LIDS researchers Kaveh Alimohammadi and Navid Azizan, with collaborators Hao Wang, Ojas Gulati, and Akash Srivastava, have received the 2026 MIT Prize for Open Data. Presented annually by the MIT School of Science and the MIT Libraries, the MIT Prize for Open Data highlights the value of open data at MIT, aiming to encourage the next generation of researchers.
The paper, “Differentially Private Synthetic Data Generation for Relational Databases,” presents a method that addresses a fundamental barrier to open science: sharing the scientific value of sensitive, interconnected data while rigorously protecting individual privacy. By providing publicly available, reproducible tools, backed by foundational theoretical advances, this work has the potential to unlock new open-data resources across disciplines.
Kaveh Alimohammadi is a fourth-year PhD student in the Institute for Data, Systems, and Society (IDSS) and a student member of LIDS. His research focuses on trustworthy machine learning and post-training of large language models, emphasizing inference-time scaling, uncertainty quantification, and efficient inference algorithms.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Associate Professor at MIT in MechE and IDSS and is a Principal Investigator in LIDS. His research lab focuses on reliable and trustworthy AI systems, with applications to high-stakes and safety-critical settings. Azizan is a recipient of several awards, including the National Science Foundation CAREER Award and research awards from Amazon, Google, and MathWorks, among others. His work has been recognized with best paper awards at several venues, including Learning for Dynamics and Control (L4DC), the INFORMS Junior Faculty Paper Competition, and ACM Greenmetrics. He was named in the list of Outstanding Academic Leaders in Data by the CDO Magazine for two consecutive years. His teaching and mentorship have been recognized with the Joseph A. Martore (1975) Excellence in Teaching Award, the Frank E. Perkins Award for Excellence in Graduate Advising (MIT Institute Award), and the UROP Outstanding Mentor Award.
Additional contributing authors include Hao Wang, a senior research scientist in the MIT-IBM Watson AI Lab; Ojas Gulati, an undergraduate student in computer science and mechanical engineering; and Akash Srivastava, a principal research scientist in the MIT-IBM Watson AI Lab.
Learn more about the prize, read the paper, and explore the GitHub.
