Ahmadreza Momeni |
I am a Ph.D. student in the Department of Electrical Engineering, advised by Professor Yonatan Gur. My research interest lies in statistical machine learning and data-driven sequential decision-making under uncertainty, with applications in revenue management and service operations, including online retail operations and recommender systems. I am interested in studying dynamic optimization problems where there are uncertainties about key characteristics of the environment such as the information acquisition process, the payoff structure, and the feedback structure, as well as in designing and analyzing decision-making policies that are adaptive with respect to these characteristics.
Ph.D. in Electrical Engineering, Stanford University, 2015 - present
Master in Electrical Engineering, Stanford University, 2015 - 2018
B.S. in Electrical Engineering, Sharif University of Technology, 2011 - 2015
Adaptive Sequential Experiments with Unknown Information Arrival Processes
with Yonatan Gur. Manufacturing & Service Operations Management (forthcoming)
an early version appeared in Advances in Neural Information Processing Systems 31 (NIPS’18)-link to a short video
recognized as a finalist of the 2018 Applied Probability Society (APS) best student paper
Smoothness-Adaptive Contextual Bandits
with Yonatan Gur and Stefan Wager. Operations Research (forthcoming)
recieved honorable mention in the 2020 George Nicholson student paper competition
see poster presented at the ICML 2020 Workshop on Theoretical Foundations of Reinforcement Learning
Delay Analysis of Network Coding in Multicast Networks with Markovian Arrival Processes: A Practical Framework in Cache-Enabled Networks
with Fatemeh Rezayi and Babak Khalaj. IEEE Vehicular Technology Society
EE263: Introduction to Linear Dynamical Systems, Instructor (Lecturer),
Summer 2016
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing,
communications, control systems, etc.
EE364a: Convex Optimization I, Instructor (Lecturer),
Summer 2017
Recognizing and solving convex optimization problems that arise in applications.
OIT269: MSx - Operations and Strategies, Teaching Assistant, Winter 2019
Operational problems and challenges faced by managers, as well as conceptual models and analytical techniques that are broadly applicable in confronting such problems.
OIT276: Data and Decisions, Teaching Assistant, Winter 2021
Statistics topics such as hypothesis testing and linear regression for MBA students