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Conferences, Lectures, & Seminars
Events for April
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ECE-S Seminar - Dr Ruohan Gao
Mon, Apr 03, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Ruohan Gao, Postdoctoral Research Fellow | Department of Computer Science, Stanford University
Talk Title: Multisensory Machine Intelligence
Abstract: The future of Artificial Intelligence demands a paradigm shift towards multisensory perception-”to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision making. While we humans perceive the world by looking, listening, touching, smelling, and tasting, traditional form of machine intelligence mostly focuses on a single sensory modality, particularly vision. My research aims to teach machines to see, hear, and feel like humans to perceive, understand, and interact with the multisensory world. In this talk, I will present my research of multisensory machine intelligence that studies two important aspects of the multisensory world: 1) multisensory objects, and 2) multisensory space. In both aspects, I will talk about how I design systems to reliably capture multisensory data, how I effectively model them with new differentiable simulation algorithms and deep learning models, and how I explore creative cross-modal/multi-modal applications with sight, sound, and touch. In the end, I will conclude with my future plans.
Biography: Ruohan Gao is a Postdoctoral Research Fellow working with Prof. Fei-Fei Li, Prof. Jiajun Wu, and Prof. Silvio Savarese in the Vision and Learning Lab at Stanford University. He obtained his Ph.D. advised by Prof. Kristen Grauman at The University of Texas at Austin and B.Eng. at The Chinese University of Hong Kong. Ruohan mainly works in the fields of computer vision and machine learning with particular interests in multisensory learning with sight, sound, and touch. His research has been recognized by the Michael H. Granof Award which is designated for UT Austin's Top 1 Doctoral Dissertation, the Google PhD Fellowship, the Adobe Research Fellowship, a Best Paper Award Runner Up at British Machine Vision Conference (BMVC) 2021, and a Best Paper Award Finalist at Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
Host: Dr Antonio Ortega, aortega@usc.edu
Webcast: https://usc.zoom.us/j/93551506449?pwd=SzF2UTRRL1ZSQjF4N3VMdDlsOEJwUT09More Information: ECE Seminar Announcement 04.03.2023 Ruohan Gao.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/93551506449?pwd=SzF2UTRRL1ZSQjF4N3VMdDlsOEJwUT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Tian Li (CMU) - Scalable and Trustworthy Learning in Heterogeneous Networks
Mon, Apr 03, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tian Li, CMU
Talk Title: Scalable and Trustworthy Learning in Heterogeneous Networks
Series: CS Colloquium
Abstract: To build a responsible data economy and protect data ownership, it is crucial to enable learning models from separate, heterogeneous data sources without data centralization. For example, federated learning aims to train models across massive networks of remote devices or isolated organizations, while keeping user data local. However, federated networks introduce a number of unique challenges such as extreme communication costs, privacy constraints, and data and systems-related heterogeneity.
Motivated by the application of federated learning, my work aims to develop principled methods for scalable and trustworthy learning in heterogeneous networks. In the talk, I discuss how heterogeneity affects federated optimization, and lies at the center of accuracy and trustworthiness constraints in federated learning. To address these concerns, I present scalable federated learning objectives and algorithms that rigorously account for and directly model the practical constraints. I will also explore trustworthy objectives and optimization methods for general ML problems beyond federated settings.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Tian Li is a fifth-year Ph.D. student in the Computer Science Department at Carnegie Mellon University working with Virginia Smith. Her research interests are in distributed optimization, federated learning, and trustworthy ML. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She received the Best Paper Award at the ICLR Workshop on Security and Safety in Machine Learning Systems, was invited to participate in the EECS Rising Stars Workshop, and was recognized as a Rising Star in Machine Learning/Data Science by multiple institutions.
Host: Dani Yogatama
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Willie Neiswanger (Stanford University) - AI-Driven Experimental Design for Accelerating Science and Engineering
Mon, Apr 03, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Willie Neiswanger, Stanford University
Talk Title: AI-Driven Experimental Design for Accelerating Science and Engineering
Series: CS Colloquium
Abstract: AI-driven experimental design methods have the potential to accelerate costly discovery and optimization tasks throughout science and engineering-”from materials design and drug discovery to computer systems tuning and instrument control. These methods are promising as they provide the intelligent decision making needed for use in complex real-world problems where experiments are time-consuming or expensive, and efficiency is paramount. In the first part of my talk, I will discuss challenges that I encountered while applying these methods to new types of scientific optimization problems being pursued at national labs. I will then introduce an information-based framework for flexible experimental design, which overcomes these challenges by enabling easy customization to new problem settings. This framework is theoretically principled, and has been used by scientists for efficient materials synthesis and optimization in large scientific instruments. Along the way, I will discuss my vision for reliable systems that expand the scope of AI-driven experimental design and make it easier to use, so that it can be put in the hands of scientists, engineers, and other practitioners everywhere.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Willie Neiswanger is a postdoctoral scholar in the Computer Science Department at Stanford University. Previously, he completed his PhD in machine learning at Carnegie Mellon University. He develops machine learning techniques to perform optimization and experimental design in costly real-world settings, where resources are limited. His work spans topics in active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning, and he applies these methods downstream to solve problems in science and engineering. Willie's work has received honors including a Best Paper Award at OSDI'21, and has been published in top machine learning venues (e.g., NeurIPS, ICML, ICLR, AAAI, AISTATS) and natural science journals (e.g., J Chem Physics, J Immunology, Cell Reports, Nucl Fusion). He has also collaborated with the SLAC National Accelerator Laboratory and the Princeton Plasma Physics Laboratory, where his methods have been run live on particle accelerators and tokamak machines for optimization/control tasks.
Host: Dani Yogatama
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE-Controls Faculty Candidate Seminar - Dr Steve Alpern
Tue, Apr 04, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Steve Alpern, Professor, University of Warwick
Talk Title: The Faulty GPS Problem: Optimal Search for Home Node on a Network, with Unreliable Directions
Abstract: Searcher wants to find the Home node on a given Network, but his directions are unreliable. At every branch node of a network Q, a Satnav (GPS) points to the arc leading to the destination, or home node, H - but only with a high known probability p. The pointer is fixed in time, so does not change when a node is revisited. Always trusting the Satnav's suggestion may lead to an infinite cycle. If one wishes to reach H in least expected time, with what probability q=q(Q,p) should one trust the pointer (if not, one chooses randomly among the other arcs)? We call this the Faulty Satnav (GPS) Problem. We also consider versions where the trust probability q can depend on the degree of the current node and a `treasure hunt' where two searchers try to reach H first. The agent searching for H need not be a car, that is just a familiar example -- it could equally be a UAV receiving unreliable GPS information.
This problem has its origin not in driver frustration but in the work of Fonio et al (2017) on ant navigation, where the pointers correspond to pheromone markers pointing to the nest.
Biography: Steve did his AB in Mathematics at Princeton, supervised by Oskar Morgenstern, and his PhD in Ergodic Theory at Courant Institute -“ NYU, under Peter Lax. He moved from ergodic theory to game theory and search theory mid career. After many years at the London School of Economics, he moved to the University of Warwick, where he is Professor of Operational Research.
