Upcoming AI Events

AI Seminar: Toward Addressing Evaluation and Explanation Challenges in Scientific ML Applications

Wednesday, May 25, 2022 - 1:00pm to 2:00pm
Zoom: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT09

Speaker Information

Shusen Liu
Research Scientist
Machine Intelligence Group
Lawrence Livermore National Laboratory

Abstract

Although the influence of deep learning in scientific domains is unmistakable, there are still fundamental barriers to utilizing these complex models for scientific discovery due in part to our inability to directly translate their predictive capabilities into scientific understanding. The root of the problem is twofold: 1) the challenge to evaluate the model in the context of the application; 2) the difficulties of reasoning about such models in terms that domain scientists can easily understand for knowledge extraction. This talk will provide a closer look at these unique challenges associated with applying deep learning to scientific applications. And cover some of our works for addressing the evaluation and explanation challenges in Scientific ML. Specifically, we will show how topological data analysis plays a crucial role in evaluating deep surrogate models for fusion science, and how deep generative models allow us to explore hypothetical materials and obtain actionable explanations that lead to improved material performance.

Speaker Bio

Shusen Liu is a research scientist with the Machine Intelligence Group at the Lawrence Livermore National Laboratory (LLNL).  His interests include fundamental research in explainable AI and high-dimensional data visualization, as well as their impact on scientific applications for advancing domain understanding. He received a Ph.D.in computing from the University of Utah in 2017.

AI Seminar: Enabling Humans and Robots to Predict the Other’s Behavior from Small Datasets

Wednesday, June 1, 2022 - 1:00pm to 2:00pm
Zoom: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5UT09

Speaker Information

Vaibhav Unhelkar
Assistant Professor of Computer Science
Rice University

Abstract

We are steadily moving towards a future where humans work with robotic assistants, teammates, and even trainers. Towards realizing this future and mitigating its adverse side effects, I will share two techniques that together enable humans and robots to predict the other’s task-oriented behavior. First, I will present AI Teacher: an interactive machine teaching framework to assist humans in acquiring mental models of robots. By building upon human’s natural ability to model others (Theory of Mind), the AI Teacher framework reduces the number of interactions it takes for humans to arrive at predictive models of robot behavior. Second, I will discuss BTIL, an imitation learning approach for enabling robots to arrive at predictive models of humans’ collaborative behavior. Discussions of both these techniques will highlight the need as well as solutions for sample-efficient learning in settings of human-robot collaboration.

Speaker Bio

Vaibhav Unhelkar is an Assistant Professor of Computer Science at Rice University, where he leads a research group in the emerging area of Human-Centered AI. Ongoing research in his group includes development of algorithms and systems that enable “robots to work with humans” and “humans to become informed users of robots.” Unhelkar received his doctorate in Autonomous Systems at MIT (2020) and completed his undergraduate education at IIT Bombay (2012). He serves as an Associate Editor for IEEE Robotics and Automation Letters and is the recipient of JPMC AI Early Career Researcher Award and AAMAS 2022 Best PC Member Award. Before joining Rice, Unhelkar worked as a robotics researcher at X, the Moonshot Factory (formerly, Google X).

Past AI Events

Sinisa Todorovic
Wednesday, May 18, 2022 - 1:00pm to 2:00pm

Karthika Mohan and Chi Zhang
Wednesday, May 11, 2022 - 1:00pm to 2:00pm

Roni Khardon
Wednesday, May 4, 2022 - 1:00pm to 2:00pm

Balaji Lakshminarayanan
Wednesday, April 27, 2022 - 1:00pm to 2:00pm

Brian Wood
Wednesday, April 20, 2022 - 1:00pm to 2:00pm

Bolei Zhou
Wednesday, April 13, 2022 - 1:00pm to 2:00pm

Heather Knight
Wednesday, April 6, 2022 - 1:00pm to 2:00pm

Kai Ming Ting
Wednesday, March 30, 2022 - 5:00pm to 6:00pm

Sharon Yixuan Li
Wednesday, March 9, 2022 - 1:00pm to 2:00pm

Richard Mallah
Wednesday, March 2, 2022 - 1:00pm to 2:00pm

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