Anand D. Sarwate

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

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.

recent(-ish) publications

Yu Wu, Yansong Li, Zeyu Dong, Nitya Sathyavageeswaran, and Anand D. Sarwate,

Learning to Help in Multi-Class Settings,

In The Thirteenth International Conference on Learning Representations (ICLR 2025), Apr 2025.

URL Bib

@inproceedings{WuLDSS:25learning,
  author = {Wu, Yu and Li, Yansong and Dong, Zeyu and Sathyavageeswaran, Nitya and Sarwate, Anand D.},
  booktitle = {The Thirteenth International Conference on Learning Representations (ICLR 2025)},
  cvcategory = {archival},
  date-added = {2025-02-17 13:53:17 -0500},
  date-modified = {2025-05-23 15:10:08 +0100},
  month = apr,
  title = {Learning to Help in Multi-Class Settings},
  url = {https://openreview.net/forum?id=NCgTbt2j1F},
  year = {2025},
  bdsk-url-1 = {https://openreview.net/forum?id=NCgTbt2j1F}
}

Ye Tao, and Anand D. Sarwate,

In Proceedings of the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025), Apr 2025.

Bib

@inproceedings{TaoS:25cdf,
  author = {Tao, Ye and Sarwate, Anand D.},
  booktitle = {Proceedings of the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025)},
  date-added = {2025-02-17 13:51:03 -0500},
  date-modified = {2025-05-23 15:09:42 +0100},
  month = apr,
  title = {Differentially Private Distribution Estimation Using Functional Approximation},
  year = {2025}
}

Sunitha Basodi, Javier Romero, Sandeep Panta, Dylan Martin, Sergey Plis, Anand D. Sarwate, and Vince D. Calhoun,

Advanced machine learning in neuroimaging studies via federated learning,

In 2024 IEEE International Conference on Big Data (BigData), Dec 2024.

DOI Bib

@inproceedings{BasodiRPMPSC:24fl,
  author = {Basodi, Sunitha and Romero, Javier and Panta, Sandeep and Martin, Dylan and Plis, Sergey and Sarwate, Anand D. and Calhoun, Vince D.},
  booktitle = {2024 IEEE International Conference on Big Data (BigData)},
  date-added = {2025-02-17 13:41:44 -0500},
  date-modified = {2025-05-23 15:10:15 +0100},
  doi = {10.1109/BigData62323.2024.10825936},
  month = dec,
  pages = {8623--8624},
  title = {Advanced machine learning in neuroimaging studies via federated learning},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.1109/BigData62323.2024.10825936}
}

Ye Tao, Anand D. Sarwate, Sandeep Panta, Sergey Plis, and Vince D. Calhoun,

In 2024 IEEE International Conference on Big Data (BigData), Dec 2024.

Abs Bib

Privacy protection is important in visualization due to the risk of leaking personal sensitive information. In this paper, we study the problem of privacy-preserving visualizations using differential privacy, employing biomedical data from neuroimaging as a use case. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. To obtain a better privacy/visual utility tradeoff, we propose workflows for connectogram and seed-based connectivity visualizations, respectively. These workflows successfully generate visualizations similar to their non-private counterparts. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization.Competing Interest StatementThe authors have declared no competing interest.
@inproceedings{TaoSPPC:24vision, author = {Tao, Ye and Sarwate, Anand D. and Panta, Sandeep and Plis, Sergey and Calhoun, Vince D.}, booktitle = {2024 IEEE International Conference on Big Data (BigData)}, date-added = {2025-02-17 13:38:59 -0500}, date-modified = {2025-05-23 15:10:26 +0100}, month = dec, pages = {8035--8041}, title = {Federated Privacy-Preserving Visualization: A Vision Paper}, year = {2024}, bdsk-url-1 = {https://www.biorxiv.org/content/early/2024/10/15/2024.10.11.617267}, bdsk-url-2 = {https://doi.org/10.1101/2024.10.11.617267} }

Bikash Kumar Dey, Sidharth Jaggi, Michael Langberg, Anand D. Sarwate, and Yihan Zhang,

Codes for Adversaries: Between Worst-Case and Average-Case Jamming,

Foundations and Trends in Communications and Information Theory, Dec 2024.

