Rate My Professor Maryam Aliakbarpour

Maryam Aliakbarpour is the Michael B. Yuen and Sandra A. Tsai Assistant Professor in the Department of Computer Science at Rice University, a position she began in fall 2023. She is affiliated with the Ken Kennedy Institute and serves on the department's Graduate Committee. Aliakbarpour earned her Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2020, advised by Ronitt Rubinfeld, with a dissertation titled "Distribution Testing: Classical and New Paradigms." She previously received an M.S. from MIT in 2015, with a thesis on "Learning and Testing Junta Distributions over Hypercubes," and a B.S. in Computer Engineering (Software) from Sharif University of Technology in 2013.

Prior to joining Rice, she held a joint postdoctoral research position at Boston University, hosted by Adam Smith, and Northeastern University, hosted by Jonathan Ullman. She was a Postdoctoral Research Associate at the University of Massachusetts Amherst from fall 2020 to summer 2021 under Andrew McGregor and participated as a Visiting Participant in the Simons Institute for the Theory of Computing's Probability, Geometry, and Computation in High Dimensions Program in fall 2020. Her research in theoretical computer science focuses on statistical learning theory, differential privacy, hypothesis testing, and algorithms with computational constraints including limited time, memory, privacy preservation, and fairness. She develops efficient algorithms for statistical problems in realistic settings, exploring tradeoffs in resources and performance using models like PAC learning.

Aliakbarpour has received the Rising Stars in EECS award in 2018 and the Neekeyfar Award from MIT's Office of Graduate Education. Her key publications include "Adversarially Robust Quantum State Learning and Testing" (FOCS 2025, with Vladimir Braverman, Nai-Hui Chia, Yuhan Liu), "Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor" (NeurIPS 2025, with Zhan Shi, Ria Stevens, Vincent X. Wang), "Optimal Hypothesis Selection in (Almost) Linear Time" (NeurIPS 2024, with Mark Bun, Adam Smith), "Differentially Private Medians and Interior Points for Non-Pathological Data" (ITCS 2024, with Rose Silver, Thomas Steinke, Jonathan Ullman), and "Hypothesis Selection with Memory Constraints" (NeurIPS 2023, with Mark Bun, Adam Smith). Her work advances privacy-preserving statistical inference and resource-efficient machine learning.