Events

Faculty Candidate Talk Rethinking AI Agents: Human-Centered Reinforcement Learning
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Tuesday, April 08, 2025, 10:30am - 12:00pm

Speaker: Stephanie Milani

Bio

Stephanie Milani is a final-year Ph.D. candidate in the Machine Learning Department at Carnegie Mellon University. Her research focuses on building reinforcement learning agents to address human-centered and use-case-inspired challenges. Her research has been published at top machine learning and human-computer interaction venues, including ICLR, NeurIPS, and CHI, and received best paper awards at the ICML MFM-EAI and NeurIPS GenAI4Health workshops. Stephanie is a 2025 Rising Star in ML & Systems, a 2024 Future Leader in Responsible Data Science & AI, and a 2024 Rising Star in Data Science. She received the CMU Machine Learning TA award, co-organized the MineRL international competition series at NeurIPS, and received the Newman Civic Fellowship for her service to computer science education.

Location : CoRE 301

Committee: 

Event Type: Faculty Candidate Talk

Abstract: : AI agents will soon be as commonplace as smartphones. These agents will make sequences of interconnected decisions that impact human lives—from serving as decision support in healthcare to shaping educational paths for millions of students. A defining challenge for the future of AI is how to build agents that can effectively operate in and adapt to these human environments.In this talk, I show how human-centered reinforcement learning offers a promising framework for addressing this challenge. First, I focus on the issue of interpretability, presenting a novel algorithm for learning transparent decision-making policies. Then, I show how human-centered design can be used to define the objectives for AI agents, exemplified through a grounded use case in mental health. Finally, recognizing that complex human domains often defy precise specification, I present our benchmark for AI agents to learn from human feedback for complex tasks. Together, this work illustrates how human-centered reinforcement learning is a valuable approach for developing AI agents that can learn from and for the people whose lives they impact.

Organization: 

Contact  Professor Lirong Xia

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https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
Password: 550978