet of Things (IoTs) and Industrie 4.0, the development of miniature and reliable devices will be far-reaching in the enhancement of quality of life and economic growth.
Smart polymeric nanocomposites are promising new materials applicable as media for nano-patterned surfaces. Much attention is being paid to carbon-based nanoparticles as fillers in polymer matrices, due to their outstanding mechanical, electrical and thermal properties. In particular, carbon nanotubes (CNTs) and graphenes are effective in the fabrication of electrically and thermally conductive polymer composites compared to metallic particles or carbon black, mainly due to their high aspect ratios (i.e. ~100-1000).
The sensors consisted of polymer reinforced with multi-walled carbon nanotubes (MWCNTs)/graphenes using a variety of manufacturing techniques. The sensors were electrically poled to generate piezoelectric phases. Both the piezoresistive and piezoelectric characteristics of the nanocomposite were utilized for improved performance of the sensors.
Another important aspect is cost effective manufacturing of conductive electrode patterns onto flexible substrates is vital for multifunctional and flexible systems. Conventional chemical etching, vacuum deposition and electrodeless plating are expensive and potentially hazardous to flexible substrates. Others have used metallic nanoparticle inks, such as silver nanoparticles, through inkjet printing, but the high cost of silver nanoparticles prevents mass production. We have recently developed a simple method to prepare hybrid copper-silver conductive tracks through flash light sintering. We demonstrate some of examples of the sensors and hybrid copper electrodes developments.
Biography: Currently Simon S. Park is a professor at the Schulich School of Engineering, Dept. of Mechanical and Manufacturing Engineering, University of Calgary. He is a professional engineer in Alberta, and is an associate member of CIRP (Int. Academy of Production Engineers) from Canada. Dr. Park received bachelor and masters degrees from the University of Toronto, Canada. He then continued his PhD at the University of British Columbia, Canada. He has worked in several companies including IBM manufacturing where he was a procurement engineer for printed circuit boards and Mass Prototyping Inc. dealing with rapid prototyping systems. In 2004, Dr. Park formed the Micro Engineering, Dynamics, and Automation Laboratory (MEDAL, www.ucalgary.ca/medal) to investigate the synergistic integration of both subtractive and additive processes that uniquely provide productivity, flexibility and accuracy to the processing of complex components. His research interests include micro machining, nano engineering, CNT nanocomposites, and alternative energy applications. He has also founded several start-up companies in sensing and oil extractions. He held a strategic chair position in AITF Sensing and monitoring. He is also an associate editor of the Journal of Manufacturing Processes, SME (Elsevier) and International Journal of Precision Engineering and Manufacturing-Green Technology (Springer). Currently, he is directly supervising 40 students and scholars.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09Location: Virtual Seminar
WebCast Link: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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NL Seminar- Understanding and Improving Learning through Inference with Large Language Models
Thu, Oct 20, 2022 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sewon Min, University of Washington
Talk Title: Understanding and Improving Learning through Inference with Large Language Models
Series: NL Seminar
Abstract: THIS TALK WILL NOT BE RECORDED, IT WILL BE BROADCAST LIVE ONLY*
REMINDER:
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
In person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.
Language models are capable of learning at inference also referred to as in context learning, learning a new task by conditioning on k examples and making a prediction for a new input with no parameter updates. While impressive, models suffer from high variance and low worst case accuracy. Moreover, we do not understand how or why in context learning works. In the first part of the talk, I will introduce new methods that lead to significant performance gains by reducing variance and improving worst case accuracy. I will present a new inference method as well as a new training method, of which combination enables the model to outperform a 230x bigger language model. In the second part of the talk, I will show that in context learning in fact works very differently from conventional learning: the model does not benefit from the correctly paired training data, but rather benefit from the correct specification of the independent distribution of inputs and labels. Finally, I will conclude the talk with lessons learned, limitations and avenues for future work.
Biography: Sewon Min is a Ph.D. student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, advised by Prof. Luke Zettlemoyer and Prof. Hannaneh Hajishirzi. She is also a part time visiting researcher at Meta AI. Her research is in the area of natural language processing and machine learning. Her work specifically focuses on question answering, natural language understanding, knowledge representation and building general purpose language understanding models. She is a recipient of the 2022 JP Morgan Ph.D. Fellowship. She has co organized multiple workshops and tutorials at ACL, EMNLP, NeurIPS and AKBC, including a workshop on Machine Reading for Question Answering, a competition on Efficient Open domain Question Answering, a workshop on Representation Learning for NLP, workshop on Semiparametric Methods in NLP, and a tutorial on Zero and Few shot Learning with Pretrained Language Models. Prior to UW, she obtained a B.S. degree in Computer Science & Engineering from Seoul National University.