DOI URL Bib

@article{DeyJLSZ:24monograph,
  author = {Dey, Bikash Kumar and Jaggi, Sidharth and Langberg, Michael and Sarwate, Anand D. and Zhang, Yihan},
  cvcategory = {books},
  date-added = {2024-12-04 08:57:39 -0500},
  date-modified = {2025-05-23 15:10:20 +0100},
  doi = {10.1561/0100000112},
  issn = {1567-2190},
  journal = {Foundations and Trends in Communications and Information Theory},
  month = dec,
  number = {3-4},
  pages = {300-588},
  title = {Codes for Adversaries: Between Worst-Case and Average-Case Jamming},
  url = {http://dx.doi.org/10.1561/0100000112},
  volume = {21},
  year = {2024},
  bdsk-url-1 = {http://dx.doi.org/10.1561/0100000112}
}

Nitya Sathyavageeswaran, Roy D Yates, Anand D Sarwate, and Narayan B. Mandayam,

Timely Offloading in Mobile Edge Cloud Systems,

In Proceedings of the 2024 IEEE Information Theory Workshop, Nov 2024.

DOI Bib

@inproceedings{Sathyavageeswaran:24itw,
  address = {Shenzhen, China},
  author = {Sathyavageeswaran, Nitya and Yates, Roy D and Sarwate, Anand D and Mandayam, Narayan B.},
  booktitle = {Proceedings of the 2024 IEEE Information Theory Workshop},
  date-added = {2024-08-17 12:04:33 -0400},
  date-modified = {2025-05-23 15:10:30 +0100},
  doi = {10.1109/ITW61385.2024.10806971},
  month = nov,
  pages = {133--138},
  title = {Timely Offloading in Mobile Edge Cloud Systems},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.1109/ITW61385.2024.10806971}
}

Ye Tao, Anand D. Sarwate, Sandeep Panta, Sergey Plis, and Vince D. Calhoun,

Privacy-Preserving Visualization of Brain Functional Connectivity,

Oct 2024.

bioRxiv 2024.10.11.617267

Abs DOI URL Bib

Privacy protection is important in visualization due to the risk of leaking personal sensitive information. In this paper, we study the problem of privacy-preserving visualizations using differential privacy, employing biomedical data from neuroimaging as a use case. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. To obtain a better privacy/visual utility tradeoff, we propose workflows for connectogram and seed-based connectivity visualizations, respectively. These workflows successfully generate visualizations similar to their non-private counterparts. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization.Competing Interest StatementThe authors have declared no competing interest.
@unpublished{TaoSPPC:24visualization, author = {Tao, Ye and Sarwate, Anand D. and Panta, Sandeep and Plis, Sergey and Calhoun, Vince D.}, date-added = {2025-02-17 13:37:37 -0500}, date-modified = {2025-05-23 15:10:46 +0100}, doi = {10.1101/2024.10.11.617267}, month = oct, note = {bioRxiv 2024.10.11.617267}, title = {Privacy-Preserving Visualization of Brain Functional Connectivity}, url = {https://www.biorxiv.org/content/early/2024/10/15/2024.10.11.617267}, year = {2024}, bdsk-url-1 = {https://www.biorxiv.org/content/early/2024/10/15/2024.10.11.617267}, bdsk-url-2 = {https://doi.org/10.1101/2024.10.11.617267} }

Anand D. Sarwate,

Machine learning with differential privacy,

In Handbook of Sharing Confidential Data Differential Privacy, Secure Multiparty Computation, and Synthetic Data, Oct 2024.