Host: Dr Petros Ioannou, ioannou@usc.edu | Dr George Papavissilopoulos, yorgos@netmode.ece.ntua.gr
Webcast: https://usc.zoom.us/j/96085498483?pwd=aXJ4U244VHhQOCtIUURDM29mb216UT09More Information: ECE-Controls_Seminar_Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/96085498483?pwd=aXJ4U244VHhQOCtIUURDM29mb216UT09
Audiences: Everyone Is Invited
Contact: John Diaz
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CS Colloquium: Rakshit Trivedi (MIT) - Foundations for Learning in Multi-agent Ecosystems: Modeling, Imitation, and Equilibria
Tue, Apr 04, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rakshit Trivedi, MIT
Talk Title: Foundations for Learning in Multi-agent Ecosystems: Modeling, Imitation, and Equilibria
Series: CS Colloquium
Abstract: The growing presence of AI in critical domains such as information communication, service, financial markets and agriculture requires designing AI systems capable of seamlessly interacting with other AI, with humans and as part of complex systems in a manner that is beneficial to humans. For an AI to be effective in such settings, a key open challenge is for it to have the ability to effectively collaborate across a broad group of interdependent agents (AI or human) in a variety of one or few-shot interactions. A crucial step towards addressing this is to enable rapid development and safe evaluation of AI agents and frameworks that can incorporate the richness and diversity observed in human behaviors and account for various social and economic factors that drives interactions in the multi-agent ecosystems. In this talk, I will set forth the research agenda of real-world in silico design for such AI systems and discuss methodological advancements in this direction. First, I will focus on automated design of central mechanisms tasked to shape the behavior of self-interested agents and drive them towards improving social welfare. I will introduce a novel multi-agent reinforcement learning technique to solve the resulting bi-level optimization problem and present its effectiveness in a simulated market economy. Next, I will discuss the setting where the self-interested agents interact with each other in a strategic manner to form networks and present our approach on discovering the underlying mechanisms that drives these interactions. This approach considers a game-theoretic formalism, and leverages recent advances in inverse reinforcement learning, thereby serving as a preliminary step towards learning models of optimizing mechanisms directly from observed data. Finally, I will focus on the use of AI agents as surrogate for human actors that can provide simulations of real-world complexity and discuss challenges and opportunities on designing AI that is capable of handling the diversity, richness, and noise that is inherent to human behaviors. I will conclude my talk with an outline of my forward-looking vision on this agenda.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Rakshit Trivedi is a Postdoctoral Associate in the Computational Science and Artificial Intelligence Laboratory (CSAIL) at MIT and a Researcher in EconCS at Harvard School of Engineering and Applied Sciences (SEAS). His research focuses on the development of AI that is capable of learning from human experiences, quickly adapt to evolving human needs and achieve alignment with human values. He is further interested in studying the effectiveness of such an AI in the presence of various socio-economic mechanisms. Towards this goal, he is currently leading a set of efforts on developing and evaluating design strategies for building helpful and prosocial artificial agents in mixed-motive settings, in collaboration with Deepmind and Cooperative AI Foundation. Previously, Rakshit completed his Ph.D. at Georgia Institute of Technology, where he focused on learning in networked and multi-agent systems to improve predictive and generative capabilities of downstream applications, by accounting for the structure and dynamics of interactions in such systems.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Photonics Seminar - Antonio Rigol, Tuesday, April 4th at 3pm in EEB 248
Tue, Apr 04, 2023 @ 03:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Marcos Antonio Rigol, Physics, Penn State
Talk Title: Typical eigenstate entanglement entropy as a diagnostic of quantum chaos and integrability
Series: Photonics Seminar Series
Abstract: The typical entanglement entropy of subsystems of random pure states is known to be (nearly) maximal, while the typical entanglement entropy of random Gaussian pure states has been recently shown to exhibit a qualitatively different behavior, with a coefficient of the volume law that depends on the fraction of the system that is traced out. We review evidence that the typical entanglement entropy of eigenstates of quantum-chaotic Hamiltonians mirrors the behavior in random pure states, while that of integrable Hamiltonians mirrors the behavior in random Gaussian pure states. Based on these results, we conjecture that the typical entanglement entropy of Hamiltonian eigenstates can be used as a diagnostic of quantum chaos and integrability. We discuss subtleties that emerge as a consequence of conservation laws, such as particle number conservation, as well as of lattice translational invariance.
Biography: Dr. Rigol is a Professor of Physics at Penn State. Before joining Penn State, he was an Associate Professor of Physics at Georgetown University. Dr. Rigol completed his undergraduate (Summa Cum Laude) and M.Sc. studies at the Institute of Nuclear Sciences and Technology in Havana. He received his Ph.D.
in Physics (Summa Cum Laude) from the University of Stuttgart, and did postdocs at the University of California Davis, the University of Southern California, and the University of California Santa Cruz.
Dr. Rigol research interest is in many-body quantum systems in and out of equilibrium, with a focus on the effect of strong correlations. His research is at the interface between condensed matter physics, ultracold atoms, and statistical mechanics. He is a Fellow of the American Physical Society and of the American Association for the Advancement of Science.
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: Marcos Antonio Rigol Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Epstein Institute - ISE 651 Seminar
Tue, Apr 04, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Yuri Faenza, Associate Professor, Dept. of Industrial Engineering and Operations Research, Columbia University
Talk Title: Stable Matchings in Choice Function Models: Theory and Applications to School Choice
Host: Dr. Giacomo Nannicini
More Information: April 4, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: Evi Micha (University of Toronto) - Fair and Efficient Decision-Making for Social Good
Wed, Apr 05, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Evi Micha, University of Toronto
Talk Title: Fair and Efficient Decision-Making for Social Good
Series: CS Colloquium
Abstract: Algorithms have had a remarkable impact on human lives as they have been used increasingly to automate critical decisions. Consequently, it is more important than ever to design decision-making algorithms that treat people fairly, use limited resources efficiently, and foster social good. To illustrate my research in this direction, I will present two recent examples: in one, we boost the efficiency of COVID testing in a real-world setting, and in the other, we make the selection of citizens' assemblies more representative. Towards the end, I will address the challenging question of algorithmic fairness, making a case that fairness notions emerging from the EconCS literature have far-reaching applications, even to machine learning.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Evi Micha is a Ph.D. candidate in the Computer Science Department at the University of Toronto, advised by Nisarg Shah. She is also an affiliate of the Vector Institute for Artificial Intelligence and a fellow of the Schwartz Reisman Institute for Technology and Society. Her research interests lie at the intersection of computer science and economics, and span areas such as algorithmic fairness and computational social choice.
Host: Sven Koenig
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Wed, Apr 05, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Ram Vasudevan, University of Michigan
Talk Title: Can't Touch This: Real-Time, Provably Safe Motion Planning and Control for High Dimensional Autonomous Systems
Abstract: A key challenge to the widespread deployment of robotic manipulators is the need to ensure safety in arbitrary environments while generating new motion plans in real-time. This talk describes a technique that constructs a parameterized representation of the forward reachable set that it then uses in concert with predictions to enable certified, collision checking. To improve computational speed, this talk describes how to represent this parameterized reachable set using a neural implicit representation without sacrificing any safety guarantees. This approach, which is guaranteed to generate safe behavior, is demonstrated across a variety of different real-world platforms including ground vehicles, manipulators, and walking robots.
Biography: Ram Vasudevan is an associate professor in the Mechanical Engineering and Robotics Departments at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences, an MS degree in Electrical Engineering, and a PhD in Electrical Engineering all from the University of California, Berkeley. He is a recipient of the NSF CAREER Award, the ONR Young Investigator Award, and the 1938E Award from the University of Michigan. His work has received best paper awards at the IEEE Conference on Robotics and Automation, the ASME Dynamics Systems and Controls Conference, and IEEE International Conference on Biomedical Robotics and Biomechatronics, and has been finalist for best paper at Robotics: Science and Systems.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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ECE-S Seminar - Dr Yi Ding
Thu, Apr 06, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Yi Ding, Postdoctoral Associate & NSF Computing Innovation Fellow | CSAIL, MIT
Talk Title: A Holistic View on Machine Learning for Systems
Abstract: Improving computer system performance and resource efficiency are long-standing goals. Recent approaches that use machine learning methods to achieve these goals rely on a predictor that predicts the latency, throughput, or energy consumption of a sub-computation to, for example, aid hardware resource management or scheduling. In this talk, I will present a holistic view on machine learning for systems. I will demonstrate that maximizing machine learning prediction accuracy does not always optimize system behavior. Instead, my research vision focuses on a holistic view on machine learning for systems. The key insight in achieving this vision is understanding the cost structure of systems problems and then making proper tradeoffs between different steps within the process. Based on this vision, I will introduce a couple of machine learning for systems solutions to meet different system goals such as energy and performance. I will conclude the talk with my future directions.
Biography: Yi Ding is an NSF Computing Innovation Fellow and Postdoctoral Associate at MIT CSAIL. Her research interests focus on co-designing machine learning and systems approaches that enhance computer system performance and resource efficiency. She is a recipient of 2020 CRA/CCC/NSF Computing Innovation Fellowship, a Rising Stars in EECS Workshop participant, and a recipient of Meta Research Award. Before MIT, she received her PhD in computer science from the University of Chicago. Website: https://y-ding.github.io/.
