Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
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 health care 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, Stephanie Milani shows how human-centered reinforcement learning offers a promising framework for addressing this challenge. First, Milani focus on the issue of interpretability, presenting novel algorithms for learning transparent decision-making policies. Then, she shows 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, Milani presents her 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.
Speaker Biography
Stephanie Milani is a final-year PhD 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 the International Conference on Learning Representations, the Conference and Workshop on Neural Information Processing System (NeurIPS), and the ACM Conference on Human Factors in Computing Systems, and has received Best Paper Awards at the International Conference on Machine Learning Multimodal Foundation Model Meets Embodied AI and NeurIPS GenAI for Health workshops. Milani is a 2024 Michigan Institute for Data & AI in Society Future Leader in Responsible Data Science and AI and a Rising Star in Data Science. She has received a CMU machine learning teaching assistant award, co-organized the MineRL international competition series at NeurIPS, and received a Newman Civic Fellowship for her service to computer science education.