Ming Hu

546 total citations
37 papers, 300 citations indexed

About

Ming Hu is a scholar working on Computer Networks and Communications, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Ming Hu has authored 37 papers receiving a total of 300 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Networks and Communications, 10 papers in Artificial Intelligence and 9 papers in Cognitive Neuroscience. Recurrent topics in Ming Hu's work include Neural dynamics and brain function (9 papers), Neuroscience and Neuropharmacology Research (5 papers) and Wireless Communication Networks Research (4 papers). Ming Hu is often cited by papers focused on Neural dynamics and brain function (9 papers), Neuroscience and Neuropharmacology Research (5 papers) and Wireless Communication Networks Research (4 papers). Ming Hu collaborates with scholars based in United States, China and Germany. Ming Hu's co-authors include Junshan Zhang, Masao Fukushima, Valentin Dragoi, Mriganka Sur, Murat Yıldırım, Xiaolong Jiang, Rajeev Rikhye, Vincent Breton‐Provencher, Yan Ding and Klaus Obermayer and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and Nature Neuroscience.

In The Last Decade

Ming Hu

33 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming Hu United States	9	93	84	74	65	33	37	300
Sumit Soman India	11	120 1.3×	28 0.3×	64 0.9×	49 0.8×	108 3.3×	38	349
Jinlong Wang China	9	104 1.1×	32 0.4×	30 0.4×	19 0.3×	93 2.8×	36	391
Rushi Bhatt United States	7	64 0.7×	48 0.6×	35 0.5×	26 0.4×	91 2.8×	13	341
Tsung-Yu Hsieh Taiwan	10	122 1.3×	22 0.3×	52 0.7×	47 0.7×	174 5.3×	25	400
Erfan Nozari United States	9	131 1.4×	116 1.4×	52 0.7×	17 0.3×	134 4.1×	22	336
Ildefons Magrans de Abril Japan	5	65 0.7×	42 0.5×	151 2.0×	17 0.3×	37 1.1×	8	338
Shriram Raghunathan India	11	133 1.4×	76 0.9×	54 0.7×	79 1.2×	50 1.5×	46	364
Amrita Chaturvedi India	10	79 0.8×	39 0.5×	44 0.6×	28 0.4×	71 2.2×	28	267
Roman Jašek Czechia	8	36 0.4×	46 0.5×	17 0.2×	18 0.3×	69 2.1×	53	247
Huaqiang Wei China	5	48 0.5×	65 0.8×	171 2.3×	41 0.6×	94 2.8×	6	304

Countries citing papers authored by Ming Hu

Since Specialization

Citations

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

Fields of papers citing papers by Ming Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Hu. A scholar is included among the top collaborators of Ming 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 Ming Hu. Ming Hu 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

Jing, Junzhan, et al.. (2025). Molecular logic for cellular specializations that initiate the auditory parallel processing pathways. Nature Communications. 16(1). 489–489. 5 indexed citations

Jin, Lei, Heather A. Sullivan, Thomas K. Lavin, et al.. (2024). Long-term labeling and imaging of synaptically connected neuronal networks in vivo using double-deletion-mutant rabies viruses. Nature Neuroscience. 27(2). 373–383. 2 indexed citations

Petravicz, Jeremy, Vincent Breton‐Provencher, Ming Hu, et al.. (2024). Stochastic Nanoroughness Inhibits and Reverses Glial Scarring In Vitro and In Vivo via a Mechanobiology Paradigm Involving Piezo‐1. Advanced Functional Materials. 35(1).

Zhou, Na, Brandon Munn, Robert Law, et al.. (2024). Cortical acetylcholine dynamics are predicted by cholinergic axon activity and behavior state. Cell Reports. 43(10). 114808–114808. 3 indexed citations

Ding, Yan, et al.. (2023). Deep forest auto-Encoder for resource-Centric attributes graph embedding. Pattern Recognition. 143. 109747–109747. 2 indexed citations

McKinney, Andrew, Ming Hu, Junzhan Jing, et al.. (2023). Cellular composition and circuit organization of the locus coeruleus of adult mice. eLife. 12. 24 indexed citations

Hu, Ming & Xiaolong Jiang. (2022). PatchView: A Python Package for Patch-clamp DataAnalysis and Visualization. The Journal of Open Source Software. 7(78). 4706–4706. 3 indexed citations

Hu, Ming, et al.. (2021). Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas. PLoS Computational Biology. 17(2). e1008548–e1008548. 4 indexed citations

Zhao, Jia, et al.. (2021). Explore unlabeled big data learning to online failure prediction in safety-aware cloud environment. Journal of Parallel and Distributed Computing. 153. 53–63. 5 indexed citations

10.

Yıldırım, Murat, et al.. (2020). Quantitative third-harmonic generation imaging of mouse visual cortex areas reveals correlations between functional maps and structural substrates. Biomedical Optics Express. 11(10). 5650–5650. 8 indexed citations

11.

Hu, Ming, et al.. (2020). Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex. Cell Reports. 33(6). 108367–108367. 12 indexed citations

12.

Hu, Ming, et al.. (2020). Choice Can Be Predicted from Populations of Bursting Neurons in Superficial Layers of Monkey V1. SSRN Electronic Journal. 2 indexed citations

13.

Hu, Ming, et al.. (2019). High-order coordination of cortical spiking activity modulates perceptual accuracy. Nature Neuroscience. 22(7). 1148–1158. 30 indexed citations

14.

Hu, Ming, et al.. (2019). Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials. PLoS ONE. 14(10). e0222649–e0222649. 8 indexed citations

15.

Hu, Ming & Masao Fukushima. (2015). MULTI-LEADER-FOLLOWER GAMES: MODELS, METHODS AND APPLICATIONS. Journal of the Operations Research Society of Japan. 58(1). 1–23. 37 indexed citations

16.

Hu, Ming & Masao Fukushima. (2013). Existence, Uniqueness, and Computation of Robust Nash Equilibria in a Class of Multi-Leader-Follower Games. SIAM Journal on Optimization. 23(2). 894–916. 22 indexed citations

17.

Hu, Ming & Masao Fukushima. (2011). Smoothing approach to Nash equilibrium formulations for a class of equilibrium problems with shared complementarity constraints. Computational Optimization and Applications. 52(2). 415–437. 6 indexed citations

18.

Jiang, Minghua, Jingli Zhou, & Ming Hu. (2007). Fuzzy Reliability of an iSCSI-based Disk Array System. 4(7). 37–42. 1 indexed citations

19.

Hu, Ming & Minghua Jiang. (2007). Simulation of Attacks on Network-based Error Detection. 2553. 99–102. 1 indexed citations

20.

Hu, Ming & Junshan Zhang. (2004). MIMO ad hoc networks: Medium access control, saturation throughput, and optimal hop distance. Journal of Communications and Networks. 6(4). 317–330. 66 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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