Host: Dr Chris Torng, ctorng@usc.edu | Dr Massoud Pedram, pedram@usc.edu
Webcast: https://usc.zoom.us/j/91455259066?pwd=dHdrZnhtRUh2KzhDQnhUZHhaTmQ5QT09More Information: ECE Seminar Announcement 04.06.2023 - Yi Ding.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/91455259066?pwd=dHdrZnhtRUh2KzhDQnhUZHhaTmQ5QT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Maithilee Kunda (Vanderbilt University) - Reasoning with visual imagery: Research at the intersection of autism, AI, and visual thinking
Thu, Apr 06, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Maithilee Kunda, Vanderbilt University
Talk Title: Reasoning with visual imagery: Research at the intersection of autism, AI, and visual thinking
Series: CS Colloquium
Abstract: While decades of AI research on high-level reasoning have yielded many techniques for many tasks, we are still quite far from having artificial agents that can just "sit down" and perform tasks like intelligence tests without highly specialized algorithms or training regimes. We also know relatively little about how and why different people approach reasoning tasks in different (often equally successful) ways, including in neurodivergent conditions such as autism. In this talk, I will discuss: 1) my lab's work on AI approaches for reasoning with visual imagery to solve intelligence tests, and what these findings suggest about visual cognition in autism; 2) how imagery-based agents might learn their domain knowledge and problem-solving strategies via search and experience, instead of these components being manually designed, including recent leaderboard results on the very difficult Abstraction & Reasoning Corpus (ARC) ARCathon challenge; and 3) how this research can help us understand cognitive strategy differences in people, with applications related to neurodiversity and employment. I will also discuss 4) our Film Detective game that aims to visually support adolescents on the autism spectrum in improving their theory-of-mind and social reasoning skills.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Maithilee Kunda is an assistant professor of computer science at Vanderbilt University. Her work in AI, in the area of cognitive systems, looks at how visual thinking contributes to learning and intelligent behavior, with a focus on applications related to autism and neurodiversity. She directs Vanderbilt's Laboratory for Artificial Intelligence and Visual Analogical Systems and is a founding investigator in Vanderbilt's Frist Center for Autism & Innovation.
She has led grants from the US National Science Foundation and the US Institute of Education Sciences and has also collaborated on large NSF Convergence Accelerator and AI Institute projects. She has published in Proceedings of the National Academy of Sciences (PNAS) and in the Journal of Autism and Developmental Disorders (JADD), the premier journal for autism research, as well as in AI and cognitive science conferences such as ACS, CogSci, AAAI, ICDL-EPIROB, and DIAGRAMS, including a best paper award at the ACS conference in 2020. Also in 2020, her research on innovative methods for cognitive assessment was featured on the national news program CBS 60 Minutes, as part of a segment on neurodiversity and employment. She holds a B.S. in mathematics with computer science from MIT and Ph.D. in computer science from Georgia Tech.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE-EP seminar - Dion Khodagholy
Fri, Apr 07, 2023 @ 09:30 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dion Khodagholy, Columbia University
Talk Title: Translational Neuroelectronics
Series: ECE-EP Seminar
Abstract: Our understanding of the brain's physiology and pathology is fueled by sophisticated bioelectronics that enable visualization and manipulation of neural circuits at multiple spatial and temporal resolutions. All components of these bioelectronic devices must be engineered with biocompatibility and clinical translation in mind. Organic electronics offer a unique approach to this device design, due to their mixed ionic/electronic conduction, mechanical flexibility, enhanced biocompatibility, and capability for drug delivery. We design, develop, and characterize conformable, stretchable organic electronic devices based on conducting polymer-based electrodes, particulate electronic composites, high-performance transistors, conformable integrated circuits, and ion-based data communication. We then use these devices in systems neuroscience experiments in animal models and humans to analyze neural network functions and facilitate new discoveries that could improve patient care.
These devices established new experimental paradigms that allowed discovery of novel brain oscillations and elucidated patterns of neural network maturation in the developing brain. Furthermore, these devices were used for intra-operative recording from patients undergoing epilepsy and deep brain stimulation surgeries, highlighting their translational potential. We have also leveraged them to form responsive electrical interventions that target biomarkers for memory consolidation and affect the progression of epilepsy.
To expand beyond neural interfaces to complete devices, we are developing fully-implantable, conformable implantable integrated circuits based on high-speed internal ion-gated organic electrochemical transistors that can perform the entire chain of signal acquisition, processing, and transmission without the need of hard Si-based devices. This multidisciplinary approach has permitted innovation of new organic electronic devices that could be leveraged establish a sustainable track of impactful bioelectronic inventions and address clinical applications such as brain-machine interfaces and therapeutic closed-loop devices.
Biography: Dion Khodagholy is an associate professor in the Department of Electrical Engineering, School of Engineering and Applied Science at Columbia University. He received his Master's degree from the University of Birmingham (UK) in Electronics and Telecommunication Engineering. This was followed by a second Master's degree in Microelectronics at the Ecole des Mines. He attained his Ph.D. degree in Microelectronics at the Department of Bioelectronics of the Ecole des Mines (France). He completed a postdoctoral fellowship as a Simon's Society fellow in systems neuroscience at New York University, Langone Medical Center. He is a recipient of the NSF CAREER award, junior fellow of Simons society, and SEAS Translational Award.
His research aims to use unique properties of materials for the purpose of designing and developing novel electronic devices that allow efficient interaction with biological substrates, and thereby enhancing our understanding of neural networks and brain function.
Host: ECE-Electrophysics
More Information: Dion Khodagholy Seminar Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Daniel Seita (CMU) - Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants
Fri, Apr 07, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Daniel Seita, Carnegie Mellon University
Talk Title: Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants
Series: CS Colloquium
Abstract: The robotics community has seen significant progress in applying machine learning for robot manipulation. However, much manipulation research focuses on rigid objects instead of highly deformable objects such as cables, fabrics, bags, liquids, and plants, which pose challenges due to their complex configuration spaces, dynamics, and self-occlusions. To achieve greater progress in robot manipulation of such diverse deformable objects, I advocate for an increased focus on learning and developing appropriate representations for robot manipulation. In this talk, I show how novel action-centric representations can lead to better imitation learning for manipulation of diverse deformable objects. I will show how such representations can be learned from color images, depth images, or point cloud observational data. My research demonstrates how novel representations can lead to an exciting new era for robot manipulation of complex objects.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Daniel Seita is a postdoctoral researcher at Carnegie Mellon University's Robotics Institute, advised by David Held. His research interests are in computer vision and machine learning for robot manipulation, with a focus on using and developing novel observation and action representations to improve manipulation of challenging deformable objects. Daniel holds a PhD in computer science from the University of California, Berkeley, advised by John Canny and Ken Goldberg. He received undergraduate degrees in math and computer science from Williams College. Daniel's research has been supported by a six-year Graduate Fellowship for STEM Diversity and by a two-year Berkeley Fellowship. He has the Honorable Mention for Best Paper award at UAI 2017, was an RSS 2022 Pioneer, and has presented his work at premier robotics conferences such as ICRA, IROS, RSS, and CoRL.
Website: https://www.cs.cmu.edu/~dseita/
Host: Stefanos Nikolaidis
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE-S Seminar - Dr Stephen Tu
Mon, Apr 10, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Stephen Tu, Research Scientist at Google Brain (Robotics at Google)
Talk Title: The foundations of machine learning for feedback control
Abstract: Recent breakthroughs in machine learning offer unparalleled optimism for the future capabilities of artificial intelligence. However, despite impressive progress, modern machine learning methods still operate under the fundamental assumption that the data at test time is generated by the same distribution from which training examples are collected. In order to build robust intelligent systems-”self-driving vehicles, robotic assistants, smart grids-”which safely interact with and control the surrounding environment, one must reason about the feedback effects of models deployed in closed-loop.
In this talk, I will discuss my work on developing a principled understanding of learning-based feedback systems, grounded within the context of robotics. First, motivated by the fact that many real world systems naturally produce sequences of data with long-range dependencies, I will present recent progress on the fundamental problem of learning from temporally correlated data streams. I will show that in many situations, learning from correlated data can be as efficient as if the data were independent. I will then examine how incremental stability-”a core idea in classical control theory-”can be used to study feedback-induced distribution shift. In particular, I will characterize how an expert policy's stability properties affect the end-to- end sample complexity of imitation learning. I will conclude by showing how these insights lead to practical algorithms and data collection strategies for imitation learning.
Biography: Stephen Tu is a research scientist at Robotics at Google in New York City. His research interests are focused on a principled understanding of the effects of using machine learning models for feedback control, with specific emphasis on robotics applications. He received his Ph.D. from the University of California, Berkeley in EECS under the supervision of Ben Recht.
