Anand D. Sarwate

I am a 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 07, 2025	Two new papers accepted: Max Vargas, Reilly Cannon, Andrew Engel, Anand D. Sarwate, Tony Chiang, Understanding generative AI output with embedding models, Science Advances, in press. Anand D. Sarwate, Flavio Calmon, Oliver Kosut, Lalitha Sankar, An information theorist’s tour of differential privacy, BITS The Information Theory Magazine, in press.
Jul 01, 2025	Headed on an epic trip for the next 4 weeks. I’ll post the slides as well. 2025-07-24 Seminar at CUHK on adversarial models for communication. 2025-07-24 Invited talk at AICIT2025 on AI models as measurement instruments 2025-07-24 Seminar at Southwest Jiaotong University (Chengdu) on tensor signal processing. 2025-08-04 Seminar at University of Sydney on adversarial models for communication. 2025-08-07 Seminar at Macquarie University on information theory and DP. 026-08-14 Seminar at Melbourne University on adversarial models for communication. 2026-08-15 Seminar at Monash University on AI models as measurement instruments.
Jul 01, 2025	I’m a full Professor now! Woohoo!
May 27, 2025	Spring 2026 updates! Congratulations to Dr. Ye Tao, who defended her thesis and graduated this May. She will be starting a job at LinkedIn shortly! I’m heading to Cambridge University for a sabbatical in the coming academic year and looking forward to it!
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.

recent(-ish) publications

[Preprint]

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

Balancing Timeliness and Privacy in Discrete-Time Updating Systems

[Other type of publication], Dec 2025

DOI Bib

        @techreport{SathyavageeswaeanSMY:25timely:private,
  author = {Sathyavageeswaran, Nitya and Sarwate, Anand D and Mandayam, Narayan B and Yates, Roy D},
  doi = {10.48550/arXiv.2512.13439},
  institution = {ArXiV},
  month = dec,
  number = {arXiv:2512.13439 [eess.SY]},
  title = {Balancing Timeliness and Privacy in Discrete-Time Updating Systems},
  year = {2025},
}

[Journal]

Sinjini Banerjee, Tim Marrinan, Reilly Cannon, Tony Chiang, and Anand D. Sarwate,

Measuring training variability from stochastic optimization using robust nonparametric testing

IEEE Journal of Selected Topics in Signal Processing 19 : (7), Oct 2025

DOI URL Bib

        @article{BanerjeeMCCS:25nonparam,
  author = {Banerjee, Sinjini and Marrinan, Tim and Cannon, Reilly and Chiang, Tony and Sarwate, Anand D.},
  doi = {10.1109/JSTSP.2025.3583140},
  journal = {IEEE Journal of Selected Topics in Signal Processing},
  month = oct,
  number = {7},
  pages = {1477--1492},
  title = {Measuring training variability from stochastic optimization using robust nonparametric testing},
  url = {https://doi.org/10.1109/JSTSP.2025.3583140},
  year = {2025},
}

[Journal]

Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut, and Lalitha Sankar,

An Information Theorist’s Tour of Differential Privacy

IEEE BITS the Information Theory Magazine 4 : (4), Oct 2025

DOI Bib

        @article{SarwateCKS:26bits,
  author = {Sarwate, Anand D. and Calmon, Flavio P. and Kosut, Oliver and Sankar, Lalitha},
  doi = {10.1109/MBITS.2025.3625155},
  journal = {IEEE BITS the Information Theory Magazine},
  month = oct,
  number = {4},
  pages = {2-15},
  title = {An Information Theorist's Tour of Differential Privacy},
  year = {2025},
}

[Conference]

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},
  month = apr,
  title = {Learning to Help in Multi-Class Settings},
  url = {https://openreview.net/forum?id=NCgTbt2j1F},
  year = {2025},
}

[Conference]

Ye Tao, and Anand D. Sarwate,

Differentially Private Distribution Estimation Using Functional Approximation

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)},
  month = apr,
  title = {Differentially Private Distribution Estimation Using Functional Approximation},
  year = {2025}
}