Host: Jon May and Meryem M'hamdi
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/94452797669Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/94452797669
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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MHI ISSS Seminar - Dr. Kamran Entesari, Friday, Oct. 21st at 2pm in EEB 132 and via Zoom
Fri, Oct 21, 2022 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kamran Entesari, Texas A&M University
Talk Title: Recent Advances in Millimeter-wave Silicon Photonics Circuits for Wireless Communications
Series: Integrated Systems
Abstract: Nowadays, continuously growing wireless traffic shapes the progress in the wireless communication systems. Therefore, next generation of wireless communication systems are actively
investigated to accommodate expanding data traffic of the future. As one of the promising candidates, silicon photonics devices and circuits are able to improve the performance of the future wireless system.
In this seminar, potential hybrid-integrated mm-wave silicon photonics receivers for future wireless communication systems are explored. The proposed mm-wave silicon photonics reconfigurable receiver front-end can be programmed as either a mm-wave band-pass filter (BPF) for channel selection or a mmwave notch filter for jammer rejection in adjacent and alternate channels within 20-43.5 GHz frequency range. This photonically-assisted mm-wave receiver is optimized for minimum noise figure (NF), maximum linearity or third-order input intercept point (IIP3) and maximum signal to noise ratio (SNR) by optical modulator bias control and optical amplification. Meanwhile, silicon photonics devices are
vulnerable to process and temperature variations. As a result, they require manual calibration, which is expensive, time consuming, and prone to human errors. Therefore, precise automatic calibration solutions with modified monitor-based silicon photonic filter structures are demonstrated and employed in the mmwave silicon photonics receiver. Also, thermal crosstalk effect in the photonic devices is investigated, and substrate thinning is proposed to suppress this effect and reduce calibration time to less than half. The proposed monitor-based tuning method compensates fabrication variations and thermal crosstalk by controlling micro-heaters as tuning elements individually using electrical monitors. This approach
successfully demonstrates calibration and dynamic tuning of silicon photonics filters in the mm-wave receiver from severely degraded initial magnitude response to a well-defined magnitude response.
Biography: Kamran Entesari received his Ph.D. degree from University of Michigan Ann Arbor, in computer Engineering at
Texas A&M University, College Station, where he is currently a Professor. His research interests include the design of RF/mm-wave integrated circuits and systems, and integrated RF/mm-wave photonics for wireless communications and sensing.
Prof. Entesari was a recipient of the 2017 and 2018 Qualcomm Faculty Award, and the 2011 National Science Foundation CAREER Award. He was the corecipient of the 2009 Semiconductor Research Corporation Design Contest Second Place Award, the Best
Student Paper Award of the IEEE RFIC Symposium in 2014 (second place), the IEEE Microwave Theory and Techniques Society award in 2011 (third place), and the IEEE Antennas and Propagation Society award in 2013 (Honorable Mention). He is currently a Technical Program Committee Member of the IEEE RFIC Symposiums and was an Associate Editor of the IEEE Microwave and Wireless Components Letters and a Member of Editorial Board for IEEE Solid-State Circuits Letters. He has published more than 150 peerreviewed
IEEE journal and conference papers.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Info: Meeting ID: 928 5171 5526, Passcode: 638839
More Information: Abstract and Bio-Oct 21-Entesari.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: Meeting ID: 928 5171 5526, Passcode: 638839
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Six Sigma Green Belt for Process Improvement
Tue, Oct 25, 2022 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: TBD, TBD
Talk Title: Six Sigma Green Belt
Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course).
Host: Executive Education
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
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MHI Photonics Seminar - Ayman Abouraddy, Tuesday, October 25th at 3pm in MCB 102
Tue, Oct 25, 2022 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ayman F. Abouraddy, University of Central Florida
Talk Title: Space-time wave packets: A new frontier for structured light
Series: Photonics Seminar Series
Abstract: Exercising control over the spatial degrees of freedom of the optical field has continued to yield breakthroughs over the past few decades, ranging from the discovery of Bessel beams and beams endowed with orbital angular momentum, to optical tweezers and traps, and the manipulation of the field in multimode optical fibers. Separately, but in parallel with these efforts, ultrafast pulse shaping has revolutionized our control over the temporal degree of freedom of the optical field. The spatial and temporal realms in optics have led for the most part independent lives with few examples of creative intersections. In this talk I show that precise, joint sculpting of the spatial and temporal degrees of freedom of optical fields - rather than modulating each separately - yields a new class of pulsed beams that I call 'space-time' (ST) wave packets. Surprising and useful optical behaviors are exhibited by ST wave packets when freely propagating or when interacting with photonic devices, leading to a new frontier for the study of structured light. I will share our recent experimental and theoretical results from this rapidly emerging topic and sketch potential applications that could benefit from ST wave packets.