Host: Dr Mahdi Soltanolkotabi, soltanol@usc.edu
Webcast: https://usc.zoom.us/j/92463220973?pwd=UHJEVmZFV2V2L25zOUo1aDY0cTFNQT09More Information: ECE Seminar Announcement 04.10.2023 Stephen Tu.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/92463220973?pwd=UHJEVmZFV2V2L25zOUo1aDY0cTFNQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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ECE-S Seminar - Dr Sabrina Neuman
Tue, Apr 11, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Sabrina Neuman, Postdoctoral NSF Computing Innovation Fellow | Harvard University
Talk Title: Designing Computing Systems for Robotics and Physically Embodied Deployments
Abstract: Emerging applications that interact heavily with the physical world (e.g., robotics, medical devices, the internet of things, augmented and virtual reality, and machine learning on edge devices) present critical challenges for modern computer architecture, including hard real-time constraints, strict power budgets, diverse deployment scenarios, and a critical need for safety, security, and reliability. Hardware acceleration can provide high-performance and energy-efficient computation, but design requirements are shaped by the physical characteristics of the target electrical, biological, or mechanical deployment; external operating conditions; application performance demands; and the constraints of the size, weight, area, and power allocated to onboard computing-- leading to a combinatorial explosion of the computing system design space. To address this challenge, I identify common computational patterns shaped by the physical characteristics of the deployment scenario (e.g., geometric constraints, timescales, physics, biometrics), and distill this real-world information into systematic design flows that span the software-hardware system stack, from applications down to circuits. An example of this approach is robomorphic computing: a systematic design methodology that transforms robot morphology into customized accelerator hardware morphology by leveraging physical robot features such as limb topology and joint type to determine parallelism and matrix sparsity patterns in streamlined linear algebra functional units in the accelerator. Using robomorphic computing, we designed an accelerator for a critical bottleneck in robot motion planning and implemented the design on an FPGA for a manipulator arm, demonstrating significant speedups over state-of-the-art CPU and GPU solutions. Taking a broader view, in order to design generalized computing systems for robotics and other physically embodied applications, the traditional computing system stack must be expanded to enable co-design with physical real-world information, and new methodologies are needed to implement designs with minimal user intervention. In this talk, I will discuss my recent work in designing computing systems for robotics, and outline a future of systematic co-design of computing systems with the real world.
Biography: Sabrina M. Neuman is a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research interests are in computer architecture design informed by explicit application-level and domain-specific insights. She is particularly focused on robotics applications because of their heavy computational demands and potential to improve the well-being of individuals in society. She received her S.B., M.Eng., and Ph.D. from MIT. She is a 2021 EECS Rising Star, and her work on robotics acceleration has received Honorable Mention in IEEE Micro Top Picks 2022 and IEEE Micro Top Picks 2023.
Host: Dr Feifei Qian, feifeiqi@usc.edu | Dr Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/j/98275605184?pwd=NVBvL2hKdEZCRFRSTm1Hb1RWTSs2QT09More Information: ECE Seminar Announcement 04.11.2023 - Sabrina Neuman.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/98275605184?pwd=NVBvL2hKdEZCRFRSTm1Hb1RWTSs2QT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: Ruishan Liu (Stanford University) - Machine learning for precision medicine
Tue, Apr 11, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ruishan Liu, Stanford University
Talk Title: Machine learning for precision medicine
Series: CS Colloquium
Abstract: Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how machine learning can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022), and how to use language models and causal inference to form individualized treatment plans.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, the Next Generation in Biomedicine by Broad Institute, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.
Host: Yan Liu
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute - ISE 651 Seminar
Tue, Apr 11, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Alexandre Jacquillat, Assistant Professor, Dept. of Operations Research and Statistics, MIT Sloan
Talk Title: Optimizing Relay Operations Toward Sustainable Logistics
Host: Dr. John Carlsson
More Information: April 11, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: C. Mohan (Tsinghua University) - Query Optimization and Processing: Trends and Directions
Wed, Apr 12, 2023 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: C. Mohan, Tsinghua University
Talk Title: Query Optimization and Processing: Trends and Directions
Series: CS Colloquium
Abstract: Query optimization and processing (QOP) have been a dominant component of relational database management systems ever since such systems emerged in the research and commercial space more than four decades ago. Technologies related to QOP have received widespread attention and have evolved significantly since the days of IBM Research's System R, the project which gave birth to the concept of cost-based query optimization. Having worked on various database management topics at the birthplace of the relational model and the SQL language, until my retirement 2 years ago as an IBM Fellow at IBM Research in Silicon Valley, I have observed at close quarters a great deal of work in QOP. In this talk, I will give a broad overview of QOP's evolution. I will discuss not only research trends but also trends in the commercial world. Work done in various organizations across the world will be covered.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China, a Member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database, blockchain, AI and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the well-known ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol.
This IBM (1997-2020), ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the United States and Indian National Academies of Engineering (2009), and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA for its blockchain and other projects.
In 2020, he joined the Advisory Board of KBA. Since 2016, Mohan has been a Distinguished Visiting Professor of China's prestigious Tsinghua University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. During most of 2022, he was a non-employee consultant at Google with the title of Visiting Researcher. He has also been a Consultant to the Microsoft Data Team. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge network of followers. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK
Host: Cyrus Shahabi
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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AME Seminar
Wed, Apr 12, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Kristen Davis, UC Irvine
Talk Title: Large Amplitude Internal Wave Transformation Between 500m and the Surfzone
Abstract: Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a dense and rapidly-sampling array spanning depths from 500 m to shore near Dongsha Atoll in the South China Sea to track large amplitude internal solitary wave (ISW) shoaling, breaking, and runup. During the observational period incident ISW amplitudes ranged between 78 m and 146 m with propagation speeds between 1.40 m/s and 2.38 m/s. Fissioning ISWs generated larger trailing elevation waves when the thermocline was deep, and evolved into onshore propagating bores in depths near 100 m. Collapsing ISWs contained significant mixing and reduced upslope bore propagation. Bores on the shallow forereef drove bottom temperature variation in excess of 10 degrees Celsius and near-bottom cross-shore currents in excess of 0.4 m/s. Bores decelerated upslope, consistent with upslope two-layer gravity current theory, though runup extent, Xr, was offshore of the predicted gravity current location. Background stratification affected the bore runup, with Xr farther offshore when the Korteweg-de Vries nonlinearity coefficient, α, was negative. Fronts associated with the shoaling local internal tide, but equal in magnitude to the soliton-generated bores, were observed onshore of 20 m depth.
Biography: Kristen Davis is an Associate Professor of Civil & Environmental Engineering at the University of California, Irvine. She is an engineer and oceanographer who is interested in studying how physical processes shape coastal waters -“ combining principles of fluid mechanics, oceanography, and ecology. Kristen uses both field observations and numerical tools to examine circulation in the ocean, its natural variability, and influence on marine ecosystems and human-nature interactions. Kristen earned a Ph.D. in Civil and Environmental Engineering at Stanford University in 2009 and was a postdoctoral researcher at the Woods Hole Oceanographic Institution and the Applied Physics Laboratory at the University of Washington. Her recent research is focused on understanding nonlinear internal wave dynamics and the feasibility of the large-scale, offshore cultivation of macroalgae for the production of biofuels and as a strategy to sequester carbon dioxide.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Semiconductors & Microelectronics Technology Seminar - Qiushi Guo, Thursday, April 13th at 11am in EEB 248
Thu, Apr 13, 2023 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Qiushi Guo, CUNY Advanced Science Research Center (ASRC)
Talk Title: Lithium niobate integrated nonlinear photonics: new devices and systems on an old material
Series: Semiconductors & Microelectronics Technology
Abstract: Despite being an old material in optical and microwave technologies in its bulk form, thin-film lithium niobate (TFLN) has recently emerged as one of the most promising integrated photonic platforms owing to its strong electro-optic (EO) coefficient, quadratic optical nonlinearity, and broadband optical transparency ranging from 250 nm to 5 um. In this talk, I will first overview the basic optical properties of LN, and how LN nanophotonics can grant us new regimes of nonlinear photonics. Then I will present some of our recent experimental results on the realization and utilization of dispersion-engineered and quasi-phase-matched ultrafast photonic devices in both classical and quantum domains. I will discuss the realization of 100 dB/cm optical parametric amplification, 1.5-3 um widely tunable optical parametric oscillator (OPO), ultra-wide bandwidth quantum squeezing, femtosecond and femtojoule on chip all-optical switching, and the integrated mode-locked lasers based on TFLN with watt-level peak power.
