Anand D. Sarwate

Associate Professor of ECE, Rutgers University,


CoRE Building Rm. 517

Busch Campus

Rutgers University

Piscataway, NJ 08854-8058

(848) 445-8516

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.


Jan 19, 2024 To appear at ICLR 2024 (spotlight): A. W. Engel, Z. Wang, N. Frank, I. Dumitriu, S. Choudhury, A. Sarwate, T. Chiang, Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models.

To appear at ICASSP 2024: J. Hoyos Sanchez, B. Taki,, W. U. Bajwa, A. D. Sarwate, Federated Learning of Tensor Generalized Linear Models with Low Separation Rank.

I’m teaching ECE 549 this semester, which was traditionally called Detection and Estimation Theory.
Nov 27, 2023 I have been appointed as a Distinguished Lecturer for 2024-2025 by the IEEE Information Theory Society. I’m happy to visit to give a talk!
Nov 22, 2023 I received the Outstanding Engineering Professor Award from the Rutgers School of Engineering!
Sep 22, 2023 Some recent activity:
Jun 20, 2023 A few new papers to appear:
  • D. Martin et al. “Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility”, to Frontiers in Neuroinformatics.
  • Z. Wang et al., “Spectral Evolution and Invariance in Linear-width Neural Networks”, to the ICML 2023 High-dimensional Learning Dynamics Workshop.
  • D. Saha et al, “Federated, Fast, and Private Visualization of Decentralized Data”, to the ICML 2023 Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities.

selected publications

  1. 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. 228–233.
  2. 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.
  3. 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, Apr. 2023.
  4. Z. Wang, A. Engel, A. Sarwate, I. Dumitriu, and T. Chiang, “Spectral evolution and invariance in linear-width neural networks,” no. arXiv:2211.06506 [cs.LG], ArXiV, Nov-2022.
  5. 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.
  6. 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.
  7. 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.