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Conferences, Lectures, & Seminars
Events for February
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CS Colloquium: Ankur Mehta (UCLA) - Towards $1 robots
Tue, Feb 01, 2022 @ 02:30 PM - 03:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ankur Mehta, University of California, Los Angeles
Talk Title: Towards $1 robots
Series: Computer Science Colloquium
Abstract: Note: *New time: 2:30PM-3:50PM PT*
Robots are pretty great -- they can make some hard tasks easy, some dangerous tasks safe, or some unthinkable tasks possible. And they're just plain fun to boot. But how many robots have you interacted with recently? And where do you think that puts you compared to the rest of the world's people?
In contrast to computation, automating physical interactions continues to be limited in scope and breadth. I'd like to change that. But in particular, I'd like to do so in a way that's accessible to everyone, everywhere. In our lab, we work to lower barriers to robotics design, creation, and operation through
material and mechanism design, computational tools, and mathematical analysis. We hope that with our efforts, everyone will be soon able to enjoy the benefits of robotics to work, to learn, and to play.
**Prof. Ankur Mehta will give his talk in person at SGM 124 and we will also host the talk over Zoom.**
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_dUyqi3ZTQiWuseUgfp4fYw
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Now, this is a story all about how
My life took me to where I am now
And I'd like to take a minute, just sit right there
I'll tell you how I became a prof at UCLA.
From East Pennsylvania born and raised
MIT is where I spent the next of my days
Getting my Masters and Bachelor's too
ECE is the field I did then pursue.
Then a couple of years until I finally would
From California, Berkeley get my doctor hood
I got in one lil' postdoc at MIT CSAIL
And then I moved to LA just south of Bel Air.
Prof. Ankur Mehta is an assistant professor of Electrical and computer Engineering at UCLA, and directs the Laboratory for Embedded Machines and Ubiquitous Robots (LEMUR). Pushing towards his visions of a future filled with robots, his research interests involve printable robotics, rapid design and
fabrication, control systems, and multi-agent networks. He has received the NSF CAREER award and a Samueli fellowship, and has received best paper awards in the IEEE Robotics & Automation Magazine and the International Conference on Intelligent Robots and Systems (IROS).
Prior to joining the UCLA faculty, Prof. Mehta was a postdoc at MIT's Computer Science and Artificial Intelligence Laboratories investigating design automation for printable robots. Before to that, he conducted research as a graduate student at UC Berkeley in wireless sensor networks and systems, small autonomous aerial robots and rockets, control systems, and micro-electro-mechanical systems (MEMS).
When not in the lab, Ankur enjoys puzzles, ultimate frisbee, board games.
Host: Stefanos Nikolaidis
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
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CS Colloquium: Sanjiban Choudhury (Cornell University) - Interactive Imitation Learning: Planning Alongside Humans
Tue, Feb 08, 2022 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sanjiban Choudhury, Cornell University
Talk Title: Interactive Imitation Learning: Planning Alongside Humans
Series: Computer Science Colloquium
Abstract: Advances in machine learning have fueled progress towards deploying real-world robots from assembly lines to self-driving. However, if robots are to truly work alongside humans in the wild, they need to solve fundamental challenges that go beyond collecting large-scale datasets. Robots must continually improve and learn online to adapt to individual human preferences. How do we design robots that both understand and learn from natural human interactions?
In this talk, I will dive into two core challenges. First, I will discuss learning from natural human interactions where we look at the recurring problem of feedback-driven covariate shift. We will tackle this problem from a unified framework of distribution matching. Second, I will discuss learning to predict human intent where we look at the chicken-or-egg problem of planning with learned forecasts. I will present a graph neural network approach that tractably reasons over latent intents of multiple actors in the scene. Finally, we will demonstrate how these methods come together to result in a self-driving product deployed at scale.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9w
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Sanjiban Choudhury is a Research Scientist at Aurora Innovation and soon-to-be Assistant Professor at Cornell University. His research goal is to enable robots to work seamlessly alongside human partners in the wild. To this end, his work focuses on imitation learning, decision making and human-robot interaction. He obtained his Ph.D. in Robotics from Carnegie Mellon University and was a Postdoctoral fellow at the University of Washington. His research has received best paper awards at ICAPS 2019, finalist for IJRR 2018, and AHS 2014, and winner of the 2018 Howard Hughes award. He is a Siebel Scholar, class of 2013.