DOI Bib

@inbook{Sarwate:24crcbook,
  address = {Boca Raton, FL, USA},
  author = {Sarwate, Anand D.},
  booktitle = {Handbook of Sharing Confidential Data Differential Privacy, Secure Multiparty Computation, and Synthetic Data},
  chapter = {7},
  cvcategory = {books},
  date-added = {2024-01-28 14:01:57 -0500},
  date-modified = {2025-05-23 15:10:36 +0100},
  doi = {10.1201/9781003185284-9},
  editor = {Drechsler, J\"{o}rg and Kifer, Daniel and Reiter, Jerome and Slavkovic, Aleksandra},
  month = oct,
  pages = {91--114},
  publisher = {CRC Press},
  series = {Chapman \& Hall/CRC Handbooks of Modern Statistical Methods},
  title = {Machine learning with differential privacy},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.1201/9781003185284-9}
}

Max Vargas, Reilly Cannon, Andrew Engel, Anand D. Sarwate, and Tony Chiang,

Understanding Generative AI Content with Embedding Models,

Aug 2024.

DOI Bib

@techreport{VargasCESC:24arxiv,
  author = {Vargas, Max and Cannon, Reilly and Engel, Andrew and Sarwate, Anand D. and Chiang, Tony},
  date-added = {2024-08-22 10:34:35 -0400},
  date-modified = {2025-05-23 15:10:55 +0100},
  doi = {10.48550/arXiv.2408.10437},
  institution = {ArXiV},
  month = aug,
  number = {arXiv:2408.10437 [cs.LG]},
  title = {Understanding Generative AI Content with Embedding Models},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.48550/arXiv.2408.10437}
}

Ye Tao, Anand D. Sarwate, Sandeep Panta, Sergey Plis, and Vince D. Calhoun,

Privacy-Preserving Visualization of Brain Functional Network Connectivity,

In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), May 2024.

DOI Bib

@inproceedings{TaoSPPC:24fnc,
  author = {Tao, Ye and Sarwate, Anand D. and Panta, Sandeep and Plis, Sergey and Calhoun, Vince D.},
  booktitle = {2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  date-added = {2025-02-17 13:46:57 -0500},
  date-modified = {2025-05-23 15:08:01 +0100},
  doi = {10.1109/ISBI56570.2024.10635222},
  month = may,
  pages = {1--5},
  title = {Privacy-Preserving Visualization of Brain Functional Network Connectivity},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.1109/ISBI56570.2024.10635222}
}

Nitya Sathyavageeswaran, Roy D. Yates, Anand D. Sarwate, and Narayan Mandayam,

Timely Offloading in Mobile Edge Cloud Systems,

May 2024.

URL Bib

@techreport{SathyavageeswaranYSM:24offloading,
  author = {Sathyavageeswaran, Nitya and Yates, Roy D. and Sarwate, Anand D. and Mandayam, Narayan},
  date-added = {2024-06-21 13:40:39 -0400},
  date-modified = {2025-05-23 15:07:54 +0100},
  institution = {ArXiV},
  month = may,
  number = {arXiv:2405.07274 [eess.SY]},
  title = {Timely Offloading in Mobile Edge Cloud Systems},
  url = {https://arxiv.org/abs/2405.07274},
  year = {2024},
  bdsk-url-1 = {https://arxiv.org/abs/2405.07274}
}

Kelly Rootes-Murdy, Sandeep Panta, Ross Kelly, Javier Romero, Yann Quidé, Murray J. Cairns, Carmel Loughland, Vaughan J. Carr, and 41 more authors

Patterns, May 2024.

Abs DOI URL Bib Local

Summary Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.

