Subhro Das

778 total citations
39 papers, 451 citations indexed

About

Subhro Das is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Subhro Das has authored 39 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 9 papers in Computer Networks and Communications and 8 papers in Electrical and Electronic Engineering. Recurrent topics in Subhro Das's work include Target Tracking and Data Fusion in Sensor Networks (7 papers), Distributed Control Multi-Agent Systems (7 papers) and Electric Motor Design and Analysis (6 papers). Subhro Das is often cited by papers focused on Target Tracking and Data Fusion in Sensor Networks (7 papers), Distributed Control Multi-Agent Systems (7 papers) and Electric Motor Design and Analysis (6 papers). Subhro Das collaborates with scholars based in United States, India and Hong Kong. Subhro Das's co-authors include José M. F. Moura, Mark G. Allen, David P. Arnold, Jeffrey H. Lang, I. Zana, Jung-Wook Park, Yingying Li, Na Li, Gregory W. Wornell and Prasanna Sattigeri and has published in prestigious journals such as IEEE Transactions on Signal Processing, Journal of Microelectromechanical Systems and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

Subhro Das

34 papers receiving 437 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Subhro Das United States	10	212	212	134	92	72	39	451
Ramazan Çoban Türkiye	14	59 0.3×	135 0.6×	98 0.7×	325 3.5×	65 0.9×	42	524
Chyun‐Chau Fuh Taiwan	12	170 0.8×	88 0.4×	48 0.4×	277 3.0×	90 1.3×	30	589
Ker‐Wei Yu Taiwan	12	268 1.3×	90 0.4×	54 0.4×	417 4.5×	93 1.3×	35	644
Mohammad Tabatabaei Iran	12	75 0.4×	61 0.3×	63 0.5×	137 1.5×	96 1.3×	33	411
Éric Busvelle France	11	61 0.3×	67 0.3×	80 0.6×	557 6.1×	30 0.4×	24	684
Ruiliang Zhang China	8	73 0.3×	89 0.4×	141 1.1×	52 0.6×	41 0.6×	29	325
Gregory J. Toussaint United States	8	160 0.8×	49 0.2×	108 0.8×	232 2.5×	26 0.4×	17	497
Guomin Zhang China	10	201 0.9×	67 0.3×	58 0.4×	27 0.3×	28 0.4×	49	354
Travis S. Taylor United States	6	91 0.4×	74 0.3×	215 1.6×	25 0.3×	12 0.2×	20	331
Kamal Ghoumid Morocco	13	265 1.3×	44 0.2×	341 2.5×	18 0.2×	26 0.4×	65	558

Countries citing papers authored by Subhro Das

Since Specialization

Citations

This map shows the geographic impact of Subhro Das's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Subhro Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subhro Das more than expected).

Fields of papers citing papers by Subhro Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhro Das. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Subhro Das. The network helps show where Subhro Das may publish in the future.

Co-authorship network of co-authors of Subhro Das

This figure shows the co-authorship network connecting the top 25 collaborators of Subhro Das. A scholar is included among the top collaborators of Subhro Das based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Subhro Das. Subhro Das is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhang, Wang, Subhro Das, Lam M. Nguyen, & Luca Daniel. (2025). Dilated Convolution for Time Series Learning. 1–5. 1 indexed citations

Reisizadeh, Amirhossein, Haochuan Li, Subhro Das, & Ali Jadbabaie. (2025). Variance-reduced Clipping for Non-convex Optimization. 1–5. 1 indexed citations

Thompson, Neil, et al.. (2024). A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(21). 23012–23018. 2 indexed citations

Wang, Zhang, Subhro Das, Tsui-Wei Weng, et al.. (2024). One Step Closer to Unbiased Aleatoric Uncertainty Estimation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(15). 16857–16864. 5 indexed citations

Li, Yingying, Tianpeng Zhang, Subhro Das, Jeff S. Shamma, & Na Li. (2023). Non-asymptotic System Identification for Linear Systems with Nonlinear Policies. IFAC-PapersOnLine. 56(2). 1672–1679. 1 indexed citations

Ghosh, Soumya K., et al.. (2023). Reliable Gradient-free and Likelihood-free Prompt Tuning. 2416–2429. 6 indexed citations

Sattigeri, Prasanna, et al.. (2023). Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9772–9781. 13 indexed citations

Nguyen, Lam M., et al.. (2023). Attacking c-MARL More Effectively: A Data Driven Approach. abs/1902.04043. 1271–1276. 1 indexed citations

Farnia, Farzan, et al.. (2023). GAT–GMM: Generative Adversarial Training for Gaussian Mixture Models. SIAM Journal on Mathematics of Data Science. 5(1). 122–146. 3 indexed citations

10.

Das, Subhro, et al.. (2022). Learning skills adjacency representations for optimized reskilling recommendations. 2022 IEEE International Conference on Big Data (Big Data). 2253–2258.

11.

Zhang, Jingzhao, Hongzhou Lin, Subhro Das, Suvrit Sra, & Ali Jadbabaie. (2021). Stochastic Optimization with Non-stationary Noise: The Power of Moment Estimation. 1 indexed citations

12.

Das, Subhro, et al.. (2021). Alternative Implementations of Square-root Cubature Kalman Filter for Sensorless PMSM Drives with Improved Dynamic Performance. IET conference proceedings.. 2020(7). 401–406.

13.

Rosenthal, Sara, Subhro Das, Pei-Yun Hsueh, Ken Barker, & Ching-Hua Chen. (2020). Efficient goal attainment and engagement in a care manager system using unstructured notes. JAMIA Open. 3(1). 62–69.

14.

Hsueh, Pei-Yun & Subhro Das. (2017). Interpretable Clustering for Prototypical Patient Understanding: A Case Study of Hypertension and Depression Subgroup Behavioral Profiling in National Health and Nutrition Examination Survey Data.. AMIA. 1 indexed citations

15.

Hsueh, Pei-Yun, Sanjoy Kumer Dey, Subhro Das, & Thomas C. Wetter. (2017). Making Sense of Patient-Generated Health Data for Interpretable Patient-Centered Care: The Transition from "More" to "Better".. PubMed. 245. 113–117. 6 indexed citations

16.

Das, Subhro. (2016). Distributed Linear Filtering and Prediction of Time-varying Random Fields. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University). 1 indexed citations

17.

Das, Subhro & José M. F. Moura. (2013). Distributed Kalman filtering and Network Tracking Capacity. 92. 629–633. 4 indexed citations

18.

Arnold, David P., Subhro Das, Jung-Wook Park, et al.. (2006). Microfabricated High-Speed Axial-Flux Multiwatt Permanent-Magnet Generators—Part II: Design, Fabrication, and Testing. Journal of Microelectromechanical Systems. 15(5). 1351–1363. 37 indexed citations

19.

Das, Subhro, et al.. (2005). An Optimum UPQC with Minimum VA Requirement and Mitigation of Unbalanced Voltage Sag. International Journal of Emerging Electric Power Systems. 2(2). 8 indexed citations

20.

Arnold, David P., I. Zana, Florian Herrault, et al.. (2005). Optimization of a Microscale, Axial-Flux, Permanent-Magnet Generator. Defense Technical Information Center (DTIC). 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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