Sadegh Shirani
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About me
About me
I am a fourth-year PhD student in Operations, Information & Technology at the Stanford Graduate School of Business, where I'm fortunate to be advised by Mohsen Bayati.
My research explores questions related to causal inference, reinforcement learning, stochastic modeling, and stochastic optimization, particularly in the context of designing experiments in the presence of complex network interference effects.
Email: sshirani 'at' stanford 'dot' edu
Working Papers
Working Papers
(1) Can We Validate Counterfactual Estimations in the Presence of General Network Interference?
with Y. Luo, W. Overman, R. Xiong, M. Bayati, 2025.
Accepted for presentation at the MSOM Technology, Innovation, and Entrepreneurship SIG, 2025
(2) Asymptotic Analysis of Multi-Class Advance Patient Scheduling
with H. Abouee-Mehrizi and M. K. S. Faradonbeh, Major Revision at Management Science, 2024.
Second place, INFORMS Health Applications Society (HAS) Best Student Paper Competition, 2023
Finalist, Canadian Operations Research Society Student Paper Award, 2022
Accepted for presentation at the MSOM Healthcare SIG, 2023
Journal Papers
Journal Papers
(1) Causal Message Passing for Experiments with Unknown and General Network Interference
with M. Bayati, Proceedings of the National Academy of Sciences (PNAS) 121(40), 2024.
Honorable mention, George Nicholson Student Paper Competition, 2024
Finalist, MSOM Student Paper Competition, 2024
Oral presentation at the Conference on Digital Experimentation @ MIT, 2024
(2) Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games
with M. Ameli, JP Lebacque, H. Abouee-Mehrizi, L. Leclercq, Transportation Science 56(6):1483-1504, 2022.
Best Paper Award, INFORMS TSL Urban Transportation SIG, 2022
Refereed Conference Papers
Refereed Conference Papers
(1) Higher-Order Causal Message Passing for Experimentation Under Unknown Interference
with M. Bayati, Yuwei Luo, William Overman, Ruoxuan Xiong, Neural Information Processing Systems (NeurIPS), 2024.
(2) Online Reinforcement Learning in Stochastic Continuous-Time Systems
with M. K. S. Faradonbeh, Proceedings of Thirty Sixth Conference on Learning Theory (COLT), PMLR 195:612-656, 2023.
(3) Thompson Sampling Efficiently Learns to Control Diffusion Processes
with M. K. S. Faradonbeh and M. Bayati, Neural Information Processing Systems (NeurIPS), 2022.
(4) Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
with M. K. S. Faradonbeh, IFAC International Workshop on Adaptive and Learning Control Systems (ALCOS), 2022
(5) Mean Field Games Framework to Departure Time Choice Equilibrium in Urban Traffic Networks
with M. Ameli, JP. Lebacque, H. Abouee-Mehrizi, L. Leclercq, Transportation Research Board 100th Annual Meeting (TRB), 2021
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