Biography: Qiushi Guo is an assistant professor at the Advanced Science Research Center, City University of New York. Prior to joining the ASRC and the CUNY Graduate Center, Qiushi was a postdoctoral research associate at the California Institute of Technology. He received his Ph.D. in Electrical Engineering from Yale University in Dec. 2019, advised by Prof. Fengnian Xia. He received his M.S. degree in Electrical Engineering from the University of Pennsylvania in 2014, and his B.S. degree in Electrical Engineering from Xi'an Jiaotong University in 2012. Qiushi is the finalist of the 2022 Rising Star of Light, and the winner of the 2021 Henry Prentiss Becton Graduate Prize for his exceptional research achievements at Yale University. His research interests include integrated nonlinear and quantum photonics, mid-infrared photonics, and 2-D materials optoelectronics. He has published more than 40 peer-reviewed research papers in leading scientific journals with citations more than 300 times. He is serving on the editorial board of the journal Micromachines.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Qiushi Guo_2023-4-13.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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NL Seminar -Drinking From The Firehose of Science.
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Waleed Ammar, Allen Inst of AI (AI2)
Talk Title: Drinking From The Firehose of Science.
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.
Five years ago, I visited ISI to talk about our progress taming the scientific literature in the Semantic Scholar team at the Allen Institute for Artificial Intelligence. In this talk, I will highlight some of the exciting developments in Semantic Scholar over the past few years, then share with you how we've enabled a wide variety of users and partners to "drink" from the firehose of scientific publications by interfacing with the Semantic Scholar APIs. I will end with an interactive discussion of how we can increase the participation of underrepresented groups in science.
Biography: Waleed Ammar currently leads the Semantic Scholar APIs effort at the Allen Institute for Artificial intelligence (AI2), which enables researchers, practitioners and decision makers to do various computations on the scientific literature in a wide variety of research fields. Before rejoining AI2 this year, Waleed was a senior research scientist at Google, where he helped develop transformer-based models for generating DNA sequences based on PacBio long-reads which significantly reduced variant-calling errors [Nature Biotech'22]. He also helped develop task-oriented dialog systems which are more robust to disfluencies, code-switching and user revisions [arXiv'23]. Prior to joining Google, Waleed led the Semantic Scholar research team's efforts to develop ML-based methods to facilitate access to the literature [e.g., NAACL 19], build a knowledge graph of the scientific literature [NAACL'18], and use this wealth of information to identify systemic social problems in science [JAMA'19]. He also occasionally teaches courses at UW linguistics as an affiliate faculty member. In 2016, Waleed received a Ph.D. degree in artificial intelligence from Carnegie Mellon University. Before pursuing the Ph.D., Waleed was a research engineer at Microsoft Research and a web developer at eSpace Technologies. Outside work, Waleed spends most of his time on the water or in dancing studios.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=SsJNCkPEDu8Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=SsJNCkPEDu8
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Ibrahim Sabek (MIT) - Building Better Data-Intensive Systems Using Machine Learning
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ibrahim Sabek, MIT
Talk Title: Building Better Data-Intensive Systems Using Machine Learning
Series: CS Colloquium
Abstract: Database systems have traditionally relied on handcrafted approaches and rules to store large-scale data and process user queries over them. These well-tuned approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). One possible solution is to build a specialized system from scratch, tailored for each use case. Although such a specialized system is able to get orders-of-magnitude better performance, building it is time-consuming and requires a huge manual effort. This pushes the need for automated solutions that abstract system-building complexities while getting as close as possible to the performance of specialized systems. In this talk, I will show how we leverage machine learning to instance-optimize the performance of query scheduling and execution operations in database systems. In particular, I will show how deep reinforcement learning can fully replace a traditional query scheduler. I will also show that-”in certain situations-”even simpler learned models, such as piece-wise linear models approximating the cumulative distribution function (CDF) of data, can help improve the performance of fundamental data structures and execution operations, such as hash tables and in-memory join algorithms.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ibrahim Sabek is a postdoc at MIT and an NSF/CRA Computing Innovation Fellow. He is interested in building the next generation of machine learning-empowered data management, processing, and analysis systems. Before MIT, he received his Ph.D. from University of Minnesota, Twin Cities, where he studied machine learning techniques for spatial data management and analysis. His Ph.D. work received the University-wide Best Doctoral Dissertation Honorable Mention from University of Minnesota in 2021. He was also awarded the first place in the graduate student research competition (SRC) in ACM SIGSPATIAL 2019 and the best paper runner-up in ACM SIGSPATIAL 2018.
Host: Cyrus Shahabi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Michael Oberst (MIT) - Rigorously Tested & Reliable Machine Learning for Health
Thu, Apr 13, 2023 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael Oberst, MIT
Talk Title: Rigorously Tested & Reliable Machine Learning for Health
Series: CS Colloquium
Abstract: How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test?
Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.
In this talk, I will introduce methods I have developed to proactively assess and improve the reliability of machine learning models. A central theme will be the application of external knowledge, including guided review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Michael Oberst is a final-year PhD candidate in Computer Science at MIT. His research focuses on making sure that machine learning in healthcare is safe and effective, using tools from causal inference and statistics. His work has been published at a range of machine learning venues (NeurIPS / ICML / AISTATS / KDD), including work with clinical collaborators from Mass General Brigham, NYU Langone, and Beth Israel Deaconess Medical Center. He has also worked on clinical applications of machine learning, including work on learning effective antibiotic treatment policies (published in Science Translational Medicine). He earned his undergraduate degree in Statistics at Harvard.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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The Knowledge Shop, Volunteer Info Session
Thu, Apr 13, 2023 @ 07:00 PM - 07:30 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
The KNOWLEDGE SHOP was created so that families in disadvantaged communities could get access to educational services that would assist in the empowerment and educational development of their children. Join the info session to learn how you can get involved by volunteering your time, knowledge, and skills.
Learn more about The Knowledge Shop by visiting:
https://www.theknowledgeshopla.com/
Location: Online Event
Audiences: Everyone Is Invited
Contact: Noe Mora
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389323
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BME Speaker, Michael Cho
Fri, Apr 14, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Michael Cho , Professor of Bioengineering, University of Texas at Austin
Talk Title: Stem cells, biomechanics, traumatic brain injury
Host: BME Professor, Qifa Zhou - ZOOM link available upon request
Location: Corwin D. Denney Research Center (DRB) - DRB 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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ECE-S Seminar - Dr Gokul Subramanian Ravi
Mon, Apr 17, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Gokul Subramanian Ravi, Postdoctoral Scholar | University of Chicago
Talk Title: A Hybrid Computing Ecosystem For Practical Quantum Advantage
Abstract: As quantum computing transforms from lab curiosity to technical reality, we must unlock its full potential to enable meaningful benefits on real-world applications with imperfect quantum technology. Achieving this vision requires computer architects to play a key role, leveraging classical computing principles to build and facilitate a hybrid computing ecosystem for practical quantum advantage.
First, I will introduce my four research thrusts toward building this hybrid ecosystem: Classical Application Transformation, Adaptive Noise Mitigation, Scalable Error Correction and Efficient Resource Management.
Second, from the Classical Application Transformation thrust, I will present "CAFQA: A classical simulation bootstrap for variational quantum algorithms", which enables accurate classical initialization for VQAs by searching efficiently through the classically simulable portion of the quantum space with Bayesian Optimization. CAFQA recovers as much as 99.99% of the accuracy lost in prior state-of-the-art classical initialization, with mean improvements of 56x.
Third, from the Scalable Error Correction thrust, I will present "Clique: Better than worst-case decoding for quantum error correction", which proposes the Clique QEC decoder for cryogenic quantum systems. Clique is a lightweight cryo-decoder for decoding and correcting common trivial errors, so that only the rare complex errors are handled outside the cryo-refrigerator. Clique eliminates 90-99+% of the cryo-refrigerator I/O decoding bandwidth, while supporting more than a million physical qubits.
Finally, I will conclude with an overview of other prior and ongoing work, along with my future research vision toward practical quantum advantage.
