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Conferences, Lectures, & Seminars
Events for October
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CS Distinguished Lecture: Tomas Lozano-Perez (MIT) - Generalization in Planning and Learning for Robotic Manipulation
Tue, Oct 05, 2021 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tomas Lozano-Perez, Massachusetts Institute of Technology (MIT)
Talk Title: Generalization in Planning and Learning for Robotic Manipulation
Series: Computer Science Distinguished Lecture Series
Abstract: An enduring goal of AI and robotics has been to build a robot capable of robustly performing a wide variety of tasks in a wide variety of environments; not by sequentially being programmed (or taught) to perform one task in one environment at a time, but rather by intelligently choosing appropriate actions for whatever task and environment it is facing. This goal remains a challenge. In this talk I'll describe recent work in our lab aimed at the goal of general-purpose robot manipulation by integrating task-and-motion planning with various forms of model learning. In particular, I'll describe approaches to manipulating objects without prior shape models, to acquiring composable sensorimotor skills, and to exploiting past experience for more efficient planning.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_K4eWcqebRsWT20GhOAbi-g
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Tomas Lozano-Perez is professor in EECS at MIT, and a member of CSAIL. He was a recipient of the 2011 IEEE Robotics
Pioneer Award and a co-recipient of the 2021 IEEE Robotics and Automation Technical Field Award. He is a Fellow of the AAAI, ACM, and IEEE.
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_K4eWcqebRsWT20GhOAbi-gLocation: Online - Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_K4eWcqebRsWT20GhOAbi-g
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Thomas Howard (University of Rochester) - Enabling Grounded Language Communication for Human-Robot Teaming
Tue, Oct 12, 2021 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Thomas Howard, University of Rochester
Talk Title: Enabling Grounded Language Communication for Human-Robot Teaming
Series: Computer Science Colloquium
Abstract: The ability for robots to effectively understand natural language instructions and convey information about their observations and interactions with the physical world is highly dependent on the sophistication and fidelity of the robot's representations of language, environment, and actions. As we progress towards more intelligent systems that perform a wider range of tasks in a greater variety of domains, we need models that can adapt their representations of language and environment to achieve the real-time performance necessitated by the cadence of human-robot interaction within the computational resource constraints of the platform. In this talk I will review my laboratory's research on algorithms and models for robot planning, mapping, control, and interaction with a specific focus on language-guided adaptive perception and bi-directional communication with deliberative interactive estimation.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_Rf6FW9NNSIWBkuNs9P5EcQ
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Thomas Howard is an assistant professor in the Department of Electrical and Computer Engineering at the University of Rochester. He also holds secondary appointments in the Department of Biomedical Engineering and Department of Computer Science, is an affiliate of the Goergen Institute of Data Science and directs the University of Rochester's Robotics and Artificial Intelligence Laboratory. Previously he held appointments as a research scientist and a postdoctoral associate at MIT's Computer Science and Artificial Intelligence Laboratory in the Robust Robotics Group, a research technologist at the Jet Propulsion Laboratory in the Robotic Software Systems Group, and a lecturer in mechanical engineering at Caltech.
Howard earned a PhD in robotics from the Robotics Institute at Carnegie Mellon University in 2009 in addition to BS degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. His research interests span artificial intelligence, robotics, and human-robot interaction with a research focus on improving the optimality, efficiency, and fidelity of models for decision making in complex and unstructured environments with applications to robot motion planning, natural language understanding, and human-robot teaming. Howard was a member of the flight software team for the Mars Science Laboratory, the motion planning lead for the JPL/Caltech DARPA Autonomous Robotic Manipulation team, and a member of Tartan Racing, winner of the 2007 DARPA Urban Challenge. Howard has earned Best Paper Awards at RSS (2016) and IEEE SMC (2017), two NASA Group Achievement Awards (2012, 2014), was a finalist for the ICRA Best Manipulation Paper Award (2012) and was selected for the NASA Early Career Faculty Award (2019). Howard's research at the University of Rochester has been supported by National Science Foundation, Army Research Office, Army Research Laboratory, Department of Defense Congressionally Directed Medical Research Program, National Aeronautics and Space Administration, and the New York State Center of Excellence in Data Science.
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_Rf6FW9NNSIWBkuNs9P5EcQLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_Rf6FW9NNSIWBkuNs9P5EcQ
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Harold Soh (National University of Singapore) - Trust, Talk, and Touch for Human-Robot Interaction
Tue, Oct 19, 2021 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Harold Soh, National University of Singapore
Talk Title: Trust, Talk, and Touch for Human-Robot Interaction
Series: Computer Science Colloquium
Abstract: In this talk, I will present three topics we've been exploring in my lab that bring us towards collaborative robots we trust. Specifically, I will discuss (1) how human trust in a robot transfers across tasks (and methods for modeling this phenomena), (2) how we can use deep self-models for human-robot communication, and if time permits, (3) how robots can extend their tactile perception for physical HRI. A common thread that runs through the topics is that using specified or learned structure can significantly improve sample efficiency. Data can be scarce in robotics and we will end the talk by briefly discussing open problems at the intersection of machine learning and HRI.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_0MjiSeXvR2-1FfxN0Bq7vg
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Harold Soh is Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), where he directs the Collaborative Learning and Adaptive Robots (CLeAR) group. Harold completed his Ph.D. at Imperial College London with Yiannis Demiris on online learning for assistive robots.