@article{RootesMurdy+:24vbm,
  author = {Rootes-Murdy, Kelly and Panta, Sandeep and Kelly, Ross and Romero, Javier and Quid{\'e}, Yann and Cairns, Murray J. and Loughland, Carmel and Carr, Vaughan J. and Catts, Stanley V. and Jablensky, Assen and Green, Melissa J. and Henskens, Frans and Kiltschewskij, Dylan and Michie, Patricia T. and Mowry, Bryan and Pantelis, Christos and Rasser, Paul E. and Reay, William R. and Schall, Ulrich and Scott, Rodney J. and Watkeys, Oliver J. and Roberts, Gloria and Mitchell, Philip B. and Fullerton, Janice M. and Overs, Bronwyn J. and Kikuchi, Masataka and Hashimoto, Ryota and Matsumoto, Junya and Fukunaga, Masaki and Sachdev, Perminder S. and Brodaty, Henry and Wen, Wei and Jiang, Jiyang and Fani, Negar and Ely, Timothy D. and Lorio, Adriana and Stevens, Jennifer S. and Ressler, Kerry and Jovanovic, Tanja and {van Rooij}, Sanne J.H. and Federmann, Lydia M. and Jockwitz, Christiane and Teumer, Alexander and Forstner, Andreas J. and Caspers, Svenja and Cichon, Sven and Plis, Sergey M. and Sarwate, Anand D. and Calhoun, Vince D.},
  date-added = {2024-06-20 17:06:26 -0400},
  date-modified = {2025-05-23 15:12:35 +0100},
  doi = {10.1016/j.patter.2024.100987},
  journal = {Patterns},
  local-url = {papers/RootesMurdy+24vbm.pdf},
  month = may,
  pages = {100987},
  title = {Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC},
  url = {https://www.sciencedirect.com/science/article/pii/S2666389924001028},
  year = {2024},
  bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S2666389924001028},
  bdsk-url-2 = {https://doi.org/10.1016/j.patter.2024.100987}
}

A. W. Engel, Z. Wang, N. Frank, I. Dumitriu, S. Choudhury, A. Sarwate, and T. Chiang,

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models,

In The Twelfth International Conference on Learning Representations, May 2024.

URL Bib

@inproceedings{EngelWFDCSC:24explanations,
  address = {Vienna, Austria},
  author = {Engel, A. W. and Wang, Z. and Frank, N. and Dumitriu, I. and Choudhury, S. and Sarwate, A. and Chiang, T.},
  booktitle = {The Twelfth International Conference on Learning Representations},
  cvcategory = {archival},
  date-added = {2024-01-28 14:11:52 -0500},
  date-modified = {2024-06-21 13:49:12 -0400},
  month = may,
  title = {Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models},
  url = {https://openreview.net/forum?id=yKksu38BpM},
  year = {2024},
  bdsk-url-1 = {https://openreview.net/forum?id=yKksu38BpM}
}

Jose Hoyos Sanchez, Batoul Taki, Waheed Bajwa, and Anand Sarwate,

Federated Learning of Tensor Generalized Linear Models with Low Separation Rank,

In Proceedings of the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Apr 2024.

DOI Bib

@inproceedings{HoyosSanchezTBS:24icassp,
  author = {Sanchez, Jose Hoyos and Taki, Batoul and Bajwa, Waheed and Sarwate, Anand},
  booktitle = {Proceedings of the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)},
  date-added = {2023-11-26 06:39:28 -0500},
  date-modified = {2025-05-23 15:07:50 +0100},
  doi = {10.1109/ICASSP48485.2024.10446033},
  month = apr,
  pages = {2136--2140},
  title = {Federated Learning of Tensor Generalized Linear Models with Low Separation Rank},
  year = {2024},
  bdsk-url-1 = {https://doi.org/10.1109/ICASSP48485.2024.10446033}
}

Batoul Taki, Anand D. Sarwate, and Waheed U. Bajwa,

In Proceedings of the 2024 Annual Conference on Information Science and Systems (CISS), Mar 2024.

Bib

@inproceedings{TakiSB:24asymptotics,
  address = {Princeton, NJ, USA},
  author = {Taki, Batoul and Sarwate, Anand D. and Bajwa, Waheed U.},
  booktitle = {Proceedings of the 2024 Annual Conference on Information Science and Systems (CISS)},
  date-added = {2024-01-28 14:16:16 -0500},
  date-modified = {2024-01-28 14:19:36 -0500},
  month = mar,
  title = {Low Separation Rank in Tensor Generalized Linear Models: an Asymptotic Analysis},
  year = {2024}
}

Zhichao Wang, Andrew Engel, Anand Sarwate, Ioana Dumitriu, and Tony Chiang,

Spectral Evolution and Invariance in Linear-width Neural Networks,

In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), Dec 2023.