Biography: Gokul Subramanian Ravi is a 2020 NSF CI Fellows postdoctoral scholar at the University of Chicago, mentored by Prof. Fred Chong. His research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He received his PhD in computer architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues, as well as two granted and three pending patents. His co-authored work was recognized as the Best Paper at HPCA 2022 and as a 2023 IEEE Micro Top Picks Honorable Mention.
Host: Dr Todd Brun, tbrun@usc.edu | Dr Christopher Torng, ctorng@usc.edu
Webcast: https://usc.zoom.us/j/97436018617?pwd=OFJVQ2Y0aCtnT0JXTE9LeWJlaGlvQT09More Information: ECE Seminar Announcement 2023.03.27 - Gokul Subramanian Ravi.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/97436018617?pwd=OFJVQ2Y0aCtnT0JXTE9LeWJlaGlvQT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Volunteers Needed! Viterbi K-12 STEM Center Summer Camps
Mon, Apr 17, 2023 @ 12:30 PM - 01:30 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Inspire the next generation of scientist and engineers by becoming a volunteer for the VIterbi K-12 STEM Center's youth summer camps. Volunteers are needed to help improve the overall operations and support elementary, middle school, and high school students with STEM-related projects and activities.
Join the information session to learn more!Location: Online Event
Audiences:
Contact: Noe Mora
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389085
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Mon, Apr 17, 2023 @ 02:00 PM - 03:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Andrew Holle, Assistant Professor, Department of Biomedical Engineering, National University of Singapore, PI, Mechanobiology Institute
Talk Title: Stem Cell Migration and Differentiation in Confining Microenvironments
Host: Peter Yingxiao Wang- Chair of Alfred E. Mann Department of Biomedical Engineering
More Info: zoom link available upon request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
Event Link: zoom link available upon request
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ECE-S Seminar - Dr Paria Rashidinejad
Tue, Apr 18, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Paria Rashidinejad, Postdoctoral Scholar | University of California, Berkeley
Talk Title: Reliable Data-Driven Decision-Making Systems
Abstract: Despite impressive success in domains such as vision and language, machine learning is still far from reliable integration into many challenging real-world scenarios, such as healthcare, where the coverage of existing data and the ability to collect new, diverse data are limited. This talk focuses on mathematically formulating and addressing some of the challenges in data-driven decision-making systems, studied in the reinforcement learning (RL) framework. I will discuss decision-making based on two sources of data: historical (offline) data and actively-collected data. In learning from offline data, I first mathematically formulate the challenge of partial data coverage. I show that this formulation combined with pessimistic offline RL unifies the major offline learning paradigms: imitation learning and conventional offline RL. I then present statistically-optimal and practical offline RL algorithms that simultaneously exploit expressive models, such as deep neural networks, and historical datasets with any coverage, to learn good decision-making policies. In learning from interactive data, I present general formulations and theoretically-guaranteed algorithms that exploit problem structure and expressive models to collect data for learning good policies, with efficacy demonstrated in a variety of navigation and locomotion tasks.
Biography: Paria Rashidinejad is a Postdoctoral Scholar at Berkeley AI Research Lab and Center for Human-Compatible AI. She received her Ph.D. in Electrical Engineering and Computer Sciences from the University of California, Berkeley in May 2022, under the supervision of Stuart Russell and Jiantao Jiao. Her research focuses on the mathematical foundations of machine learning and AI and designing capable and general-purpose AI and ML systems for reliable integration into the real world. She also works on machine learning applications in areas such as healthcare, robotics, and systems.
Host: Dr Somil Bansal, somilban@usc.edu | Dr Urbashi Mitra, ubli@usc.edu
Webcast: https://usc.zoom.us/j/95534730279?pwd=SWtyVk0zcWtkWUQ5WVltRlNMalpNZz09More Information: ECE Seminar Announcement 04.18.2023 - Paria Rashidinejad.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
WebCast Link: https://usc.zoom.us/j/95534730279?pwd=SWtyVk0zcWtkWUQ5WVltRlNMalpNZz09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Epstein Institute - ISE 651 Seminar
Tue, Apr 18, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Randy Hall, Dean's Professor, Daniel J. Epstein Dept. of ISE, USC
Talk Title: Data Informed Mitigation to Reduce the Health Consequences of Pandemics
Host: Prof. Maged Dessouky
More Information: April 18, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Photonics Seminar - Michael Shlesinger
Wed, Apr 19, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Michael Shlesinger, Office of Naval Research
Talk Title: Stretched Times and Divergent Time Scales Near the Glass Transition
Series: Photonics Seminar Series
Abstract: Near the glass transition temperature, Tg, many supercooled liquids experience stretched exponential relaxations rather than faster exponential decay. The relaxation has a time scale that diverges at a critical temperature, Tc, which is below Tg. I derive these laws from a model of anomalous defect diffusion and apply the theory to the pressure dependent conductivity of ion doped polymers near Tg. From a thermodynamic viewpoint comparisons are made of isochoric activation energy to isobaric activation enthalpy to determine the relative importance of volume and temperature to electrical conductivity. A key ingredient in the theory is the disappearance of free volume associated with defects when the temperature is lowered. The theory is able to explain the free volume measurements made by positron annihilation experiments.
Biography: Dr. Michael Shlesinger received a B.S. in Math and Physics from SUNY Stony Brook in 1970 and PhD in Physics from the University of Rochester in 1976. He then worked at the La Jolla Institute, Georgia Tech, and the University of Maryland before joining the Office of Naval Research in 1983. He became Head of ONR's Physics Division in 1986 and a member of the Senior Executive Service in 1987. He switched to a Chief Scientist role in 1995 and received the Presidential Rank Award in 2004 and ONR's Outstanding Lifetime Achievement Award in 2006. He has held the Kinnear Chair for Science at the USNA. One of his ONR responsibilities was the Division Director for Marine Corps programs. His ONR programs have focused on fields including Nonlinear Dynamics; Fractals; and Plasmonic Materials. He co-founded the Experimental Chaos Conference and received the APS Outstanding Referee Award. His work on random processes can be found in his 2021 mathematical autobiography "An Unbounded Experience in Random Walks with Applications".
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: Michael Shlesinger Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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AME Seminar
Wed, Apr 19, 2023 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Ivan Bermejo-Moreno, USC
Talk Title: Interactions of Shock Waves and Turbulence Through Numerical Simulations
Abstract: Hypersonic flight and propulsion pose fundamental challenges that arise from interactions between shock waves and turbulence. These interactions can be beneficial, enhancing the mixing of fuel and oxidizer in a scramjet engine, but they can also be detrimental, compromising the integrity of the flying vehicle through uncontrolled aerothermostructural coupling. This presentation will highlight recent developments on the prediction and understanding of these phenomena by means of high-fidelity numerical simulations. First, focus will be placed on interactions of shock waves reflecting off turbulent boundary layers that develop along rigid and flexible walls, by loosely coupling a wall-modeled large-eddy simulation solver for the fluid flow with an elastic solid structural solver that accounts for geometric nonlinearities. Strong shock/boundary-layer interactions will be emphasized, resulting in mean flow separation and low-frequency unsteadiness that can couple with natural frequencies of the solid structure. Simulation results will be compared with supersonic wind-tunnel experiments. Second, the enhancement of scalar mixing under canonical shock-turbulence interactions will be addressed by means of shock-capturing direct numerical simulations, evaluating the effects of the shock and turbulence Mach numbers, and the Reynolds number. Statistical analyses will highlight changes along the mean flow direction of scalar variance and dissipation-rate budgets, flow topology, and alignments of the scalar gradient with vorticity and strain-rate eigendirections. A novel methodology to track the time evolution of geometric and physical quantities of turbulent flow structures will be introduced and applied to study the dynamics of isoscalar surfaces across the shock-turbulence interaction.
Biography: Ivan Bermejo-Moreno received an engineer's degree from the School of Aeronautics at the Polytechnic University of Madrid, Spain (2001). He then worked for two years in the aerospace industry (GMV) and received a Fulbright Fellowship to pursue M.Sc. (2004) and Ph.D. (2008) degrees in aeronautics from the California Institute of Technology. Afterwards, he held a postdoctoral research fellowship at the Center for Turbulence Research, Stanford University/NASA Ames Research Center (2009-2014). He joined the Aerospace and Mechanical Engineering Department at the University of Southern California as assistant professor in 2015. His research combines numerical methods, physical modeling, and high-performance computing for the simulation and analysis of turbulent fluid flows involving multi-physics phenomena. He is a recipient of the Rolf D. Buhler Memorial Award, the William F. Ballhaus Prize, the Hans G. Hornung Prize, and the NSF CAREER Award.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09Location: John Stauffer Science Lecture Hall (SLH) - 102
WebCast Link: https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Min Family Challenge Finals
Wed, Apr 19, 2023 @ 06:00 PM - 08:00 PM
Viterbi Technology Innovation and Entrepreneurship
Conferences, Lectures, & Seminars
The Min Family Social Entrepreneurship Challenge was inspired by the Min family's enduring faith and aspiration to empower future generations of engineers to use their acquired knowledge and leverage the power of technology to better the world by globally serving the least fortunate and their pressing societal needs. This year the theme focuses on the Sustainable Development Goals.