Biography: Ayman F. Abouraddy received the B.S. and M.S. degrees from Alexandria University, Alexandria, Egypt, in 1994 and 1997, respectively, and the Ph.D. degree from Boston University, Boston, MA, in 2003, all in electrical engineering. In 2003 he joined the Massachusetts Institute of Technology (MIT) as a postdoctoral fellow, and then became a Research Scientist at the Research Laboratory of Electronics in 2005. He is the coauthor of more than 130 journal publications, 240 conference presentations, and 70 invited talks; he holds seven patents, and has three patents pending, and is a fellow of the OSA. He joined CREOL, The College of Optics & Photonics, at the University of Central Florida as an assistant professor in September 2008 and was promoted to full professor in August 2017. His recent research interests are in the area of structured light, particularly in the emerging field of space-time optics and photonics, in addition to quantum optics and quantum information processing.
Host: Mercedeh Khajavikhan
More Information: Ayman Abouraddy Flyer.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Epstein Institute - ISE 651 Seminar
Tue, Oct 25, 2022 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Simge Kucukyavuz, Professor, Dept. of Industrial Engineering & Management Sciences, Northwestern University
Talk Title: TBD
Host: Dr. Andres Gomez
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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MFD Seminar With Professor Kelsey Stoerzinger
Tue, Oct 25, 2022 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Professor Kelsey Stoerzinger, Chemical, Biological & Environmental Engineering Assistant Professor, Oregon State University
Talk Title: MFD Seminar With Professor Kelsey Stoerzinger
Series: MFD Distinguished Lecture Series
Biography: Expertise and Interests:
Kelsey Stoerzinger's research interests span the (electro)chemical transformation of molecules into fuels, chemical feedstocks, and recovered resources. Special emphasis is put on the use of abundant elements to drive these reactions in an economical and scalable manner by renewable electricity. Surface science approaches are used to probe the reaction mechanism by in situ and operando X-ray and vibrational spectroscopies. In 2021, she received the NSF CAREER award for her work in seawater electrolysis.
Biography:
Scientist -“ Pacific Northwest National Laboratory (joint appointment, 2018-present)
Linus Pauling Postdoctoral Fellow -“ Pacific Northwest National Laboratory (2016-2018)
Ph.D. -“ Materials Science and Engineering, Massachusetts Institute of Technology, NSF Graduate Research Fellow (2011-2016)
M.Phil. -“ Physics, University of Cambridge, Churchill Fellow (2010-2011)
B.S. -“ Materials Science and Engineering, Northwestern University (2006-2010)
Host: Professor Sharada, Mork Family Department of Chemical Engineering and Materials Science
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: Anthony Tritto
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Six Sigma Green Belt for Process Improvement
Wed, Oct 26, 2022 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: TBD, TBD
Talk Title: Six Sigma Green Belt
Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course).
Host: Executive Education
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
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Nano Science & Technology seminar - Shaloo Rakheja, Wednesday, Oct. 26th at 10:30am in EEB 248
Wed, Oct 26, 2022 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Shaloo Rakheja, University of Illinois at Urbana-Champaign
Talk Title: Spin dynamics in antiferromagnets and its applications
Series: Nano Science & Technology
Abstract: Antiferromagnets (AFM) materials have ordered spin moments that alternate between individual atomic sites, which gives them a vanishing macroscopic magnetic signature and picosecond intrinsic timescale. Traditionally, AFM materials have played a secondary role to ferromagnets, which are used as active elements in commercial spintronic devices like magnetic sensors and non-volatile magnetic memory. However, it was recently suggested that spin transfer torque could in principle be used to manipulate the magnetic order in AFMs, leading to either stable AFM order precessions for their use as high-frequency oscillators, or switching of the AFM order for their use as magnetic memories.
My presentation will focus on the physics and modeling of electrically driven spin dynamics in thin films of two unique AFMs: Cr2O3, a single-phase magnetoelectric material that can be manipulated solely with electric fields and the Weyl semi-metal Mn3Sn in which spin torque can induce chiral spin rotations. Cr2O3-based ferromagnet-free random access memory has been experimentally demonstrated, while in the case of Mn3Sn, spin torque driven dynamics were found to induce chiral oscillations, from the megahertz to the terahertz frequency range. These materials can overcome the central challenge of manipulating and reading the AFM's order parameter via microelectronics compatible circuitry, thus allowing us to develop antiferromagnetic spintronics along a similar route as ferromagnetic spintronics.
