Thinh T. Doan

745 total citations
40 papers, 263 citations indexed

About

Thinh T. Doan is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computational Mechanics. According to data from OpenAlex, Thinh T. Doan has authored 40 papers receiving a total of 263 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 21 papers in Computer Networks and Communications and 7 papers in Computational Mechanics. Recurrent topics in Thinh T. Doan's work include Stochastic Gradient Optimization Techniques (18 papers), Distributed Control Multi-Agent Systems (17 papers) and Sparse and Compressive Sensing Techniques (7 papers). Thinh T. Doan is often cited by papers focused on Stochastic Gradient Optimization Techniques (18 papers), Distributed Control Multi-Agent Systems (17 papers) and Sparse and Compressive Sensing Techniques (7 papers). Thinh T. Doan collaborates with scholars based in United States, Switzerland and Japan. Thinh T. Doan's co-authors include Justin Romberg, Siva Theja Maguluri, R. Srikant, Carolyn L. Beck, Yifei Song, Hao-Hsuan Chang, Lingjia Liu, Nitin H. Vaidya, John-Paul Clarke and Nirupam Gupta and has published in prestigious journals such as IEEE Transactions on Automatic Control, Automatica and IEEE Transactions on Wireless Communications.

In The Last Decade

Thinh T. Doan

34 papers receiving 256 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thinh T. Doan United States	10	147	114	56	40	36	40	263
Van Sy United States	7	216 1.5×	76 0.7×	37 0.7×	17 0.4×	35 1.0×	23	296
Franck Iutzeler France	8	180 1.2×	71 0.6×	54 1.0×	67 1.7×	74 2.1×	23	300
Sergio Valcárcel Macua Spain	8	101 0.7×	74 0.6×	42 0.8×	18 0.5×	18 0.5×	17	194
Olivier Devolder Belgium	5	39 0.3×	148 1.3×	55 1.0×	41 1.0×	143 4.0×	5	292
Salar Fattahi United States	9	33 0.2×	37 0.3×	76 1.4×	31 0.8×	33 0.9×	26	208
Chenguang Xi United States	8	380 2.6×	142 1.2×	76 1.4×	32 0.8×	87 2.4×	15	448
Maher Nouiehed United States	6	36 0.2×	99 0.9×	27 0.5×	19 0.5×	28 0.8×	10	180
Bryan Van Scoy United States	7	228 1.6×	89 0.8×	79 1.4×	27 0.7×	37 1.0×	19	359
Kunal Srivastava United States	8	259 1.8×	94 0.8×	34 0.6×	12 0.3×	72 2.0×	9	328
Sean Lawlor Canada	5	306 2.1×	124 1.1×	44 0.8×	13 0.3×	90 2.5×	7	358

Countries citing papers authored by Thinh T. Doan

Since Specialization

Citations

This map shows the geographic impact of Thinh T. Doan'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 Thinh T. Doan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thinh T. Doan more than expected).

Fields of papers citing papers by Thinh T. Doan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thinh T. Doan. 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 Thinh T. Doan. The network helps show where Thinh T. Doan may publish in the future.

Co-authorship network of co-authors of Thinh T. Doan

This figure shows the co-authorship network connecting the top 25 collaborators of Thinh T. Doan. A scholar is included among the top collaborators of Thinh T. Doan 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 Thinh T. Doan. Thinh T. Doan 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

Bose, Subhonmesh, et al.. (2024). Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization. Journal of Optimization Theory and Applications. 203(2). 1991–2024.

Doan, Thinh T., et al.. (2024). Convergence Rates of Gradient Descent-Ascent Dynamics Under Delays in Solving Nonconvex Min-Max Optimization. 2748–2753.

Chang, Hao-Hsuan, Yifei Song, Thinh T. Doan, & Lingjia Liu. (2023). Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access. IEEE Transactions on Wireless Communications. 22(8). 5337–5348. 20 indexed citations

Doan, Thinh T.. (2023). Finite-time convergence rates of distributed local stochastic approximation. Automatica. 158. 111294–111294.

Doan, Thinh T., et al.. (2022). Finite-Time Convergence Rates of Decentralized Stochastic Approximation With Applications in Multi-Agent and Multi-Task Learning. IEEE Transactions on Automatic Control. 68(5). 2758–2773. 4 indexed citations

Doan, Thinh T., et al.. (2022). Finite-Sample Analysis of Two-Time-Scale Natural Actor–Critic Algorithm. IEEE Transactions on Automatic Control. 68(6). 3273–3284. 13 indexed citations

Doan, Thinh T., et al.. (2022). Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes. 2022 IEEE 61st Conference on Decision and Control (CDC). 4028–4033. 2 indexed citations

Doan, Thinh T., et al.. (2022). Convergence Rates of Asynchronous Policy Iteration for Zero-Sum Markov Games under Stochastic and Optimistic Settings. 2022 IEEE 61st Conference on Decision and Control (CDC). 3493–3498. 2 indexed citations

Doan, Thinh T., et al.. (2021). Distributed two-time-scale methods over clustered networks. arXiv (Cornell University). 4625–4630. 8 indexed citations

10.

Doan, Thinh T., Siva Theja Maguluri, & Justin Romberg. (2020). Convergence Rates of Distributed Gradient Methods Under Random Quantization: A Stochastic Approximation Approach. IEEE Transactions on Automatic Control. 66(10). 4469–4484. 45 indexed citations

11.

Doan, Thinh T. & Justin Romberg. (2020). Finite-Time Performance of Distributed Two-Time-Scale Stochastic Approximation. 26–36. 1 indexed citations

12.

Zhang, Sheng, et al.. (2019). Performance of Q-learning with Linear Function Approximation: Stability and Finite-Time Analysis. arXiv (Cornell University). 8 indexed citations

13.

Doan, Thinh T., Siva Theja Maguluri, & Justin Romberg. (2019). Convergence Rates of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning. arXiv (Cornell University). 2 indexed citations

14.

Zhang, Sheng, et al.. (2019). Finite-Time Analysis of Q-Learning with Linear Function Approximation.. arXiv (Cornell University). 3 indexed citations

15.

Doan, Thinh T. & Justin Romberg. (2019). Linear Two-Time-Scale Stochastic Approximation A Finite-Time Analysis. 399–406. 3 indexed citations

16.

Doan, Thinh T., Siva Theja Maguluri, & Justin Romberg. (2018). Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization. arXiv (Cornell University). 2 indexed citations

17.

Doan, Thinh T., Siva Theja Maguluri, & Justin Romberg. (2018). On the Convergence of Distributed Subgradient Methods under Quantization. 567–574. 3 indexed citations

18.

Doan, Thinh T., Carolyn L. Beck, & R. Srikant. (2017). Impact of Communication Delays on the Convergence Rate of Distributed Optimization Algorithms. arXiv (Cornell University). 2 indexed citations

19.

Doan, Thinh T. & Choon Yik Tang. (2012). Continuous-time constrained distributed convex optimization. 1482–1489. 5 indexed citations

20.

Schilling, M., et al.. (1998). Sequential probabilistic methods for power system operation and planning. RWTH Publications (RWTH Aachen). 179(13). 16 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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