William Overman
PhD Student, Stanford
wpo@stanford.edu
Bio
My research focuses on developing theoretically grounded approaches to AI safety and alignment [NeurIPS'24, NeurIPS'25], with a particular emphasis on AI control and scalable oversight. I'm especially interested in how tools from black-box uncertainty quantification, reinforcement learning, and theoretical computer science can help us reason rigorously about the behavior of powerful AI systems.
I’m a Ph.D. student in Operations, Information, and Technology at Stanford Graduate School of Business, advised by Mohsen Bayati. Before Stanford, I was a visiting researcher at the Institue for Basic Science in South Korea and earned an M.Sc. in Computer Science from UC Irvine. I graduated from Caltech in 2020 with a double major in Mathematics and Computer Science.
In addition to my primary focus on AI safety and alignment, my research also explores reinforecment learning [ICLR'22, RLC'25], causal inference [NeurIPS'24, arXiv'25], and AI applications in healthcare [ SSRN'23,arXiv'25]. Throughout my graduate studies, I have interned at Uber, where I have applied my research work in RL and causal inference to problems in the ridesharing and delivery marketplaces.
Publications
‡ indicates equal contribution. ※ indicates equal contribution, sole student.
Ask or Play: Scalable Oversight through Markov Potential Games
W Overman, M Bayati
NeurIPS'25 Workshop: ML×OR Workshop. 2025.
Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models
W Overman, M Bayati
NeurIPS'25: Neural Information Processing Systems. 2025.
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
S Shirani, Y Luo, W Overman, R Xiong, M Bayati
arXiv'25: arXiv preprint arXiv:2502.01106. 2025.
Accepted for presentation at the MSOM Technology, Innovation, and Entrepreneurship SIG, 2025.
Aligning Model Properties via Conformal Risk Control
W Overman, JJ Vallon, M Bayati
NeurIPS'24: Neural Information Processing Systems. 2024.
Higher-Order Causal Message Passing for Experimentation with Complex Interference
M Bayati‡, Y Luo‡, W Overman‡, S Shirani‡, R Xiong‡
NeurIPS'24: Neural Information Processing Systems. 2024.
Global convergence of multi-agent policy gradient in markov potential games
S Leonardos‡, W Overman※, I Panageas‡, G Piliouras‡
ICLR'22: International Conference on Learning Representations. 2022.
Independent natural policy gradient always converges in markov potential games
R Fox‡, SM McAleer‡, W Overman※, I Panageas‡
AISTATS'22: Artificial Intelligence and Statistics. 2022.
Ask or Play: Scalable Oversight through Markov Potential Games
W Overman, M Bayati
NeurIPS'25 Workshop: ML×OR Workshop. 2025.
Conformal Arbitrage: Risk-Controlled Balancing of Competing Objectives in Language Models
W Overman, M Bayati
NeurIPS'25: Neural Information Processing Systems. 2025.
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
S Shirani, Y Luo, W Overman, R Xiong, M Bayati
arXiv'25: arXiv preprint arXiv:2502.01106. 2025.
Accepted for presentation at the MSOM Technology, Innovation, and Entrepreneurship SIG, 2025.
On aligning prediction models with clinical experiential learning: A prostate cancer case study
JJ Vallon, W Overman, W Xu, N Panjwani, X Ling, S Vij, HP Bagshaw, ...
arXiv'25: arXiv preprint arXiv:2509.04053. 2025.
Aligning Model Properties via Conformal Risk Control
W Overman, JJ Vallon, M Bayati
NeurIPS'24: Neural Information Processing Systems. 2024.
Higher-Order Causal Message Passing for Experimentation with Complex Interference
M Bayati‡, Y Luo‡, W Overman‡, S Shirani‡, R Xiong‡
NeurIPS'24: Neural Information Processing Systems. 2024.
Beating price of anarchy and gradient descent without regret in potential games
I Sakos, S Leonardos, SA Stavroulakis, W Overman, I Panageas, G Piliouras
ICLR'24: International Conference on Learning Representations. 2024.
Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism
K Yu, D Lee, W Overman, D Lee
RLC 2025 (Reinforcement Learning Conference).
Journal version: Reinforcement Learning Journal (2025).
Occupancy Prediction with Patient Data: Evaluating Time-Series, Patient-Level Aggregation, and Deep Set Models
SH Kim‡, W Overman※, J Pauphilet‡, WC Cha
Major Revision at Manufacturing & Service Operations Management (MSOM).
Global convergence of multi-agent policy gradient in markov potential games
S Leonardos‡, W Overman※, I Panageas‡, G Piliouras‡
ICLR'22: International Conference on Learning Representations. 2022.
Independent natural policy gradient always converges in markov potential games
R Fox‡, SM McAleer‡, W Overman※, I Panageas‡
AISTATS'22: Artificial Intelligence and Statistics. 2022.
Some Ordered Ramsey Numbers of Graphs on Four Vertices
W Overman, JF Alm, K Coffey, C Langhoff
Australasian Journal of Combinatorics, Vol 88(3), 266–281. 2024.
Vitæ
Full Resume in PDF.
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Stanford 2022 - nowPhD Student
GSB -
Uber Summer 2023-Applied Scientist, Intern
Airports Team & Uber Eats -
UC Irvine 2020 - 2022Masters Student
Computer Science -
IBS, South Korea 2021Visiting Researcher
Discrete Mathematics Group -
Caltech 2016 - 2020B.Sc.
Mathematics and Computer Science -
IBS, South Korea Summer 2019Summer Undergraduate Research Fellowship
Discrete Mathematics Group -
Oxford Summer 2018Summer Undergraduate Research Fellowship
Mathematical Instutute