I will discuss my group's recent work in developing new analytic models and numerical techniques to handle the complex domain dynamics across many length scales and time scales in AFM structures. I will use these models to explain recent experimental findings and bridge the gap between physics and applications development. I will conclude my talk by summarizing the limits, challenges, and opportunities of AFM spintronics for future technologies such as high-density, secure nonvolatile memory, compact narrowband terahertz sources, and spike generators.
Biography: Shaloo Rakheja is currently an Assistant Professor in the Electrical and Computer Engineering (ECE) department at the University of Illinois at Urbana-Champaign. She is currently leading the Center for Aggressive Scaling by Advanced Processes for Electronics and Photonics (ASAP) -“ an Industry-University Cooperative Research Center, expected to be launched as a Phase 1 Center by the NSF in 2022. Shaloo is an expert in physics-based modeling of nanoelectronic and magnetic devices for energy-efficient computing and communication. She has developed multi-scale models, spanning from first-principles calculations to circuit-compatible implementations, for enabling materials-to-circuits co-design for a wide range of technologically relevant applications.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Info: https://usc.zoom.us/j/99956388667?pwd=UHZ2bEZSY0FuakM5dGFwcU1GcTB2QT09
More Information: Shaloo Rakheja_10262022.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: https://usc.zoom.us/j/99956388667?pwd=UHZ2bEZSY0FuakM5dGFwcU1GcTB2QT09
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CS Colloquium: Keith Burghardt (USC ISI) - Utilizing Data Analysis To Reduce AI Biases
Wed, Oct 26, 2022 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Keith Burghardt, USC ISI
Talk Title: Utilizing Data Analysis To Reduce AI Biases
Abstract: Biases are erroneous assumptions about data that can lead artificial intelligence (AI) systems to discriminate, policy makers to make harmful decisions, and data scientists to make conclusions that contradict reality. Biases, however, are often challenging to find or remove because they can be subtle and deeply embedded within data. In this talk, I will discuss how data can inadvertently create biases and present my research that aims to reduce them. I will first show how data can enhance biases, including how computer-human interactions can drive algorithmic ranking systems to erroneous conclusions and how anti-vaccine sentiment and hate speech can become prevalent on social media, which can lead to stereotypes embedded in AI language models. I will then discuss methods that reduce biases by utilizing data analysis. I will show how these methods can improve AI fairness and help researchers better understand how large systems, such as institutions and cities, evolve in time. I will conclude the talk by laying out how this work is a step towards the wider goal of AI risk minimization.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Join Zoom Meeting
https://usc.zoom.us/j/93464447234?pwd=ZHlKeFlJVTBHWmhoTS9NRVBqTVV5QT09
Meeting ID: 934 6444 7234
Passcode: 475457
Biography: Burghardt is a Computer Scientist at the USC Information Sciences Institute who specializes in complex science, geospatial analysis, and reducing biases with data analysis. He has papers in journals such as NPJ Computational Materials and Communications Physics, and in conferences, such as ICWSM, ASONAM, and CSCW. Burghardt has been a PI in grants from Amazon and ISI, co-PI in grants from DARPA, and co-organized the Inclusive and Fair Speech Technologies special session at the INTERSPEECH 2022 Conference. Burghardt received a PhD and BS (Magna Cum Laude with High Honors) in Physics at the University of Maryland in 2016 and 2012, respectively.
Host: Vatsal Sharan
Audiences: Everyone Is Invited
Contact: Cherie Carter
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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, Oct 26, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Samuel Coogan, Georgia Institute of Technology
Talk Title: Runtime Assurance for Safe Autonomy from Fast, In-the-Loop Reachability
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: In this talk, we show how efficient reachability methods enable runtime assurance (RTA) for safe autonomy. We focus on interconnected and/or high dimensional systems and we leverage reachability techniques enabled by mixed monotone systems theory. Mixed monotonicity decomposes a dynamical system's vector field into cooperative and competitive elements, resulting in a larger dimensional monotone system for which powerful results from monotone systems theory for, e.g., reachability and invariance are applicable. Notably, these methods offer two key properties: they enable reachable set over-approximations that can be computed very fast for, e.g., inclusion at runtime in feedback controllers, and they scale to high dimensional systems such as neural networks. We demonstrate how both of these appealing features enable RTA mechanisms with provable guarantees for learning-enabled control systems.
