Mingming Ha

1.5k total citations · 1 hit paper
38 papers, 1.1k citations indexed

About

Mingming Ha is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Mingming Ha has authored 38 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computational Theory and Mathematics, 23 papers in Artificial Intelligence and 14 papers in Biomedical Engineering. Recurrent topics in Mingming Ha's work include Adaptive Dynamic Programming Control (32 papers), Reinforcement Learning in Robotics (16 papers) and Mechanical Circulatory Support Devices (14 papers). Mingming Ha is often cited by papers focused on Adaptive Dynamic Programming Control (32 papers), Reinforcement Learning in Robotics (16 papers) and Mechanical Circulatory Support Devices (14 papers). Mingming Ha collaborates with scholars based in China, United States and Canada. Mingming Ha's co-authors include Ding Wang, Junfei Qiao, Derong Liu, Mingming Zhao, Long Cheng, Lingzhi Hu, Junfei Qiao, Jun Yan, Junfei Qiao and Xiaobo Guo and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and IEEE Transactions on Industrial Electronics.

In The Last Decade

Mingming Ha

38 papers receiving 1.1k citations

Hit Papers

The intelligent critic framework for advanced optimal con... 2022 2026 2023 2024 2022 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mingming Ha China 17 776 581 428 257 195 38 1.1k
Chunbin Qin China 13 557 0.7× 426 0.7× 270 0.6× 175 0.7× 162 0.8× 49 916
Lili Cui China 12 941 1.2× 676 1.2× 434 1.0× 259 1.0× 215 1.1× 29 1.2k
Avimanyu Sahoo United States 16 575 0.7× 682 1.2× 274 0.6× 314 1.2× 81 0.4× 46 1.1k
Ali Heydari United States 18 632 0.8× 550 0.9× 259 0.6× 261 1.0× 189 1.0× 46 941
Hanguang Su China 17 519 0.7× 616 1.1× 289 0.7× 252 1.0× 86 0.4× 55 1.1k
Hamidreza Modares United States 10 810 1.0× 657 1.1× 510 1.2× 202 0.8× 219 1.1× 23 1.2k
Mohammed Abouheaf Canada 16 384 0.5× 511 0.9× 241 0.6× 371 1.4× 45 0.2× 66 977
Tao Bian United States 13 765 1.0× 677 1.2× 343 0.8× 256 1.0× 206 1.1× 27 1.2k
Geyang Xiao China 17 895 1.2× 628 1.1× 469 1.1× 272 1.1× 161 0.8× 47 1.2k
Zeinab Montazeri Iran 16 261 0.3× 104 0.2× 447 1.0× 151 0.6× 39 0.2× 19 704

Countries citing papers authored by Mingming Ha

Since Specialization
Citations

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

Fields of papers citing papers by Mingming Ha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingming Ha

This figure shows the co-authorship network connecting the top 25 collaborators of Mingming Ha. A scholar is included among the top collaborators of Mingming Ha 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 Mingming Ha. Mingming Ha 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.
Wu, Qitian, et al.. (2024). Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions. 3173–3184. 7 indexed citations
2.
Guo, Xiaobo, Mingming Ha, Xuewen Tao, et al.. (2024). Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation. ACM Transactions on Knowledge Discovery from Data. 18(5). 1–29. 1 indexed citations
3.
Chen, Ying, et al.. (2023). Semi-Supervised Heterogeneous Graph Learning with Multi-Level Data Augmentation. ACM Transactions on Knowledge Discovery from Data. 18(2). 1–27. 1 indexed citations
4.
Tao, Xuewen, et al.. (2023). Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning. 151–160. 4 indexed citations
5.
Qiao, Junfei, Mingming Zhao, Ding Wang, & Mingming Ha. (2023). Adjustable Iterative Q-Learning Schemes for Model-Free Optimal Tracking Control. IEEE Transactions on Systems Man and Cybernetics Systems. 54(2). 1202–1213. 18 indexed citations
6.
Wang, Ding, et al.. (2023). Discounted linear Q-learning control with novel tracking cost and its stability. Information Sciences. 626. 339–353. 6 indexed citations
7.
Wang, Ding, Mingming Ha, & Mingming Zhao. (2023). Advanced Optimal Control and Applications Involving Critic Intelligence. 1 indexed citations
8.
Zhao, Mingming, et al.. (2023). Advanced value iteration for discrete-time intelligent critic control: A survey. Artificial Intelligence Review. 56(10). 12315–12346. 48 indexed citations
9.
Ha, Mingming, et al.. (2023). Stable approximate Q-learning under discounted cost for data-based adaptive tracking control. Neurocomputing. 568. 127048–127048. 1 indexed citations
10.
Wang, Ding, Mingming Zhao, Mingming Ha, & Junfei Qiao. (2023). Convergence and Stability of Optimal Regulation via Generalized N-Step Value Gradient Learning. IEEE Transactions on Neural Networks and Learning Systems. 35(8). 10923–10934. 2 indexed citations
11.
Ha, Mingming, et al.. (2023). Neural Node Matching for Multi-Target Cross Domain Recommendation. 2154–2166. 2 indexed citations
12.
Liu, Derong, Mingming Ha, & Shan Xue. (2022). State of the Art of Adaptive Dynamic Programming and Reinforcement Learning. SHILAP Revista de lepidopterología. 1(2). 93–110. 2 indexed citations
13.
Wang, Ding, et al.. (2022). Advanced Optimal Tracking Control With Stability Guarantee via Novel Value Learning Formulation. IEEE Transactions on Neural Networks and Learning Systems. 35(6). 8254–8265. 5 indexed citations
14.
Ha, Mingming, et al.. (2022). Theoretical Analysis of Value-Iteration-Based Q-Learning with Approximation Errors. 43. 120–125. 1 indexed citations
15.
Zhao, Mingming, Ding Wang, Mingming Ha, & Junfei Qiao. (2022). Evolving and Incremental Value Iteration Schemes for Nonlinear Discrete-Time Zero-Sum Games. IEEE Transactions on Cybernetics. 53(7). 4487–4499. 28 indexed citations
16.
Ha, Mingming, Ding Wang, & Derong Liu. (2021). Neural-network-based discounted optimal control via an integrated value iteration with accuracy guarantee. Neural Networks. 144. 176–186. 23 indexed citations
17.
Wang, Ding, et al.. (2021). Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application. Neural Networks. 143. 121–132. 43 indexed citations
18.
Ha, Mingming, Ding Wang, & Derong Liu. (2021). Offline and Online Adaptive Critic Control Designs With Stability Guarantee Through Value Iteration. IEEE Transactions on Cybernetics. 52(12). 13262–13274. 43 indexed citations
19.
Ha, Mingming, Ding Wang, & Derong Liu. (2020). Generalized value iteration for discounted optimal control with stability analysis. Systems & Control Letters. 147. 104847–104847. 41 indexed citations
20.
Ha, Mingming, Ding Wang, Derong Liu, & Bo Zhao. (2018). Adaptive Event-Based Control for Discrete-Time Nonaffine Systems with Constrained Inputs. 104–109. 1 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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