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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
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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VPC April 2023 Meeting
Mon, Apr 03, 2023 @ 04:30 PM - 06:00 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Monthly meeting of Viterbi student organization leaders
Location: Sign into EngageSC to View Location
Audiences:
Contact: Kevin Giang
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389203
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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
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
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
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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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: Ronald Tutor Hall of Engineering (RTH) - RTH 306
WebCast Link: https://usc.zoom.us/j/91455259066?pwd=dHdrZnhtRUh2KzhDQnhUZHhaTmQ5QT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CS Colloquium: KMaithilee 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
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
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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Tabling Session with the Navy Talent Acquisition Group Pacific
Mon, Apr 10, 2023 @ 12:00 PM - 04:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Tabling Session with the Navy Talent Acquisition Group Pacific!
Date: Monday, April 10th
Time: 12:00 p.m. - 4:00 p.m.
Location: Epstein Family Plaza
Program overview: The Nuclear Propulsion Officer Candidate program (NUPOC) is open to graduates and students who are within 30 months of graduating. The collegiate programs provides students with a monthly salary so they can focus on completing their degrees, leading to an active duty Naval career in Nuclear power and engineering management. Upon graduation, NUPOC candidates are provided with valuable Nuclear power technical training. This career path has outstanding potential for growth, either in the Navy or the Civilian sector if they decide to separate at the end of their initial service obligation. Academic success is a students primary focus while in our collegiate program. Collegiate monthly pay works out to $6,500 per month, up to 30 months.
The following positions are part of the NUPOC program:
Naval Reactors Engineer (approximate GPA 3.8 - 4.0)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
5 year position in Washington D.C. (DOES NOT DEPLOY).
Job entails approving, confirming, and planning the design, operation, and maintenance of over 100 nuclear reactors.
Supports the operational fleet (submarines and aircraft carriers).
Nuclear Power School Instructor (approximate GPA 3.6 - 4.0).
5 year position in Charleston, SC (DOES NOT DEPLOY)
Job entails training future Nuclear Propulsion Officers and Nuclear Field Enlisted personnel, while gaining valuable teaching experience in an exciting and technologically advanced curriculum.
Submarine Officer (GPA 3.0+)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
Will be stationed on either a Ballistic Missile Trident, Fast Attack, or Guided Missile nuclear-powered submarine.
You will oversee everything from nuclear propulsion plant operations to weapon systems and navigational duties.
Surface Warfare Officer (GPA 3.0+)
$15,000 sign-on bonus + an additional $2,000 upon completion of Nuclear Power School.
Will complete a first tour on a conventionally powered combat ship where you will receive your Surface Warfare Officer Qualification.
Following your first tour you will complete Nuclear Power Training and be stationed on an Nimitz-class nuclear powered aircraft carrier.
There you will oversee the operation and maintenance of the sophisticated nuclear propulsion plant.
Majors: STEM majors who have completed 1 year of Calculus and Physics and have a GPA of at least 3.0.
US Citizenship is required to apply. We are not able to offer Visa Sponsorship or hire students on CPT/OPT.Location: Epstein Family Plaza
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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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
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 and Career Development Professor, Dept. of Operations Research, MIT Sloan
Talk Title: TBD
Host: Dr. John Carlsson
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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ECE-S Seminar - Dr Igor Kadota
Wed, Apr 12, 2023 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr Igor Kadota, Postdoctoral Research Scientist | Department of Electrical Engineering, Columbia University
Talk Title: Wireless Networks for Future Applications: from Networks of Drones to Adaptive Control of Integrated Circuits
Abstract: Emerging applications such as the Internet-of-Things and Smart-City Intersections have in common: (i) the potential to greatly benefit society, and (ii) the need for an underlying communication network that can satisfy stringent performance requirements in terms of data rates, latency, information freshness, scalability, and resiliency, which are unachievable by traditional networks, including current 5G deployments. Developing the next-generation communication networks is a challenging endeavor that requires interdisciplinary research combining rigorous theory, data-driven solutions, and experimentation with advanced wireless systems.
