Events

CILVR Seminar: Rethinking AI Agents: Human-Centered Reinforcement Learning

Speaker: Stephanie Milani

Location: 60 Fifth Avenue, Room TBD
Videoconference link: https://nyu.zoom.us/j/91067963925

Date: Wednesday, February 25, 2026

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 novel algorithms 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.

Bio: Stephanie Milani is an Assistant Professor/Faculty Fellow at the Courant Institute of Mathematical Sciences at NYU. Her research focuses on building reinforcement learning agents to address human-centered and use-case-inspired challenges. Her work 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 Machine Learning & Systems, a 2024 Future Leader in Responsible Data Science & AI, and a 2024 Rising Star in Data Science. She received her Ph.D. in Machine Learning Department at Carnegie Mellon University (CMU) in 2025, where she was advised by Fei Fang. She received the CMU Machine Learning TA award, co-organized the MineRL international competition series at NeurIPS, and was awarded the CMU SCS Distinguished Dissertation Award Honorable Mention.