I am a PhD student at MIT, working under the supervision of Ankur Moitra. I studied my master’s in the algorithms and complexity group at the University of Waterloo where I was fortunate to be advised by Gautam Kamath.
I was named a 2026 Apple Scholar in AIML.
Google Scholar / DBLP / arxiv / Github / Twitter
Learning Gaussian Graphical Models from a Glauber Trajectory
without Mixing
Eric Shen*, Tony Wu*, Mahbod Majid,
Ankur Moitra.
ICML 2026 (to appear).
Computation-Utility-Privacy Tradeoffs in Bayesian
Estimation
Sitan Chen, Jingqiu Ding, Mahbod Majid,
Walter McKelvie.
STOC 2026 (to appear). arxiv
Presented at TPDP
2026.
Sample-Optimal Private Regression in Polynomial Time
Prashanti Anderson, Ainesh Bakshi, Mahbod Majid, Stefan
Tiegel.
STOC 2025. arxiv
Presented at TPDP
2025.
On the Consistent Recovery of Joint Distributions from
Conditionals
Mahbod Majid*, Rattana Pukdee*,
Vishwajeet Agrawal*, Burak Varıcı, Pradeep Kumar Ravikumar.
AISTATS 2025. paper
Private Mean Estimation with Person-Level Differential
Privacy
Sushant Agarwal, Gautam Kamath, Mahbod Majid,
Argyris Mouzakis, Rose Silver, Jonathan Ullman.
SODA 2025. arxiv
Presented at TPDP
2025.
Sample-Efficient Private Learning of Mixtures of
Gaussians
Hassan Ashtiani, Mahbod Majid, Shyam
Narayanan.
NeurIPS 2024 (Spotlight Presentation) arxiv
Presented at TPDP
2025.
Robustness Implies Privacy in Statistical
Estimation
Samuel B. Hopkins, Gautam Kamath, Mahbod
Majid, Shyam Narayanan.
STOC 2023. arxiv
Presented at TPDP
2023 (Oral Presentation).
Efficient Mean Estimation with Pure Differential Privacy via
a Sum-of-Squares Exponential Mechanism
Samuel B.
Hopkins, Gautam Kamath, Mahbod Majid.
STOC 2022. arxiv video
Presented at FORC
2022, non-archival track.
Presented at TPDP 2022.
I help organize MIT ML Tea with Kiril Bangachev and Julia Chae.
mahbod at mit.edu