Anand D. Sarwate
Associate Professor of ECE, Rutgers University, firstname.lastname@example.org
CoRE Building Rm. 517
Piscataway, NJ 08854-8058
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.
|May 28, 2023||Just returned from a really fabulous workshop on Information-Theoretic 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.|
|May 19, 2023||Finally mostly ported everything over to the new site…|
|Jul 1, 2022||New grant! RINGS: REALTIME: Resilient Edge-cloud Autonomous Learning with Timely Inferences, co-PIs Roy D. Yates, Waheed U. Bajwa, and Dipankar Raychaudhuri. We will be looking at issues in adaptive edge-cloud assisted ML-based services from the ground-up.|
- 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 linear-width neural networks,” ArXiV, arXiv:2211.06506 [cs.LG], Nov. 2022.
- H. Gazula, K. Rootes-Murdy, 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, 2022.
- 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 Binary-Erasure Channels,” in Proceedings of the 2023 IEEE International Symposium on Information Theory (ISIT), 2023, pp. to appear.