In this talk, I will discuss selected interdisciplinary projects, including: (i) A network control algorithm with provable performance guarantees in terms of information freshness and its implementation in a network of drones. (ii) A predictive weather-aware routing and admission control algorithm for a city-scale millimeter-wave backhaul network in Sweden. (iii) A system that adaptively reconfigures a highly complex state-of-the-art integrated circuit in order to enable full-duplex wireless communication. Finally, I will discuss my research vision on developing cross-layer networking solutions that can dynamically adapt the wireless systems and, at the same time, intelligently allocate the available communication and computation resources aiming to meet the stringent performance requirements of emerging and future applications.
Biography: Igor Kadota is a Postdoctoral Research Scientist at Columbia University. He received the Ph.D. degree from the Laboratory for Information and Decision Systems (LIDS) at MIT in 2020. His research is on modeling, analysis, optimization, and implementation of next-generation communication networks, with the emphasis on advanced wireless systems and time-sensitive applications. Igor was a recipient of several research, teaching, and mentoring awards, including the 2018 Best Paper Award at IEEE INFOCOM, the 2020 MIT School of Engineering Graduate Student Extraordinary Teaching and Mentoring Award, and he was selected as a 2022 LATinE Trailblazer in Engineering Fellow by Purdue's College of Engineering. For additional information, please visit: http://www.igorkadota.com
Host: Dr Bhaskar Krishnamachari, bkrishna@usc.edu | Dr Keith Chugg, chugg@usc.edu | Dr Kostantinos Psounis, kpsounis@usc.edu
Webcast: https://usc.zoom.us/j/98851311744?pwd=THVMY0FjWlRVTG8ycDdVYTB3K3A4UT09More Information: ECE Seminar Announcement 04.12.2023 - Igor Kadota.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/98851311744?pwd=THVMY0FjWlRVTG8ycDdVYTB3K3A4UT09
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Computer Science General Faculty Meeting
Wed, Apr 12, 2023 @ 12:00 PM - 02:00 PM
Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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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
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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Getting to Know Goldman Sachs Engineering (Summer 2024 Opportunities)
Wed, Apr 12, 2023 @ 05:30 PM - 06:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Getting to Know Goldman Sachs Engineering (Summer 2024 Opportunities)
Date: Wednesday, April 12th
Time: 5:30 p.m. - 6:30 p.m.
Location: Zoom RSVP HERE
Calling all sophomores! Join this engaging virtual session to learn from Goldman Sachs Engineers about opportunities at the firm, ways to maximize your summer in preparation for your junior year, and what it means to sit at the intersection of finance and tech.
While Goldman Sachs has wrapped up its 2023 summer recruiting, applications launch for summer internships for 2024 on August 15 and we are eager to have interested and talented Trojans positioned to succeed in our process this summer. The session will introduce you to the Goldman selection process, while simultaneously preparing you to grow your technical skills and launch your career in tech.
Are you recruiting for internships, full time, or both? We will be recruiting for 2024 Summer Internships (which open August 15, 2023). We mostly want to get on students radars early and provide some insight into how they can maximize their summer going into their junior year.
Can you offer Visa sponsorship? Are you able to hire a student on CPT or OPT? Yes to all 3!Location: Zoom, please see below for details on how to RSVP
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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NL Seminar -Title TBA
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: TBA
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.
Biography: TBA
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/91659640295Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/91659640295
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
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
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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ShowCAIS 2023
Fri, Apr 14, 2023 @ 09:00 AM - 05:00 PM
Computer Science, USC Viterbi School of Engineering
University Calendar
The USC Center for AI in Society is hosting an all-day, in-person symposium on Friday, April 14th on the USC campus. This event will highlight the work of students and faculty using AI for good, and will include lunch and refreshments.