URL Bib

@incollection{WangESDC:23neurips,
  author = {Wang, Zhichao and Engel, Andrew and Sarwate, Anand and Dumitriu, Ioana and Chiang, Tony},
  booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS 2023)},
  cvcategory = {archival},
  date-added = {2023-09-22 12:16:24 -0400},
  date-modified = {2024-06-21 13:55:22 -0400},
  month = dec,
  publisher = {Curran Associates, Inc.},
  title = {Spectral Evolution and Invariance in Linear-width Neural Networks},
  url = {https://openreview.net/forum?id=gpqBGyKeKH},
  year = {2023},
  bdsk-url-1 = {https://drive.google.com/file/d/1dzxXlayANDzFOFSC6jOgwvOZ-Iweo44C/view}
}

Sasha Costanza-Chock, Kristin Rose (editor), Kate Henne, Sabelo Mhlambi, and Anand Sarwate,

Critical AI and Design Justice: An Interview with Sasha Costanza-Chock,

Critical AI, Oct 2023.

DOI Bib

@article{CostanzaChockRHMS:23sarwate,
  author = {Costanza-Chock, Sasha and {Rose (editor)}, Kristin and Henne, Kate and Mhlambi, Sabelo and Sarwate, Anand},
  date-added = {2023-09-22 11:40:32 -0400},
  date-modified = {2025-05-23 15:07:35 +0100},
  doi = {10.1215/2834703X-10734036},
  journal = {Critical AI},
  month = oct,
  number = {1},
  title = {Critical AI and Design Justice: An Interview with Sasha Costanza-Chock},
  volume = {1--2},
  year = {2023},
  bdsk-url-1 = {https://doi.org/10.1215/2834703X-10734036}
}

Batoul Taki, Anand D. Sarwate, and Waheed U. Bajwa,

Structured Low-Rank Tensor Models for Logistic Regression,

Transactions on Machine Learning Research, Aug 2023.

URL Bib

@article{TakiGSB:23LSRtensorLR,
  author = {Taki, Batoul and Sarwate, Anand D. and Bajwa, Waheed U.},
  date-added = {2022-02-09 12:38:04 -0500},
  date-modified = {2025-05-23 15:07:24 +0100},
  journal = {Transactions on Machine Learning Research},
  month = aug,
  title = {Structured Low-Rank Tensor Models for Logistic Regression},
  url = {https://openreview.net/forum?id=qUxBs3Ln41},
  year = {2023},
  bdsk-url-1 = {https://doi.org/10.1109/IEEECONF53345.2021.9723149}
}

Debbrata Kumar Saha, Vince Calhoun, Soo Min Kwon, Anand Sarwate, Rekha Saha, and Sergey Plis,

Federated, Fast, and Private Visualization of Decentralized Data,

In Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities (FL-ICML 2023), Jul 2023.

URL Bib

@inproceedings{SahaCKSSP:23dnse:workshop,
  author = {Saha, Debbrata Kumar and Calhoun, Vince and Kwon, Soo Min and Sarwate, Anand and Saha, Rekha and Plis, Sergey},
  booktitle = {Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities (FL-ICML 2023)},
  date-added = {2023-11-24 17:11:41 -0500},
  date-modified = {2024-01-28 14:19:28 -0500},
  month = jul,
  title = {Federated, Fast, and Private Visualization of Decentralized Data},
  url = {https://openreview.net/forum?id=XkfbFUqvek},
  year = {2023},
  bdsk-url-1 = {https://openreview.net/forum?id=XkfbFUqvek}
}

Dylan Martin, Sunitha Basodi, Sandeep Panta, Kelly Rootes-Murdy, Paul Prae, Anand D. Sarwate, Ross Kelly, Javier Romero, and 8 more authors

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility,

Frontiers in Neuroinformatics, Jun 2023.

