Jiaqiao Hu

1.4k total citations
66 papers, 745 citations indexed

About

Jiaqiao Hu is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Jiaqiao Hu has authored 66 papers receiving a total of 745 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 34 papers in Computational Theory and Mathematics and 32 papers in Management Science and Operations Research. Recurrent topics in Jiaqiao Hu's work include Advanced Multi-Objective Optimization Algorithms (28 papers), Simulation Techniques and Applications (17 papers) and Reinforcement Learning in Robotics (15 papers). Jiaqiao Hu is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (28 papers), Simulation Techniques and Applications (17 papers) and Reinforcement Learning in Robotics (15 papers). Jiaqiao Hu collaborates with scholars based in United States, China and South Korea. Jiaqiao Hu's co-authors include Michael C. Fu, Steven I. Marcus, Hyeong Soo Chang, Enlu Zhou, Ping Hu, Qi Fan, Zheng Su, Yijie Peng, Eugene A. Feinberg and Jian-Qiang Hu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Automatica.

In The Last Decade

Jiaqiao Hu

62 papers receiving 720 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiaqiao Hu United States 12 369 338 230 143 105 66 745
Hyeong Soo Chang South Korea 14 402 1.1× 226 0.7× 140 0.6× 220 1.5× 112 1.1× 57 770
Enlu Zhou United States 13 140 0.4× 318 0.9× 140 0.6× 67 0.5× 83 0.8× 88 610
Abdullah Balamash Saudi Arabia 14 395 1.1× 346 1.0× 300 1.3× 176 1.2× 78 0.7× 36 844
Savaş Dayanik United States 15 260 0.7× 468 1.4× 250 1.1× 86 0.6× 76 0.7× 34 1.1k
Daniel Berleant United States 14 500 1.4× 127 0.4× 163 0.7× 61 0.4× 101 1.0× 78 982
Koos Vrieze Netherlands 8 249 0.7× 374 1.1× 157 0.7× 152 1.1× 75 0.7× 20 796
Khaled Mellouli Tunisia 15 511 1.4× 252 0.7× 168 0.7× 59 0.4× 46 0.4× 41 724
Zhuoran Yang United States 15 461 1.2× 150 0.4× 171 0.7× 231 1.6× 127 1.2× 79 843
Arnab Nilim United States 8 204 0.6× 177 0.5× 74 0.3× 52 0.4× 112 1.1× 12 530
Rubén Saborido Canada 10 142 0.4× 131 0.4× 158 0.7× 73 0.5× 48 0.5× 22 453

Countries citing papers authored by Jiaqiao Hu

Since Specialization
Citations

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

Fields of papers citing papers by Jiaqiao Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiaqiao Hu. 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 Jiaqiao Hu. The network helps show where Jiaqiao Hu may publish in the future.

Co-authorship network of co-authors of Jiaqiao Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Jiaqiao Hu. A scholar is included among the top collaborators of Jiaqiao Hu 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 Jiaqiao Hu. Jiaqiao Hu 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.
Fu, Michael C., Jiaqiao Hu, & Katya Scheinberg. (2025). Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis. IISE Transactions. 58(2). 240–256. 1 indexed citations
2.
Zhang, Jianghua, et al.. (2024). Nonparametric multi-product dynamic pricing with demand learning via simultaneous price perturbation. European Journal of Operational Research. 319(1). 191–205. 2 indexed citations
3.
Hu, Jiaqiao, et al.. (2024). A model-adaptive random search actor critic: convergence analysis and inventory-control case studies. Annals of Operations Research. 1 indexed citations
4.
Hu, Jiaqiao, et al.. (2024). Quantile Optimization via Multiple-Timescale Local Search for Black-Box Functions. Operations Research. 73(3). 1535–1557. 7 indexed citations
5.
Hu, Jiaqiao, et al.. (2024). Relative Q-Learning for Average-Reward Markov Decision Processes With Continuous States. IEEE Transactions on Automatic Control. 69(10). 6546–6560. 2 indexed citations
6.
Fu, Michael C., et al.. (2024). Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs. European Journal of Operational Research. 321(2). 493–502. 2 indexed citations
7.
Hu, Jiaqiao & Michael C. Fu. (2024). Technical Note—On the Convergence Rate of Stochastic Approximation for Gradient-Based Stochastic Optimization. Operations Research. 73(2). 1143–1150. 7 indexed citations
8.
Hu, Jiaqiao, et al.. (2024). Simulation optimization of conditional value-at-risk. IISE Transactions. 57(10). 1167–1181.
9.
Hu, Jiaqiao, et al.. (2023). An ϵ-Greedy Multiarmed Bandit Approach to Markov Decision Processes. SHILAP Revista de lepidopterología. 6(1). 99–112. 1 indexed citations
10.
Hu, Jian-Qiang, et al.. (2023). Black-box CoVaR and Its Gradient Estimation. SSRN Electronic Journal. 2 indexed citations
11.
Hu, Jiaqiao, et al.. (2022). A Stochastic Approximation Method for Simulation-Based Quantile Optimization. INFORMS journal on computing. 34(6). 2889–2907. 12 indexed citations
12.
Zhang, Qi & Jiaqiao Hu. (2021). Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization. Operations Research. 70(6). 3519–3537. 3 indexed citations
13.
Hu, Jiaqiao, et al.. (2018). Some Monotonicity Results for Stochastic Kriging Metamodels in Sequential Settings. INFORMS journal on computing. 30(2). 278–294. 6 indexed citations
14.
Fan, Qi & Jiaqiao Hu. (2018). Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization. INFORMS journal on computing. 30(4). 677–693. 11 indexed citations
15.
Fan, Qi & Jiaqiao Hu. (2016). Simulation optimization via promising region search and surrogate model approximation. Winter Simulation Conference. 649–658. 2 indexed citations
16.
Hu, Jiaqiao, et al.. (2016). Computing equilibrium prices for a capital asset pricing model with heterogeneous beliefs and margin-requirement constraints. European Journal of Operational Research. 256(1). 24–34. 3 indexed citations
17.
Zhou, Enlu & Jiaqiao Hu. (2012). Combining gradient-based optimization with stochastic search. Winter Simulation Conference. 1–12. 4 indexed citations
18.
Hu, Jiaqiao & Ping Hu. (2010). An approximate annealing search algorithm to global optimization and its connection to stochastic approximation. Winter Simulation Conference. 1223–1234. 3 indexed citations
19.
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
20.
Hu, Jiaqiao, Michael C. Fu, & Steven I. Marcus. (2005). Stochastic optimization using model reference adaptive search. Winter Simulation Conference. 811–818. 6 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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