Stephanie Milani
I am a final-year Ph.D. candidate in the Machine Learning Department at Carnegie Mellon University, where I am advised by Fei Fang.
My ultimate goal is to create practical AI agents that can learn from feedback to complement and augment human abilities. This involves studying, building, and improving techniques for reinforcement learning, foundation models, and human-centered AI.
I am a Future Leader in Responsible Data Science & AI and a Rising Star in Data Science.
My research has has received various awards, including Best Paper at a NeurIPS workshop and Outstanding Paper at an ICML workshop. My work has also been featured in Nature Journal, VentureBeat, The Verge, Science News Explores, and BBC News, among others.
During my Ph.D., I interned at Microsoft Research twice, once with Geoff Gordon and once with Katja Hofmann.
Before that, I completed my B.S. in Computer Science and B.A. in Psychology at the University of Maryland, Baltimore County, where I worked with Marie desJardins and Cynthia Matuszek.
Alongside my research, I am committed to excellence in teaching and to broadening inclusion across academia.
For my teaching, I have been recognized with a Teaching Award from the Carnegie Mellon Machine Learning Department, where I served as a TA for 10-777 Historical Advances in ML & AI and Head TA for 10-405/10-605 ML with Large Datasets.
For my service, I was nationally recognized with a Newman Civic Fellowship.
I co-organized two international AI competition series, the MineRL Diamond and BASALT Competitions at NeurIPS (2019 -- 2022), as well as the 2023 ICML WiML Un-Workshop.
🎓 On the Academic Job Market!
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Selected Publications
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels
Zhuorui Ye*,
Stephanie Milani*,
Geoffrey J. Gordon,
Fei Fang
ICLR, 2025
RLC TAFM Workshop, 2024 (Spotlight); RLC InterpPol Workshop, 2024 (Oral)
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
Ruiyi Wang*,
Stephanie Milani*,
Jamie C. Chiu,
Shaun M. Eack,
Travis Labrum,
Samuel M. Murphy,
Nev Jones,
Kate Hardy,
Hong Shen,
Fei Fang,
Zhiyu Zoey Chen
EMNLP, 2024
NeurIPS FM-EduAssess Workshop, 2024 (Oral); NeurIPS GenAI for Health Workshop, 2024 (Best Paper Award)
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning
Aravind Venugopal,
Stephanie Milani,
Fei Fang,
Balaraman Ravindran
AAMAS, 2024
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
Stephanie Milani,
Anssi Kanervisto,
Karolis Ramanauskas,
Sander Schulhoff,
Brandon Houghton,
Rohin Shah
NeurIPS Datasets & Benchmarks Track, 2023 (Oral)
ICML MFM-EAI Workshop, 2024 (Outstanding Paper Award)
Explainable Reinforcement Learning: A Survey and Comparative Review
Stephanie Milani,
Nicholay Topin,
Manuela Veloso,
Fei Fang
ACM CSUR Special Issue on Trustworthy AI, 2023
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani,
Arthur Juliani,
Ida Momennejad,
Raluca Georgescu,
Jaroslaw Rzpecki,
Alison Shaw,
Gavin Costello,
Fei Fang,
Sam Devlin,
Katja Hofmann
CHI, 2023
Uni[MASK]: Unified Inference in Sequential Decision Problems
Micah Carroll,
Jessy Lin,
Orr Paradise,
Raluca Georgescu,
Mingfei Sun,
David Bignell,
Stephanie Milani,
Katja Hofmann,
Matthew Hausknecht,
Anca Dragan,
Sam Devlin
NeurIPS, 2022 (Oral)
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning
Stephanie Milani*,
Zhicheng Zhang*,
Nicholay Topin,
Zheyuan Ryan Shi,
Charles Kamhoua,
Evangelos E. Papalexakis,
Fei Fang
ECML-PKDD, 2022
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Nicholay Topin,
Stephanie Milani,
Fei Fang,
Manuela Veloso
AAAI, 2021
Harnessing the Power of Deception in Attack Graph-Based Security Games
Stephanie Milani,
Weiran Shen,
Kevin S. Chan,
Sridhar Venkatesan,
Nandi O. Leslie,
Charles Kamhoua,
Fei Fang
GameSec, 2020
Perceptions of Domestic Robots' Normative Behavior Across Cultures
Huao Li,
Stephanie Milani,
Vigneshram Krishnamoorthy,
Michael Lewis,
Katia Sycara
AIES, 2019
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I got this great website here.
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