Mingjun Zhong

4.7k citations
55 papers · 2.6k indexed · 2 hit papers · h-index 17

Mingjun Zhong

51 papers receiving 2.6k citations

Hit Papers

Sequence-to-Point Learning With Neural Networks for Non-I...35720142026201820224008001.2k

Peers

Mingjun Zhong
Comparison fields: 5 of 174
  • Computer Vision and Pattern Recognition 538
  • Health Informatics 33
  • Building and Construction 300
  • Artificial Intelligence 706
  • Computational Mathematics 11
Replace Dongbo Xi with:
Dongbo Xi China
Keyu Duan China
Zhiyuan Qi China
S. Lecœuche France
Zachary C. Lipton United States
豊 松尾
Yimin Yang China
Mohammad Ghavamzadeh United States
Weisi Guo United Kingdom
Wenjie Yang China
Mingjun Zhong relative to Dongbo Xi China Dongbo Xi's profile →
Citations per field
00.5×5.3×
Dongbo Xi · 1×
Citations per year

Countries citing papers authored by Mingjun Zhong

Since Specialization
Citations

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

Fields of papers citing papers by Mingjun Zhong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mingjun Zhong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mingjun Zhong Line = papers co-authored together Mingjun Zhong links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 20251
4 20250
5 20242
6 20248
7 20241
8 20230
9 20234
10 202212
11 202210
12 202140
13 20216
14 201918
15 201978
16 201514
17
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
201447
18
On the Fully Bayesian Treatment of Latent Gaussian Models using Stochastic Simulations
20122
19
Reversible Jump MCMC for Non-Negative Matrix Factorization
200912
20 200770

About Mingjun Zhong

Mingjun Zhong is a scholar working on Health Informatics, Sensory Systems and Signal Processing, having authored 55 papers that have together received 2.6k indexed citations. Recurring topics across this work include Smart Grid Energy Management (9 papers), Blind Source Separation Techniques (8 papers), Bayesian Methods and Mixture Models (5 papers), Hearing, Cochlea, Tinnitus, Genetics (5 papers), Gaussian Processes and Bayesian Inference (5 papers), Building Energy and Comfort Optimization (4 papers), Energy Load and Power Forecasting (4 papers) and Spectroscopy and Chemometric Analyses (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (538 citations), Health Informatics (33 citations) and Building and Construction (300 citations). Mingjun Zhong has collaborated with scholars based in China, United Kingdom and Greece. Frequent co-authors include Charles Sutton, Nigel Goddard, Stefano Squartini, Chaoyun Zhang, Mark Girolami, Chao Gao, Fabien Lotte, Yongjin Guo, Anatole Lécuyer and Wenpeng Luan. Their work appears in journals such as Neurocomputing, Reliability Engineering & System Safety, Journal of Medical Genetics, Information Sciences and Ocean Engineering.

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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