Anand D. Sarwate
Associate Professor of ECE, Rutgers University, anand.sarwate@rutgers.edu
CoRE Building Rm. 517
Busch Campus
Rutgers University
Piscataway, NJ 088548058
I am an Associate Professor in the Department of Electrical and Computer Engineering
at Rutgers, The State University of New Jersey.
I also am a member of the graduate faculty in the Department of Computer Science
and the Department of Statistics.
I like to work on problems that involve probability, mathematical statistics,
and optimization, with applications in information theory, communication,
signal processing, and machine learning. I’m particularly interested in
how these things intersect in the context of distributed/decentralized
systems with constraints like privacy, bandwidth, latency, power, and so on.
This page is still somewhat under construction. I anticipate that will be the state for quite a while.
news
Nov 22, 2023  I received the Outstanding Engineering Professor Award from the Rutgers School of Engineering! 

Nov 22, 2023  I have been appointed as a Distinguished Lecturer for 20242025 by the IEEE Information Theory Society. I’m happy to visit to give a talk! 
Sep 22, 2023 
Some recent activity:

Jun 20, 2023 
A few new papers to appear:

May 28, 2023  Just returned from a really fabulous workshop on InformationTheoretic Methods for Trustworthy Machine Learning at the Simons Institute for the Theory of Computing at UC Berkeley. It was weird to wander around a place I lived 20 years ago but really nice to see old friends and meet new people. 
selected publications
 R. Islam, K. N. Keya, S. Pan, A. D. Sarwate, and J. R. Foulds, “Differential Fairness: An Intersectional Framework for Fair AI,” Entropy, vol. 25, no. 4, Apr. 2023.
 Z. Wang, A. Engel, A. Sarwate, I. Dumitriu, and T. Chiang, “Spectral evolution and invariance in linearwidth neural networks,” ArXiV, arXiv:2211.06506 [cs.LG], Nov. 2022.
 H. Gazula, K. RootesMurdy, B. Holla, S. Basodi, Z. Zhang, E. Verner, R. Kelly, P. Murthy, A. Chakrabarti, D. Basu, S. Bhagyalakshmi Nanjayya, R. Lenin Singh, R. Lourembam Singh, K. Kalyanram, K. Kartik, K. Kalyanaraman, K. Ghattu, R. Kuriyan, S. S. Kurpad, G. J. Barker, R. D. Bharath, S. Desrivieres, M. Purushottam, D. P. Orfanos, E. Sharma, M. Hickman, M. Toledano, N. Vaidya, T. Banaschewski, A. L. W. Bokde, H. Flor, A. Grigis, H. Garavan, P. Gowland, A. Heinz, R. Brühl, J.L. Martinot, M.L. Paillére Martinot, E. Artiges, F. Nees, T. Paus, L. Poustka, J. H. Fröhner, L. Robinson, M. N. Smolka, H. Walter, J. Winterer, R. Whelan, J. A. Turner, A. D. Sarwate, et al., “Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains,” Neuroinformatics, vol. 21, pp. 287–301, Apr. 2023.
 Y. Zhang, S. Jaggi, M. Langberg, and A. D. Sarwate, “The Capacity of Causal Adversarial Channels,” in Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT), 2022.
 N. Sathyavageeswaran, R. D. Yates, A. D. Sarwate, and N. Mandayam, “Privacy Leakage in Discrete Time Updating Systems,” in Proceedings of the 2022 IEEE International Symposium on Information Theory (ISIT), 2022.
 Y. Zhang, S. Vatedka, S. Jaggi, and A. D. Sarwate, “Quadratically Constrained Myopic Adversarial Channels,” IEEE Transactions on Information Theory, vol. 68, pp. 4901–4948, Aug. 2022.
 S. Li, P. Krishan, S. Jaggi, M. Langberg, and A. D. Sarwate, “Computationally Efficient Codes for Adversarial BinaryErasure Channels,” in Proceedings of the 2023 IEEE International Symposium on Information Theory (ISIT), 2023, pp. 228–233.