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9wLocation: Online - Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9w
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: Julian Togelius (NYU) - Generating content for fun, games, and intelligence
Tue, Feb 15, 2022 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Julian Togelius, New York University
Talk Title: Generating content for fun, games, and intelligence
Series: Computer Science Colloquium
Abstract: Designing and developing video games is hard work. Much of that work goes into designing the environments, levels, characters, items, and graphical assets that the player interacts with. For decades, some games have featured procedural content generation, where parts of the game was generated algorithmically as it was being played. Recently, we have seen an explosion of interest in this field, with many new techniques being applied to content generation problems. As video games are increasingly shaping culture and society, including visions of the so called "metaverse", these problems and their solutions are becoming increasingly important. But could procedural content generation also play a role outside of what we think of as games, in fields such as architecture, interior design, and robotics? And can we further the development of artificial general intelligence by generating new tasks and environments to optimally challenge our developing artificial intelligences? I will lay out some visions for this and show some recent approaches to generating game content, based on methods as diverse as evolutionary computation, reinforcement learning, self-supervised learning, and constraint solving.
***Prof. Julian Togelius will give his talk in person at SGM 124 and we will also host the talk over Zoom.***
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1w
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, and a co-founder of modl.ai. He works on artificial intelligence for games and on games for artificial intelligence. His current main research directions involve procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. Additionally, he works on topics in evolutionary computation, quality-diversity algorithms, and reinforcement learning. From 2018 to 2021, he was the Editor-in-Chief of the IEEE Transactions on Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex. He has previously worked at IDSIA in Lugano and at the IT University of Copenhagen.
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1wLocation: Seeley G. Mudd Building (SGM) - 124
WebCast Link: https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1w
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: Siddharth Srivastava (Arizona State University) - Principles and Algorithms for Data-Efficient Assistive Sequential Decision Making
Tue, Feb 22, 2022 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Siddharth Srivastava, Arizona State University
Talk Title: Principles and Algorithms for Data-Efficient Assistive Sequential Decision Making
Series: Computer Science Colloquium
Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI systems that can plan and act over multiple timesteps. Our results show that abstraction mechanisms are invaluable not only in improving the efficiency of sequential decision making, but also in developing AI systems that can explain their own behavior to non-experts, and in computing user-interpretable assessments of the limits and capabilities of Black-Box AI systems. I will also present some of our work on learning the requisite abstractions in a bottom-up fashion. Throughout the talk I will highlight the theoretical guarantees that our methods provide along with results from empirical evaluations featuring decision-support/digital AI systems and physical robots.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Join Zoom Meeting
https://usc.zoom.us/j/99395482251
Meeting ID: 993 9548 2251
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Host: Sven Koenig
Webcast: https://usc.zoom.us/j/99395482251Location: Online - Zoom
WebCast Link: https://usc.zoom.us/j/99395482251
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Distinguished Lecture: Hod Lipson (Columbia University) - Automating discovery: From cognitive robotics to particle physics
Tue, Feb 22, 2022 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hod Lipson, Columbia University
Talk Title: Automating discovery: From cognitive robotics to particle physics
Series: Computer Science Distinguished Lecture Series
Abstract: Can machines discover scientific laws automatically? Despite the prevalence of big data, the process of distilling data into scientific laws has resisted automation. Particularly challenging are situations with small amounts of data that is difficult or expensive to collect. This talk will outline a series of recent research projects, starting with self-reflecting robotic systems, and ending with machines that can formulate hypotheses, design experiments, and interpret the results, to discover new scientific laws. We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.
Register in advance for this webinar at:
https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hg
After registering, attendees will receive a confirmation email containing information about joining the webinar.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Hod Lipson is a professor of Engineering at Columbia University in New York, and a co-author of the award winning book "Fabricated: The New World of 3D printing", and "Driverless: Intelligent cars and the road ahead". His work on self-aware and self-replicating robots challenges conventional views of robotics, and his TED talk on self-aware machines is one of the most viewed presentations on AI. Lipson directs the Creative Machines Lab, which pioneers new ways to make machines that create, and machines that are creative. For more information visit http://hodlipson.com
Host: Stefanos Nikolaidis
Webcast: https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hgLocation: Online - Zoom Webinar
WebCast Link: https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hg
Audiences: Everyone Is Invited
Contact: Computer Science Department
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
