Selecting Decision-Relevant Concepts in Reinforcement Learning
Alleviates manual concept selection burden by introducing the first algorithm for automatically selecting the concepts an RL agent should use for decision making.
Stephanie Milani
I am a postdoctoral researcher at Microsoft Research NY. I am also a Visiting Researcher at NYU Courant. In 2027, I will join Johns Hopkins University as an Assistant Professor in the Computer Science Department. My goal is to create practical AI agents that can learn from feedback to complement and augment human abilities. This involves designing techniques for reinforcement learning, foundation models, and human-centered AI.
I did my PhD in Machine Learning at Carnegie Mellon University, where I was advised by Fei Fang. I then spent a year as an Assistant Professor / Faculty Fellow at NYU Courant, where I worked with Rajesh Ranganath and Sumit Chopra. I am a Rising Star in ML & Systems, a Future Leader in Responsible Data Science & AI, and a Rising Star in Data Science. My dissertation received Honorable Mention for the SCS Dissertation Award. During my PhD, I interned at Microsoft Research twice, once with Geoff Gordon and once with Katja Hofmann.