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Events for March 26, 2024
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CS Colloquium: Xiang Anthony Chen - Catalyzing AI Advances with Human-Centered Interactive Systems
Tue, Mar 26, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Xiang Anthony Chen, UCLA
Talk Title: Catalyzing AI Advances with Human-Centered Interactive Systems
Abstract: Despite the unprecedented advances in AI, there has always been a gap between how well an AI model performs and how such performance can serve humanity. In this seminar, I will describe my past work to close this gap. Specifically, I develop human-centered interactive systems that catalyze advances in AI to achieve three levels of objectives: aligning with human values, assimilating human intents, and augmenting human abilities. Further, I will discuss my ongoing and future research, focused on AI for scientific discovery, AI with Theory of Mind, and AI-mediated human communication. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Xiang ‘Anthony' Chen is an Assistant Professor in UCLA's Department of Electrical & Computer Engineering. He received a Ph.D. in the School of Computer Science at Carnegie Mellon University. Anthony's area of expertise is Human-Computer Interaction (HCI). His research employs human-centered design methods to build systems that catalyze advances in AI to better serve humanity, supported by NSF CAREER Award, ONR YIP Award, Google Research Scholar Award, Intel Rising Star Award, Hellman Fellowship, NSF CRII Award, and Adobe Ph.D. Fellowship. Anthony’s work has resulted in 55+ publications with three best paper awards and three honorable mentions in top-tier HCI conferences.
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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CAIS Webinar: Dr. Jessica Ridgway (University of Chicago) - Predictive Analytics for Engagement in HIV Care
Tue, Mar 26, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Jessica Ridgway, University of Chicago
Talk Title: Predictive Analytics for Engagement in HIV Care
Abstract: Engagement in care is essential for the health of people with HIV, but only half of people with HIV in the U.S. receive regular medical care. Dr. Ridgway will discuss her research utilizing machine learning models based on electronic medical record data to predict engagement in care among people with HIV. She has developed machine learning models using structured data as well as natural language processing of unstructured clinical notes. She will discuss challenges and pitfalls in utilizing electronic medical record data for HIV-related predictive modeling, as well as implications for implementation in clinical practice.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Jessica Ridgway, MD, MS, is an Associate Professor of Medicine in the Section of Infectious Diseases and Global Health and Director of Medical Informatics at the University of Chicago. She is Director of Predictive Analytics for the Chicago Center for HIV Elimination. Her research focuses on utilizing large electronic medical record databases to understand HIV epidemiology across the continuum of care and implementation of clinical informatics interventions to improve HIV care and prevention.
Host: USC Center for Artificial Intelligence in Society (CAIS)
More Info: https://usc.zoom.us/webinar/register/WN_gEn8OHXBQnmpYiWc9hJimw
Location: Zoom only - https://usc.zoom.us/webinar/register/WN_gEn8OHXBQnmpYiWc9hJimw
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/webinar/register/WN_gEn8OHXBQnmpYiWc9hJimw
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PhD Dissertation Defense - Aniruddh Puranic
Tue, Mar 26, 2024 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Aniruddh Puranic Committee: Jyotirmoy V. Deshmukh (Chair), Gaurav Sukhatme, Stefanos Nikolaidis, and Stephen Tu Title: Sample-Efficient and Robust Neurosymbolic Learning from Demonstrations Abstract: Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning (RL) without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, this thesis develops a neurosymbolic learning framework which is a hybrid method that integrates neural network-based learning with symbolic (e.g., rule, logic, graph) reasoning to leverage the strengths of both approaches. Specifically, this framework uses Signal Temporal Logic (STL) to express high-level robotic tasks and its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. This dissertation presents the LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer reward functions; these reward functions can then be used by reinforcement learning algorithms to obtain control policies. Experimental evaluations on several diverse set of environments show that the additional information in the form of formally specified task objectives allows the framework to outperform prior state-of-the-art LfD methods. Many real-world robotic tasks consist of multiple objectives (specifications), some of which may be inherently competitive, thus prompting the need for deliberate trade-offs. This dissertation then further extends the LfD-STL framework by a developing metric - performance graph - which is a directed graph that utilizes the quality of demonstrations to provide intuitive explanations about the performance and trade-offs of demonstrated behaviors. This performance graph also offers concise insights into the learning process of the RL agent, thereby enhancing interpretability, as corroborated by a user study. Finally, the thesis discusses how the performance graphs can be used as an optimization objective to guide RL agents to potentially learn policies that perform better than the (imperfect) demonstrators via apprenticeship learning (AL). The theoretical machinery developed for the AL-STL framework examines the guarantees on safety and performance of RL agents. https://usc.zoom.us/j/98964159897?pwd=a2ljaGNEOGcvMkl1WU9yZENPc0M1dz09
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Aniruddh Puranic
Event Link: https://usc.zoom.us/j/98964159897?pwd=a2ljaGNEOGcvMkl1WU9yZENPc0M1dz09
