Dutta, Sanghamitra
Maryland Applied Graduate Engineering
EDUCATION
- Ph.D., Electrical and Computer Engineering, Carnegie Mellon University
- M.S., Electrical and Computer Engineering, Carnegie Mellon University
- B. Tech., Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur
AWARDS
- 2025 NSF MPS SPEED Award (CoPI)
- 2024 NSF CAREER Award
- 2024 George Corcoran Memorial Award
- 2024 Google Gift Funding
- 2023 JPMorgan Faculty Award
- 2023 Northrop Grumman Seed Grant
- 2022 Simons Institute Fellowship for Causality
- 2021 A G Milnes Outstanding Thesis Award
- 2020 Cylab Presidential Fellowship
- 2019 K&L Gates Presidential Fellowship in Ethics and Computational Technologies
- 2019 Axel Berny Presidential Graduate Fellowship
- 2017 Tan Endowed Graduate Fellowship
- 2016 Prabhu and Poonam Goel Graduate Fellowship
- 2015 Nilanjan Ganguly Memorial Award for Best B. Tech. Thesis
- 2014 HONDA Young Engineer and Scientist Award
ABOUT
Sanghamitra Dutta is an assistant professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park since Fall 2022. She is also affiliated with the Center for Machine Learning (CML) at UMIACS, the Department of Computer Science, the Values-Centered Artificial Intelligence (VCAI), the Applied Mathematics & Statistics, and Scientific Computation (AMSC), and the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM). Prior to joining UMD, she was a senior research associate at JPMorgan Chase AI Research New York in the Explainable AI Centre of Excellence (XAI CoE). She received her Ph.D. and Master's from Carnegie Mellon University and B. Tech. from IIT Kharagpur, all in Electrical and Computer Engineering.
Her research interests broadly revolve around efficient, reliable, and trustworthy machine learning. She is particularly interested in addressing the challenges concerning explainability, efficiency, robustness, and ethics by bringing in novel foundational perspectives deep-rooted in information theory, statistics, causality, and optimization. Her research has featured in New Scientist and Montreal AI Ethics Brief, and also been adopted as part of the fair-lending model review at JPMorgan. She is also interested in AI for accelerated material discovery.
In her prior work, she has also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale distributed machine learning, using tools from coding theory (an emerging area called “coded computing”). Her results on coded computing has received substantial attention from across disciplines.
She is a recipient of the 2024 NSF CAREER Award, 2024 George Corcoran Memorial Award, 2023 JPMorgan Faculty Award, 2022 Simons Institute Fellowship for Causality, 2021 AG Milnes Outstanding Thesis Award from CMU, and 2019 K&L Gates Presidential Fellowship. She has also pursued research internships at IBM Research and Dataminr. She recently featured in the list of 100 Brilliant Women in AI Ethics 2025.
ENEE621: Detection and Estimation Theory Spring 2023, Spring 2025
ENEE436: Foundations of Machine Learning Fall 2022, Fall 2023, Fall 2024
CMSC848W/ENEE739F: Selected Topics in Information Processing; Foundations of Interpretable Artificial Intelligence Fall 2025