Jayadev Acharya

Jayadev Acharya is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University, with graduate field memberships in Computer Science and Operations Research and Information Engineering. His research focuses on the intersection of information theory, statistical inference, algorithms, and machine learning. He explores trade-offs between data, memory, time, and robustness in learning problems, including quantum information and machine unlearning.

Education:

• B.Tech in Electronics and Communication Engineering from Indian Institute of Technology, Kharagpur (2007)
• M.S. in Electrical and Computer Engineering from University of California, San Diego (2009)
• Ph.D. in Electrical and Computer Engineering from University of California, San Diego (2014)

Research Trends: His recent work (2020-2022) emphasizes information-constrained inference, differential privacy, quantum entropy estimation, and distributed learning. Key subfields include communication complexity, local privacy, and adaptive gradient processing. His publications span NeurIPS, ICML, COLT, and IEEE Transactions on Information Theory.

Awards:

• Kenneth A. Goldman ’71 Excellence in Teaching Award (Cornell, 2022)
• MIT Energy Initiative Fellowship (2014)
• Shannon Graduate Fellowship (UCSD, 2012)
• Jack Keil Wolf Student Paper Award (ISIT, 2010)

Advising and Grants: He advises Sourabh Bhadane, Saravanan Kandasamy, Yuhan Liu, Ziteng Sun, and Huanyu Zhang. Research funded by NSF-CAREER, NSF-CRII, NSF-CIF small grants, and Google Faculty Research Award.