This years Min Family Challenge 2022-2023 cohort have been going through educational sessions, workshops, and meetings with mentors throughout the academic year. This years Min Family Challenge will culminate with the Finals Showcase on April 19 at 6pm.Location: Private Location (register to display)
Audiences: Everyone Is Invited
Contact: Johannah Murray
Event Link: https://engage.usc.edu/Viterbitie/rsvp?id=389254
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NL Seminar - Modular Language Models
Thu, Apr 20, 2023 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Suchin Gururangan, University of Washington, Univ. Of Washington
Talk Title: Modular Language Models
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.
Conventional language models (LMs) are trained densely: all parameters are updated with respect to all data. We argue that dense training leads to a variety of well-documented issues with LMs, including their prohibitive training cost and unreliable downstream behavior. We then introduce a new class of LMs that are fundamentally modular, where components (or experts) of the LM are specialized to distinct domains in the training corpus, and experts are conditionally updated based on the domain of the incoming document. We show how modularity addresses the limitations of dense training by enabling LMs that are rapidly customizable (with the ability to mix, add, or remove experts after training), embarrassingly parallel (requiring no communication between experts), and sparse (needing only a few experts active at a time for inference). Key to our proposal is exploring what constitutes the domains to which experts specialize, as well as reflecting on the data sources used to train LMs. Our new techniques chart a path towards collaborative LM development, where anyone can contribute and maintain experts at very modest computational cost.
Biography: Suchin Gururangan is a 3rd year PhD candidate at the University of Washington, advised by Noah A. Smith and Luke Zettlemoyer. He was previously a visiting researcher at Meta AI, a pre doctoral resident at the Allen Institute for AI, and spent several years in industry as a data scientist. His research interests span many areas of NLP, currently he works on modular, sparse language models that are efficient to customize and scale. His work has received awards at ACL 2020 and 2021, and he is supported by the Bloomberg Data Science PhD Fellowship.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=lWlVRGgwRK4Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=lWlVRGgwRK4
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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CS Colloquium: Alvaro Velasquez (DARPA) - Neuro-Symbolic Transfer and Optimization
Thu, Apr 20, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alvaro Velasquez, DARPA
Talk Title: Neuro-Symbolic Transfer and Optimization
Series: CS Colloquium
Abstract: Neuro-symbolic artificial intelligence (NSAI) has experienced a renaissance and gained much traction in recent years as a potential "third wave" of AI to follow the tremendously successful second wave underpinned by statistical deep learning. NSAI seeks the integration of neural learning systems and formal symbolic reasoning for more efficient, robust, and explainable AI. This integration has been successful in classification and reinforcement learning, among other areas, but its application to transfer learning and combinatorial optimization remains largely unexplored. In this talk, we will cover recent advancements for the integration of symbolic structures in transferring knowledge between agents in the context of reinforcement learning and planning for sequential decision-making. We will also explore the concept of dataless neural networks as a framework for integrating combinatorial optimization problems and learning models. We conclude with a vision for these areas and the technical challenges that follow.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Alvaro Velasquez is a program manager in the Innovation Information Office (I2O) of the Defense Advanced Research Projects Agency (DARPA), where he currently leads programs on neuro-symbolic AI. Before that, Alvaro oversaw the machine intelligence portfolio of investments for the Information Directorate of the Air Force Research Laboratory (AFRL). Alvaro received his PhD in Computer Science from the University of Central Florida in 2018 and is a recipient of the distinguished paper award from AAAI, best paper and patent awards from AFRL, the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) award, the University of Central Florida 30 Under 30 award. He has authored over 60 papers and two patents and serves as Associate Editor of the IEEE Transactions on Artificial Intelligence. His research has been funded by the Air Force Office of Scientific Research.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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MHI ISSS Seminar - Prof. Yahya Tousi, Thursday, April 20th at 2pm in RTH 211
Thu, Apr 20, 2023 @ 02:00 PM - 02:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Yahya Tousi, University of Minnesota, Twin Cities
Talk Title: Toward energy-efficient and scalable mm-wave systems
Series: Integrated Systems
Abstract: The end of device scaling is the dawning of a new era in integrated circuit design. Today, there is a growing demand for energy-efficient systems in multi-sensor electric vehicles, UAVs, and distributed wireless pico-cells. This is while, the intrinsic performance of analog building blocks no longer scales with technology nodes. In this talk I will argue that in the absence of device-level scaling, rethinking the frontend architecture by modernizing the traditional hierarchical design can open the door to substantial improvements in hardware efficiency and scalability. I will present two examples to support this claim.
In the first work we rethink digital processing in phase modulated radars by replacing it with a more efficient mixed analog processing scheme. The new system demonstrates more than an order of magnitude improvement in energy efficiency compared to traditional radar sensors. In the second work I introduce a nearest element phase monitoring architecture that overcomes the scalability challenges in traditional LO distribution schemes. Based on this new approach and for the first time, we implement a mm-wave phased array radiator with seamless multi-chip scalability. These two examples demonstrate how combining architectural and circuit-level innovations in this new era can lead to efficient and scalable mm-wave and THz systems.
Biography: Yahya Tousi received his Ph.D. degree in 2012 from the Department of Electrical and Computer Engineering at Cornell University, Ithaca, NY. In 2014 he joined the IBM T. J. Watson Research Center at Yorktown Heights, NY to develop the next generation of mm-wave phased array transceivers for wireless communication systems, and since 2017 he has been with the ECE Department at the University of Minnesota, Twin Cities. His current research interests are in high performance integrated circuits and novel architectures for mm-wave and terahertz systems with applications in communication, sensing, and healthcare. Dr. Tousi is the co-recipient of ISSCC Lewis Award for Outstanding Paper, and the Journal of Solid-State Best Paper Award both in 2017, the DARPA Young Faculty Award in 2020 and the DARPA Director's Fellowship Award in 2022.
Host: MHI - ISSS, Hashemi, Chen and Sideris
More Info: Zoom Link/Code: Meeting ID: 950 2226 0136, Passcode: 325523
More Information: FLYER_Tousi.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: Zoom Link/Code: Meeting ID: 950 2226 0136, Passcode: 325523
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Pitfalls and Paradoxes in the History of Probability Theory
Fri, Apr 21, 2023 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Michael Shlesinger, Office of Naval Research
Talk Title: Pitfalls and Paradoxes in the History of Probability Theory
Abstract: From the throwing of bones, dice, choosing long or short sticks, or debating the risk of the smallpox vaccine, fascinating and sometimes puzzling questions have arisen to advance the field of probability. We discuss interesting personalities and their famous questions and paradoxes including Galileo and Newton's dice game, de Mere's Grand Scandal, the Pascal-Fermat letters, the St. Petersburg Paradox, Bernoulli's Monster, and Bertrand's Paradox. We discuss the discovery of limit theorems from DeMoivre who first arrived at the Gaussian to Poisson who studied the same process, but with a twist arrived instead at the Poisson distribution. Levy considered a self-similar random process to arrive at random variables with infinite moments with now connections to fractals.
Biography: Dr. Michael Shlesinger received a B.S. in Math and Physics from SUNY Stony Brook in 1970 and PhD in Physics from the University of Rochester in 1976. He then worked at the La Jolla Institute, Georgia Tech, and the University of Maryland before joining the Office of Naval Research in 1983. He became Head of ONR's Physics Division in 1986 and a member of the Senior Executive Service in 1987. He switched to a Chief Scientist role in 1995 and received the Presidential Rank Award in 2004 and ONR's Outstanding Lifetime Achievement Award in 2006. He has held the Kinnear Chair for Science at the USNA. One of his ONR responsibilities was the Division Director for Marine Corps programs. His ONR programs have focused on fields including Nonlinear Dynamics; Fractals; and Plasmonic Materials. He co-founded the Experimental Chaos Conference and received the APS Outstanding Referee Award. His work on random processes can be found in his 2021 mathematical autobiography "An Unbounded Experience in Random Walks with Applications."