Abs DOI URL Bib

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC’s federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.
@article{Martin:23vaults, author = {Martin, Dylan and Basodi, Sunitha and Panta, Sandeep and Rootes-Murdy, Kelly and Prae, Paul and Sarwate, Anand D. and Kelly, Ross and Romero, Javier and Baker, Bradley T. and Gazula, Harshvardhan and Bockholt, Jeremy and Turner, Jessica A. and Esper, Nathalia B. and Franco, Alexandre R. and Plis, Sergey and Calhoun, Vince D.}, date-added = {2023-11-24 17:18:14 -0500}, date-modified = {2025-05-23 15:06:59 +0100}, doi = {10.3389/fninf.2023.1207721}, journal = {Frontiers in Neuroinformatics}, month = jun, title = {Enhancing collaborative neuroimaging research: introducing {COINSTAC Vaults} for federated analysis and reproducibility}, url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.1207721}, volume = {17}, year = {2023}, bdsk-url-1 = {https://www.frontiersin.org/articles/10.3389/fninf.2023.1207721}, bdsk-url-2 = {https://doi.org/10.3389/fninf.2023.1207721} }

Sijie Li, Prasad Krishan, Sidharth Jaggi, Michael Langberg, and Anand D Sarwate,

Computationally Efficient Codes for Adversarial Binary-Erasure Channels,

In Proceedings of the 2023 IEEE International Symposium on Information Theory (ISIT), Jun 2023.

DOI URL Bib

@inproceedings{LKJLS:23efficient,
  author = {Li, Sijie and Krishan, Prasad and Jaggi, Sidharth and Langberg, Michael and Sarwate, Anand D},
  booktitle = {Proceedings of the 2023 IEEE International Symposium on Information Theory (ISIT)},
  date-added = {2020-06-20 10:20:00 -0400},
  date-modified = {2023-11-26 14:38:14 -0500},
  doi = {10.1109/ISIT54713.2023.10206731},
  month = jun,
  pages = {228--233},
  title = {Computationally Efficient Codes for Adversarial Binary-Erasure Channels},
  url = {https://doi.org/10.1109/ISIT54713.2023.10206731},
  year = {2023},
  bdsk-url-1 = {https://dx.doi.org/10.1109/ISIT44484.2020.9174487}
}

Eric Silk, Swarnita Chakraborty, Nairanjana Dasgupta, Anand D. Sarwate, Andrew Lumsdaine, and Tony Chiang,

Minibatching Offers Improved Generalization Performance for Second Order Optimizers,

May 2023.

DOI URL Bib

@techreport{SilkCDSLC:23minibatching,
  author = {Silk, Eric and Chakraborty, Swarnita and Dasgupta, Nairanjana and Sarwate, Anand D. and Lumsdaine, Andrew and Chiang, Tony},
  date-added = {2024-07-08 05:00:23 -0400},
  date-modified = {2025-05-23 15:06:47 +0100},
  doi = {10.48550/arXiv.2307.11684},
  institution = {ArXiV},
  month = may,
  number = {arXiv:2307.11684 [cs.LG]},
  title = {Minibatching Offers Improved Generalization Performance for Second Order Optimizers},
  url = {https://arxiv.org/abs/2307.11684},
  year = {2023},
  bdsk-url-1 = {https://arxiv.org/abs/2307.11684}
}

Naima Tasnim, Jafar Mohammadi, Anand D Sarwate, and Hafiz Imtiaz,

Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning,

Entropy, May 2023.

DOI URL Bib

@article{TasnimMSI:23capefm,
  author = {Tasnim, Naima and Mohammadi, Jafar and Sarwate, Anand D and Imtiaz, Hafiz},
  date-added = {2023-05-09 13:58:04 -0400},
  date-modified = {2025-05-23 15:06:54 +0100},
  doi = {10.3390/e25050825},
  journal = {Entropy},
  month = may,
  number = {5},
  pages = {825},
  title = {Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning},
  url = {https://doi.org/10.3390/e25050825},
  volume = {25},
  year = {2023},
  bdsk-url-1 = {https://doi.org/10.3390/e25050825}
}