[Conference]

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)},
  doi = {10.1109/BigData62323.2024.10825936},
  month = dec,
  pages = {8623--8624},
  title = {Advanced machine learning in neuroimaging studies via federated learning},
  year = {2024},
}

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

Federated Privacy-Preserving Visualization: A Vision Paper
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)}, month = dec, pages = {8035--8041}, title = {Federated Privacy-Preserving Visualization: A Vision Paper}, year = {2024}, bdsk-url-2 = {https://doi.org/10.1101/2024.10.11.617267} }

[Journal]

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 21 : (3-4), 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},
  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},
  year = {2024},
}

[Conference]

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},
  doi = {10.1109/ITW61385.2024.10806971},
  month = nov,
  pages = {133--138},
  title = {Timely Offloading in Mobile Edge Cloud Systems},
  year = {2024},
}

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

Privacy-Preserving Visualization of Brain Functional Connectivity
[Other type of publication], Oct 2024
.

bioRxiv 2024.10.11.617267

Abs DOI 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.
@techreport{TaoSPPC:24visualization, author = {Tao, Ye and Sarwate, Anand D. and Panta, Sandeep and Plis, Sergey and Calhoun, Vince D.}, doi = {10.1101/2024.10.11.617267}, institution = {bioRxiv}, month = oct, note = {bioRxiv 2024.10.11.617267}, title = {Privacy-Preserving Visualization of Brain Functional Connectivity}, year = {2024}, bdsk-url-2 = {https://doi.org/10.1101/2024.10.11.617267} }

[Book Chapter]

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},
  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},
}

[Conference]

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)},
  doi = {10.1109/ISBI56570.2024.10635222},
  month = may,
  pages = {1--5},
  title = {Privacy-Preserving Visualization of Brain Functional Network Connectivity},
  year = {2024},
}

[Preprint]

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

Timely Offloading in Mobile Edge Cloud Systems

[Other type of publication], May 2024

URL Bib

        @techreport{SathyavageeswaranYSM:24offloading,
  author = {Sathyavageeswaran, Nitya and Yates, Roy D. and Sarwate, Anand D. and Mandayam, Narayan},
  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},
}

[Journal]

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.},
  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-2 = {https://doi.org/10.1016/j.patter.2024.100987}
}

[Conference]

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},
  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},
}

[Conference]

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)},
  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},
}

[Conference]

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

Low Separation Rank in Tensor Generalized Linear Models: an Asymptotic Analysis

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)},
  month = mar,
  title = {Low Separation Rank in Tensor Generalized Linear Models: an Asymptotic Analysis},
  year = {2024}
}

[Conference]

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},
  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},
}

[Journal]

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 1–2 : (1), Oct 2023

DOI Bib

        @article{CostanzaChockRHMS:23sarwate,
  author = {Costanza-Chock, Sasha and {Rose (editor)}, Kristin and Henne, Kate and Mhlambi, Sabelo and Sarwate, Anand},
  doi = {10.1215/2834703X-10734036},
  journal = {Critical AI},
  month = oct,
  number = {1},
  title = {Critical AI and Design Justice: An Interview with Sasha Costanza-Chock},
  year = {2023},
}

[Journal]

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.},
  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},
}

[Conference]

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)},
  month = jul,
  title = {Federated, Fast, and Private Visualization of Decentralized Data},
  url = {https://openreview.net/forum?id=XkfbFUqvek},
  year = {2023},
}

[Journal]
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 17 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.}, 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}, year = {2023}, bdsk-url-2 = {https://doi.org/10.3389/fninf.2023.1207721} }

[Conference]

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)},
  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},
}

[Preprint]

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

Minibatching Offers Improved Generalization Performance for Second Order Optimizers

[Other type of publication], 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},
  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},
}

[Journal]

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

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

Entropy 25 : (5), May 2023

DOI URL Bib

        @article{TasnimMSI:23capefm,
  author = {Tasnim, Naima and Mohammadi, Jafar and Sarwate, Anand D and Imtiaz, Hafiz},
  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},
  year = {2023},
}