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Events for the 2nd week of 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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Maseeh Entrepreneurship Prize Competition Semi Finals
Thu, Apr 06, 2023 @ 04:30 PM - 06:30 PM
Viterbi Technology Innovation and Entrepreneurship
Receptions & Special Events
Maseeh Entrepreneurship Prize Competition Semi Finals
Come and hear pitches from the Maseeh Entrepreneurship Prize Competition (MEPC) teams in this years 2023 program. Hear about deep technology businesses as participants compete to make the finals.Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Viterbi TIE
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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