Harold's current research focuses on machine learning and decision-making for trustworthy collaborative robots. His work spans cognitive modeling (specifically human-robot trust) to physical systems (tactile intelligence with novel e-skins), and has been recognized with best paper award nominations at RSS, HRI, and IROS. Harold has served on the HRI committee as LBR Co-Chair (2019) and on the Technical Advances PC as a member (2020) and Chair (2021). He is an Associate Editor of the ACM Transactions on Human Robot Interaction (2021). He serves as PC member / reviewer for the top publication venues in AI (NeurIPS, ICML, ICLR, AAAI) and robotics (ICRA, IROS, RSS, HRI).
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_0MjiSeXvR2-1FfxN0Bq7vgLocation: Online Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_0MjiSeXvR2-1FfxN0Bq7vg
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Huan Liu (Arizona State University) - Social Media Mining: A Bountiful Frontier in AI
Thu, Oct 21, 2021 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Huan Liu , Arizona State University
Talk Title: Social Media Mining: A Bountiful Frontier in AI
Series: CS Colloquium
Abstract: Social media data differs from conventional data in many ways. It is not only big, but also noisy, linked, multimodal, and user-generated. Unprecedented opportunities thus emerge for CS and AI research through the lens of social data. In this talk, I use examples to illustrate: (1) fundamental problems associated with social media, that challenge common practice and existential understanding in machine learning and data mining; (2) intriguing questions, unique to social media, that can be answered by mining social media; and (3) how we can make a difference -“ that is, contribute to society at large - by developing novel socially responsible AI algorithms in our work on social media mining and AI. There are great opportunities ahead for research, teaching, and interdisciplinary collaborations in advancing knowledge in CS, AI, and data science.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. Huan Liu is Professor of Computer Science and Engineering at Arizona State University. He was recognized for excellence in teaching and research in Computer Science and Engineering at ASU. His research interests include AI, data mining, feature selection, and social media mining. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He graduated 32 PhD students at ASU and many of them won highly coveted awards (click here for their first job after graduation). He is Founding Field Chief Editor of Frontiers in Big Data, its Specialty Chief Editor of Data Mining and Management, Editor in Chief of ACM TIST, and Conference Co-Chair of ACM WSMD2022. His research has been funded by NSF, AFOSR, AFRL, ARL, ARO, DARPA, NASA, and ONR, among others. He is a Fellow of AAAI, AAAS, ACM, and IEEE.
Host: Laurent Itti
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Huan Liu (Arizona State University) - Nurturing Tomorrow's CS Leaders Together
Fri, Oct 22, 2021 @ 10:00 AM - 10:45 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Huan Liu , Arizona State University
Talk Title: Nurturing Tomorrow's CS Leaders Together
Abstract: With the recent surge of interest in and increasing need for AI, machine learning, and data science, we computer scientists and engineers are poised to welcome the golden age of computer science and reimagine new opportunities together for tomorrow's CS leaders. We will equip students with a solid theoretical foundation, state-of-the-art technology, and high-level skills so they are ready to be leaders in this fast-developing discipline. We will do this while enhancing diversity and equity on and off campus; promoting inclusiveness; and fostering a vibrant research and teaching ecosystem in which faculty and students excel in their unique ways. We strive to remain a global leader pursuing excellence in computer science education, research, and service and to stay at the forefront of computer science with world-class faculty and strong, aspiring students, and all in a student-focused, faculty-led, ethical, and creative learning environment.
Biography: Dr. Huan Liu is Professor of Computer Science and Engineering at Arizona State University. He was recognized for excellence in teaching and research in Computer Science and Engineering at ASU. His research interests include AI, data mining, feature selection, and social media mining. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He graduated 32 PhD students at ASU and many of them won highly coveted awards (click here for their first job after graduation). He is Founding Field Chief Editor of Frontiers in Big Data, its Specialty Chief Editor of Data Mining and Management, Editor in Chief of ACM TIST, and Conference Co-Chair of ACM WSMD2022. His research has been funded by NSF, AFOSR, AFRL, ARL, ARO, DARPA, NASA, and ONR, among others. He is a Fellow of AAAI, AAAS, ACM, and IEEE.
http://www.public.asu.edu/~huanliu
Host: Laurent Itti
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Nikhil Garg (Cornell University) - Combatting Gerrymandering with Social Choice: the Design of Multi-member Districts
Thu, Oct 28, 2021 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nikhil Garg, Cornell University
Talk Title: Combatting Gerrymandering with Social Choice: the Design of Multi-member Districts
Abstract: Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States -- winner-take-all single-member districts -- both enables substantial partisan gerrymandering and constrains `fair' redistricting, preventing proportional representation in legislatures. We study the design of multi-member districts (MMDs), in which each district elects multiple representatives, potentially through a non-winner-takes-all voting rule. We carry out large-scale analyses for the U.S. House of Representatives under MMDs with different social choice functions, under algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes -- under various multi-winner social choice functions -- into an algorithm that optimizes over an ensemble of maps. We find that with three-member districts using Single Transferable Vote, fairness-minded independent commissions would be able to achieve proportional outcomes in every state up to rounding, and advantage-seeking partisans would have their power to gerrymander significantly curtailed. Simultaneously, such districts would preserve geographic cohesion, an arguably important aspect of representative democracies. In the process, we open up a rich research agenda at the intersection of social choice and computational redistricting.
Biography: Nikhil Garg is an Assistant Professor of Operations Research and Information Engineering at Cornell Tech as part of the Jacobs Technion-Cornell Institute. His research interest is the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. He received his PhD from Stanford University, where he was part of the Society and Algorithms Lab and Stanford Crowdsourced Democracy Team. He has spent time at Uber, NASA, and Microsoft, and most recently was the Principal Data Scientist at PredictWise, which provides election analytics for political campaigns. Nikhil has received the INFORMS George Dantzig Dissertation Award, an honorable mention for the ACM SIGecom dissertation award, and 2nd place in the MSOM student paper competition.
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
