Hyeong Soo Chang

1.2k total citations
57 papers, 770 citations indexed

About

Hyeong Soo Chang is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Networks and Communications. According to data from OpenAlex, Hyeong Soo Chang has authored 57 papers receiving a total of 770 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Artificial Intelligence, 26 papers in Management Science and Operations Research and 16 papers in Computer Networks and Communications. Recurrent topics in Hyeong Soo Chang's work include Reinforcement Learning in Robotics (33 papers), Advanced Bandit Algorithms Research (14 papers) and Optimization and Search Problems (13 papers). Hyeong Soo Chang is often cited by papers focused on Reinforcement Learning in Robotics (33 papers), Advanced Bandit Algorithms Research (14 papers) and Optimization and Search Problems (13 papers). Hyeong Soo Chang collaborates with scholars based in South Korea, United States and Austria. Hyeong Soo Chang's co-authors include Steven I. Marcus, Michael C. Fu, Jiaqiao Hu, Robert Givan, Edwin K. P. Chong, Mark A. Shayman, Hong-Gi Lee, Ping Hu, Jihoon Yang and Min Fu and has published in prestigious journals such as IEEE Transactions on Automatic Control, Automatica and Operations Research.

In The Last Decade

Hyeong Soo Chang

51 papers receiving 729 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyeong Soo Chang South Korea 14 402 226 220 140 113 57 770
Jiaqiao Hu United States 12 369 0.9× 338 1.5× 143 0.7× 230 1.6× 69 0.6× 66 745
Cees Witteveen Netherlands 18 536 1.3× 106 0.5× 240 1.1× 144 1.0× 116 1.0× 90 988
Janusz Marecki United States 15 362 0.9× 231 1.0× 291 1.3× 58 0.4× 38 0.3× 38 993
Jun-Lin Lin Taiwan 17 421 1.0× 231 1.0× 84 0.4× 134 1.0× 97 0.9× 42 813
Witold Pedrycz Canada 10 287 0.7× 155 0.7× 56 0.3× 194 1.4× 53 0.5× 23 724
Zhuoran Yang United States 15 461 1.1× 150 0.7× 231 1.1× 171 1.2× 148 1.3× 79 843
Rob Powers United States 10 403 1.0× 269 1.2× 159 0.7× 55 0.4× 91 0.8× 11 789
Fernando Paredes Chile 16 333 0.8× 96 0.4× 97 0.4× 100 0.7× 51 0.5× 61 764
Cristina Bazgan France 16 121 0.3× 308 1.4× 251 1.1× 484 3.5× 88 0.8× 61 1.1k

Countries citing papers authored by Hyeong Soo Chang

Since Specialization
Citations

This map shows the geographic impact of Hyeong Soo Chang'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 Hyeong Soo Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hyeong Soo Chang more than expected).

Fields of papers citing papers by Hyeong Soo Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hyeong Soo Chang. 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 Hyeong Soo Chang. The network helps show where Hyeong Soo Chang may publish in the future.

Co-authorship network of co-authors of Hyeong Soo Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Hyeong Soo Chang. A scholar is included among the top collaborators of Hyeong Soo Chang 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 Hyeong Soo Chang. Hyeong Soo Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chang, Hyeong Soo. (2015). Random search for constrained Markov decision processes with multi-policy improvement. Automatica. 58. 127–130. 3 indexed citations
2.
Chang, Hyeong Soo, Jiaqiao Hu, Michael C. Fu, & Steven I. Marcus. (2013). Simulation-Based Algorithms for Markov Decision Processes. CERN Document Server (European Organization for Nuclear Research). 47 indexed citations
3.
Hu, Jiaqiao & Hyeong Soo Chang. (2012). Approximate stochastic annealing for online control of infinite horizon Markov decision processes. Automatica. 48(9). 2182–2188. 2 indexed citations
4.
Chang, Hyeong Soo, et al.. (2010). Adaptive Adversarial Multi-Armed Bandit Approach to Two-Person Zero-Sum Markov Games. IEEE Transactions on Automatic Control. 55(2). 463–468. 8 indexed citations
5.
Chang, Hyeong Soo. (2009). Decentralized Learning in Finite Markov Chains: Revisited. IEEE Transactions on Automatic Control. 54(7). 1648–1653. 5 indexed citations
6.
Hu, Jiaqiao & Hyeong Soo Chang. (2008). A population-based cross-entropy method with dynamic sample allocation. 220. 2426–2431. 1 indexed citations
7.
Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, & Steven I. Marcus. (2007). Simulation-based Algorithms for Markov Decision Processes (Communications and Control Engineering). Springer eBooks. 24 indexed citations
8.
Chang, Hyeong Soo, Jiaqiao Hu, Michael C. Fu, & Steven I. Marcus. (2007). Simulation-based Algorithms for Markov Decision Processes. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 134 indexed citations
9.
Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, & Steven I. Marcus. (2007). Recursive Learning Automata Approach to Markov Decision Processes. IEEE Transactions on Automatic Control. 52(7). 1349–1355. 7 indexed citations
10.
Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, & Steven I. Marcus. (2007). An Asymptotically Efficient Simulation-Based Algorithm for Finite Horizon Stochastic Dynamic Programming. IEEE Transactions on Automatic Control. 52(1). 89–94. 16 indexed citations
11.
Chang, Hyeong Soo. (2006). Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming. Journal of Global Optimization. 35(3). 423–441. 27 indexed citations
12.
Chang, Hyeong Soo, Michael C. Fu, Jiaqiao Hu, & Steven I. Marcus. (2005). An Adaptive Sampling Algorithm for Solving Markov Decision Processes. Operations Research. 53(1). 126–139. 77 indexed citations
13.
Chang, Hyeong Soo. (2004). Technical Note: On Ordinal Comparison of Policies in Markov Reward Processes. Journal of Optimization Theory and Applications. 122(1). 207–217. 2 indexed citations
14.
Chang, Hyeong Soo. (2004). An ant system based exploration-exploitation for reinforcement learning. 3805–3810 vol.4. 10 indexed citations
15.
Chang, Hyeong Soo & Steven I. Marcus. (2003). Approximate receding horizon approach for Markov decision processes: average reward case. Journal of Mathematical Analysis and Applications. 286(2). 636–651. 22 indexed citations
16.
Chang, Hyeong Soo, et al.. (2003). Multitime scale Markov decision processes. IEEE Transactions on Automatic Control. 48(6). 976–987. 64 indexed citations
17.
Chang, Hyeong Soo, et al.. (2002). A model for multi-time scaled sequential decision making processes. 3813–3818 vol.4. 3 indexed citations
18.
Chang, Hyeong Soo & Steven I. Marcus. (2001). Markov Games: Receding Horizon Approach. Digital Repository at the University of Maryland (University of Maryland College Park).
19.
Chang, Hyeong Soo, Robert Givan, & Edwin K. P. Chong. (2000). On-line scheduling via sampling. 62–71. 43 indexed citations
20.
Chong, Edwin K. P., Robert Givan, & Hyeong Soo Chang. (2000). A framework for simulation-based network control via hindsight optimization. 1433–1438 vol.2. 47 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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