Registration: https://sites.google.com/usc.edu/showcais2023/registration?authuser=0
PLEASE REACH OUT TO THE SHOWCAIS ORGANIZING COMMITTEE WITH ANY QUESTIONS: USCCAIS@USC.EDULocation: Ronald Tutor Hall (RTH) 526
Audiences: Everyone Is Invited
Contact: Caitlin Dawson
Event Link: https://sites.google.com/usc.edu/showcais2023/home?authuser=0
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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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Volunteers Needed! Viterbi K-12 STEM Center Summer Camps
Mon, Apr 17, 2023 @ 12:30 PM - 01:30 PM
Viterbi School of Engineering Student Affairs
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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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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PHD Thesis Proposal - Julie Jiang
Mon, Apr 17, 2023 @ 01:00 PM - 02:30 PM
Computer Science
University Calendar
PHD Thesis Proposal: Julie Jiang
Committee: Emilio Ferrara (Chair), Barath Raghavan, Su Jung Kim, Jesse Thomason, Kristina Lerman
Title: Socially-Infused Mining of Digital Traces of Human Behavior Data
Abstract:
The vast amount of data generated by human behavior online provides valuable insight into how people interact with one another and with digital environments. However, mining this data can be time-consuming and computationally intensive. This dissertation proposes a unified language and network model that leverages the concept of homophily to efficiently analyze large-scale human behavior. By identifying patterns in network interactions and linguistic styles, this model can characterize political polarization, detect hateful and toxic users, and quantify users based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments. I apply this model to a range of real-world problems, including characterizing political polarization, understanding social influence on networks of hateful users, and contextualizing user behavior based on their moral foundation leanings. The findings demonstrate how seemingly simple patterns in online behavior can offer a deeper understanding of human behavior in digital environments.
Location: https://usc.zoom.us/j/96953099505?pwd=MDhJVFFSbDhuNnBWNm9JZjRFRUVjZz09
Audiences: Everyone Is Invited
Contact: Asiroh Cham
Event Link: https://usc.zoom.us/j/96953099505?pwd=MDhJVFFSbDhuNnBWNm9JZjRFRUVjZz09
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CS Colloquium: Marynel Vazquez
Tue, Apr 18, 2023
Computer Science
Conferences, Lectures, & Seminars
Series: Computer Science Colloquium
Host: Stefanos Nikolaidis
Audiences: Everyone Is Invited
Contact: Computer Science Department
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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, Professor and Director of CREATE, Daniel J. Epstein Dept. of ISE, USC
Talk Title: TBD
Host: Prof. Maged Dessouky
Location: Ethel Percy Andrus Gerontology Center (GER) - GER 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Computer Science General Faculty Meeting
Wed, Apr 19, 2023 @ 12:00 PM - 02:00 PM
Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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Min Family Challenge Finals
Wed, Apr 19, 2023 @ 06:00 PM - 08:00 PM
Viterbi Technology Innovation and Entrepreneurship
Receptions & Special Events
Hear from the final Min Family Challenge Teams in their technologies and innovation solution that meets societal needs. As they compete for the 50,000 prize towards their business.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Viterbi TIE
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NL Seminar
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: TBA
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.
Biography: TBA
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://usc.zoom.us/j/93307399820Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://usc.zoom.us/j/93307399820
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
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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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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Engineers for Earth Day - Wikipedia Editathon
Fri, Apr 21, 2023 @ 01:00 PM - 03:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Join the Engineering in Society Program and the Science & Engineering Library in their second annual Engineers for Earth Day Wikipedia Editathon!
When: Friday, April 21, 2023
When: 1-3PM
Where: SSL 210
Use your research and writing skills to improve the world's knowledge about sustainability and engineering!
We welcome new and experienced Wikipedia editors.
Location: Seaver Science Library (SSL) - 210
Audiences: Everyone Is Invited
Contact: Helen Choi
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Engineers for Earth Day: Wikipedia Editathon
Fri, Apr 21, 2023 @ 01:00 PM - 03:00 PM
USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Use your writing and research skills to increase the world’s knowledge of sustainability and engineering on Wikipedia!
Location: Sign into EngageSC to View Location
Audiences:
Contact: Melissa Medeiros
Event Link: https://engage.usc.edu/viterbi/rsvp?id=389201
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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. Amy Cohn, Professor, Dept. of Industrial & Operations Engineering | University of Michigan, Ann Arbor
Talk Title: TBD
Host: Dr. Sze-chuan Suen
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
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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Computer Science General Faculty Meeting
Wed, Apr 26, 2023 @ 12:00 PM - 02:00 PM
Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526- Hybrid
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
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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