Host: Paul Bogdan
Location: Hedco Neurosciences Building (HNB) - 100
Audiences: Everyone Is Invited
Contact: Estela Lopez
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BME Speaker, Dr. Hangbo Zhao
Fri, Apr 21, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Hangbo Zhao , Assistant Professor, Viterbi School of Aerospace and Mechanical Engineering
Talk Title: Soft 3-dimensional bioelectronics
Host: BME Professor Ellis Meng - ZOOM link available upon request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
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MoBI Seminar: The Brain's Crescendo; How Music Training Impacts Child Development
Mon, Apr 24, 2023 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Assal Habibi, Associate Research Professor of Psychology, Brain and Creativity Institute, University of Southern California
Talk Title: The Brain's Crescendo; How Music Training Impacts Child Development
Abstract: In a multi-year longitudinal study, we have been investigating the effects of a group-based music training program on the development of children, beginning at age 6, using behavioral, neuroimaging, and electrophysiological measures. The target group of children have been participating in the Youth Orchestra of Los Angeles (YOLA) program. This music program is based on the Venezuelan system of musical training known as El-Sistema and offers free music instruction 6-“7 hours weekly to children from underprivileged and under-resourced areas of Los Angeles. The children in the music program have been compared with two groups of children, one involved in a community-based sports program, and another not enrolled in any systematic afterschool training. During this talk, I will share some of the behavioral and neuroimaging results from this study. Over the course of 5 years, we have observed that children in the music group had better performance than comparison groups in musically relevant auditory skills (pitch and rhythm discrimination) and showed an accelerated maturity of auditory processing as measured by cortical auditory evoked potentials. We also observed that children in the music group showed a different rate of cortical thickness maturation between the right and left posterior superior temporal gyrus and higher fractional anisotropy in the corpus callosum, specifically in the crossing pathways connecting superior frontal, sensory, and motor segments. For nonmusical skills, children with music training, compared with children without music training, showed stronger neural activation during a cognitive inhibition task in brain regions involved in response inhibition and decision-making (bilateral pre-SMA/SMA, ACC, IFG). Finally, we observed that parents of children involved in music training, after four years, rated their children higher on the emotional stability personality trait and lower on aggression and on hyperactivity compared to children not involved in music activities despite no differences in these measures before children's entry into the program. Considering a general reduction in art education specifically in the communities where there is limited access to art exposure in general, and specifically to music education, the findings from this study is providing compelling answers to the ongoing discussion about music's role in the education curriculum.
Biography: Assal Habibi is an Associate Research Professor of Psychology at the Brain and Creativity Institute at the University of Southern California. Her research takes a broad perspective on understanding the influence of arts and specifically music on health and development, focusing on how biological dispositions and learning experiences shape the brain and development of cognitive, emotional and social abilities during childhood and adolescence. She is an expert on the use of electrophysiologic and neuroimaging methods to investigate human brain function and has used longitudinal and cross-sectional designs to investigate how implementing music training programs within the school curricula impacts the learning and academic achievement of children from under-resourced communities. Her research program has been supported by federal agencies and private foundations including the NIH, NEA and the GRoW @ Annenberg Foundation and her findings have been published in peer reviewed journals including Cerebral Cortex, Music Perception, Neuroimage and PLoS ONE. Currently, she is the lead investigator of a multi-year study, in collaboration with the Los Angeles Philharmonic and their Youth Orchestra program (YOLA), investigating the effects of early childhood music education on the development of brain function and structure as well as learning skills, cognitive, emotional, and social abilities. Dr. Habibi is a classically trained pianist and has many years of musical teaching experience with children, a longstanding personal passion.
Host: Dr. Karim Jerbi, karim.jerbi.udem@gmail.com and Dr. Richard M. Leahy, leahy@sipi.usc.edu
Webcast: https://usc.zoom.us/j/99532928626?pwd=QjlwM2JGejZLdzNPdWEwc3RSNk0wdz09More Information: MoBI-Seminar-Habibi-042423.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
WebCast Link: https://usc.zoom.us/j/99532928626?pwd=QjlwM2JGejZLdzNPdWEwc3RSNk0wdz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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CS Colloquium: Wanrong Zhang (Harvard) - Enabling Interactivity to Move Differential Privacy Closer to Practice
Tue, Apr 25, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Wanrong Zhang, Harvard University
Talk Title: Enabling Interactivity to Move Differential Privacy Closer to Practice
Series: CS Colloquium
Abstract: With growing concerns about large-scale data collection and surveillance, the development of privacy-preserving tools can help alleviate public fears about the misuse of personal data. The field of differential privacy (DP) offers powerful data analysis tools that provide worst-case privacy guarantees. However, most of the existing tools in the differential privacy literature only apply to static databases with non-interactive analysis, which release query answers in a single shot. In practice, data analysts often need to perform a sequence of adaptive analyses on data arriving online, which raises the need for interactive data analysis. This development poses two major questions: 1. How can we design interactive mechanisms that strike a better trade-off between privacy and accuracy? 2. Can we combine multiple interactive mechanisms as building blocks to create a more complex DP algorithm?
In this talk, I will discuss some of my work that answers these questions. To answer the first question, I have created a wide set of tools for private online decision-making problems. I will present one example problem for handling online databases---differentially private change-point detection. Second, I will show the optimal composition theorems for composing multiple interactive mechanisms. My work is among the first to address this long-standing gap in the understanding of composition for differential privacy. I will conclude the talk with my future directions.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Wanrong Zhang is an NSF Computing Innovation Fellow in the Theory of Computing group at Harvard John A. Paulson School of Engineering and Applied Sciences. She is also a member of the Harvard Privacy Tools/OpenDP project. Her primary focus is to address new challenges introduced by real-world deployments of differential privacy. Before joining Harvard, she received her Ph.D. from Georgia Institute of Technology. She was selected as a rising star in EECS in 2022 and a rising star in Data Science in 2021. She is a recipient of the Computing Innovation Fellowship from CCC/CRA/NSF.
Host: Jiapeng Zhang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute - ISE 651 Seminar
Tue, Apr 25, 2023 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dinakar Gade, Product Manager, Xpress Optimization
Talk Title: Recent Trends and Evolution in Optimization Solvers
Host: Prof. Suvrajeet Sen
More Information: April 25, 2023.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Colloquium: Silvia Sellan (University of Toronto) - "Geometry +": A Tour of Geometry Processing Research
Tue, Apr 25, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Silvia Sellan, University of Toronto
Talk Title: "Geometry +": A Tour of Geometry Processing Research
Series: CS Colloquium
Abstract: From virtual reality to 3D printing, all the way through self-driving cars and the metaverse, today's technological advances rely more and more on capturing, creating and processing three-dimensional geometry. In this talk, we will show how geometry processing can empower other areas of Computer Science to find new research questions and solutions. Specifically, we will focus on our latest progress on realtime fracture simulation for video games, an algorithmic fairness analysis of gender in the Computer Graphics literature and a quantification of the uncertainty associated with several steps of the Geometry Processing pipeline
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Silvia is a fourth year Computer Science PhD student at the University of Toronto. She is advised by Alec Jacobson and working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean's Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH. She is currently looking to survey potential future postdoc and faculty positions, starting Fall 2024
Host: Oded Stein
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
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Partners in Materials Research Seminar Series - DR. LOUIS PEREZ of Apeel Sciences
Tue, Apr 25, 2023 @ 04:00 PM - 05:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Louis Perez, Apeel Sciences
Talk Title: From Idea to Product: Leveraging Chemistry and Materials to Reduce Food Waste
Host: Mork Family Department of Chemical Engineering and Materials Science and The USC Materials Consortium
More Information: Confirmed Flyer. Louis Perez.pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 252
Audiences: Everyone Is Invited
Contact: Monique Garcia
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BME Seminar Speaker, Dr. Jianping Fu
Fri, Apr 28, 2023 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jianpin Fu , Professor, Mechanical Engineering, Biomedical Engineering, Cell & Developmental Biology, University of Michigan
Talk Title: Stem cell and developmental bioengineering
Host: BME Professor Keyue Shen - Zoom Available Upon Request
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Michele Medina
