news | Anand D. Sarwate

May 23, 2025	A bunch of updates after not so many updates: The site has been updated with more content, including slides from some recent talks. I was awarded the Presidential Outstanding Faculty Scholar Award by Rutgers. This is given to faculty getting promoted to Professor (from Associate). However, I have not received official notification for the promotion so I’ll just assume the award is a good sign (?) Our monograph is finally done! Dey et al, Codes for Adversaries: Between Worst-Case and Average-Case Jamming, Foundations and Trends in Communications and Information Theory, 2024. This is is the culmination of 15+ years of research we’ve been doing on adversarial channel models! Preprints! Vargas et al., Understanding Generative AI Content with Embedding Models, under review. Tao et al., Privacy-Preserving Visualization of Brain Functional Connectivity, about to be submitted. Banerjee et al., Measuring model variability using robust non-parametric testing, revision under review. Journal and conference papers! Tao and Sarwate, Differentially Private Distribution Estimation Using Functional Approximation, ICASSP 2025. We look at CDF approximation using functional approximation. A journal version is in the works. Wu et al., Learning to Help in Multi-Class Settings, ICLR 2025. We study a variation on learning with abstention to design effective “helpers” for resource-constrained devices needing to do ML/AI stuff. Tao et al., Federated Privacy-Preserving Visualization: A Vision Paper, IEEE BigData 2024. We’re looking at where and when private visualization might be useful. Sathyavageeswaran et al., Timely Offloading in Mobile Edge Cloud Systems, ITW 2024. We use an MDP framework to analyze computational offloading policies. Dey, et al., Computationally Efficient Codes for Strongly Dobrushin-Stambler Nonsymmetrizable Oblivious AVCs, ISIT 2024. We develop polynomial time codes for classes of adversarial channels! A. D. Sarwate, Machine learning with differential privacy, a chapter in the Handbook of Sharing Confidential Data Differential Privacy, Secure Multiparty Computation, and Synthetic Data from CRC. A limited survey of “classical” ML under differential privacy, more aimed at potential practitioners. Rootes-Murdy et al., Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC, Patterns, 2024. A use-case for COINSTAC, the federated learning platform for neuroimaging analysis on which I have been collaborating for the last 10 years.
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: Critical AI and Design Justice: An Interview with Sasha Costanza-Chock, to appear in Critical AI. This is the transcript of a panel interview with Sasha Costanza-Chock, author of Design Justice. Zhichao Wang, Andrew Engel, Anand Sarwate, Ioana Dumitriu, Tony Chiang, Spectral Evolution and Invariance in Linear-width Neural Networks, accepted to NeurIPS 2023.
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.
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 26, 2022	Some new work: Taki, Sarwate, Bajwa, “Structured Low-Rank Tensors for Generalized Linear Models”, accepted to Transactions on Machine Learning Research (TMLR). Silk, Chakraborty, Dasgupta, Sarwate, Lumsdaine, Chang, “Minibatching Offers Improved Generalization Performance for Second Order Optimizers” (ArXiV).
Jul 01, 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.