Biography: Samuel. Coogan is an associate professor and the Demetrius T. Paris Junior Professor at the Georgia Institute of Technology in the School of Electrical and Computer Engineering and the School of Civil and Environmental Engineering. Prior to joining Georgia Tech in 2017, he was an assistant professor at the University of California, Los Angeles from 2015 to 2017. His research is in the area of dynamical systems and autonomy and focuses on developing scalable tools for verification and control of networked, cyber-physical systems with an emphasis on transportation systems. He received a CAREER Award from the National Science Foundation in 2018, a Young Investigator Award from the Air Force Office of Scientific Research in 2019, and the Donald P Eckman Award from the American Automatic Control Council in 2020.
Host: Pierluigi Nuzzo, nuzzo@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
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Six Sigma Green Belt for Process Improvement
Thu, Oct 27, 2022 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: TBD, TBD
Talk Title: Six Sigma Green Belt
Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course).
Host: Executive Education
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
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NL Seminar
Thu, Oct 27, 2022 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Eric Wallace, University of Cal-Berkeley
Talk Title: Emerging Vulnerabilities in Large-scale NLP Models
Series: NL Seminar
Abstract: Abstract: REMINDER
Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.
If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.
In person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.
The current era of machine learning and natural language processing is dominated by scale modern models use supermassive parameter counts, dataset sizes, and compute budgets. While this scaling undoubtedly unlocks new capabilities and performance improvements, it may also expose new vulnerabilities, risks, and harms. In this talk, I will discuss a series of vulnerabilities that emerge in large scale NLP models that not only expose worrisome security and privacy risks, but also provide new perspectives into how and why the models work. Concretely, I will show how adversaries can extract private training data, steal model weights, and poison training sets, all using limited black box access to models. Throughout the talk, I'll provide a particular focus on insights that we can derive from these attacks, especially regarding the impact of model scaling.
Biography: Eric Wallace is a 4th year PhD student at UC Berkeley advised by Dan Klein and Dawn Song. His research interests focus on making large language models more robust, trustworthy, secure, and private. Eric's work is supported by the Apple Fellowship in AI/ML, and he received the best demo award at EMNLP 2019.
Host: Jon May and Meryem M'hamdi
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=42LNH1dTlggLocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=42LNH1dTlgg
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CEE Seminar Series
Thu, Oct 27, 2022 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Thomas Harmon, University of California Merced
Talk Title: Labor and Automation in California Agriculture (LACA): Equity, Productivity, and Resilience
Abstract: See attached
Host: Amy Childress and Felipe de Barros
More Information: Thomas Harmon Speaker 10272022.pdf
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Salina Palacios
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CS Colloquium: Matteo Sesia (USC Marshall School of Business) - Conformal inference for uncertainty-aware classification
Thu, Oct 27, 2022 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matteo Sesia, USC Marshall School of Business
Talk Title: Conformal inference for uncertainty-aware classification
Series: Computer Science Colloquium
Abstract: Complex machine learning classifiers, including deep neural networks, are sometimes able to achieve very high predictive accuracy, but they are not designed to realistically capture uncertainty or to estimate reliable probabilities. In fact, these models are often overconfident, and this issue can make it challenging for practitioners to accept the use of machine learning algorithms in delicate real-world applications. This talk will describe recent advances in the field of conformal inference which allow us to address the overconfidence of machine learning classifiers. First, this talk will present a powerful and statistically principled methodology for assessing the uncertainty of predictions computed by any pre-trained classification model, in such a way as to account for possible heterogeneity in the levels of uncertainty affecting different individual data points. Then, building upon the previous results, this talk will present a novel methodology for training deep neural networks in such a way as to learn multi-class classification models that are less prone to overconfidence, ultimately leading to even more reliable uncertainty-aware predictions.
Prof. Sesia will give his talk in person at RTH 115 and we will also host the talk over Zoom.
Join Zoom Meeting
https://usc.zoom.us/j/92821217575
Meeting ID: 928 2121 7575
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Matteo Sesia is an assistant professor in the department of Data Sciences and Operation at the USC Marshall School of Business.
Matteo joined USC Marshall in 2020, immediately after earning a PhD in Statistics from Stanford University, where he was advised by Emmanuel Candes. Matteo's research primarily focuses on developing novel methodology for model-free statistical inference with big data, and on developing statistically principled algorithms for uncertainty-aware machine learning.
Host: Yan Liu
Webcast: https://usc.zoom.us/j/92821217575Location: Ronald Tutor Hall of Engineering (RTH) - 115
WebCast Link: https://usc.zoom.us/j/92821217575
Audiences: Everyone Is Invited
Contact: Department of Computer Science
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Six Sigma Black Belt
Mon, Oct 31, 2022 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Black Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Black Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's Black belt exam (administered on the final day of the course).
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Contact: Corporate and Professional Programs
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
