The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Kun Zhang sits on this spectrum.
This scientist: 200 publications — 46th percentile
46% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Kun Zhang sits on this spectrum.
This scientist: 53 D-Index — 67th percentile
67% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Kun Zhang is affiliated with Carnegie Mellon University in the United States and has contributed extensively to the field of computer science. Their research spans multiple subfields, notably artificial intelligence, computer vision and pattern recognition, management science and operations research, signal processing, and electrical and electronic engineering.
The scientist's publications cover a wide range of topics, including Bayesian modeling and causal inference, domain adaptation and few-shot learning, advanced graph neural networks, generative adversarial networks and image synthesis, anomaly detection techniques and applications, blind source separation techniques, and machine learning and data classification.
Kun Zhang has authored research articles published in high-profile venues such as:
Frequent publication venues for their work include:
Collaborations have been an important aspect of Kun Zhang's research activities. Frequent coauthors include Biwei Huang, Ruichu Cai, Guangyi Chen, Mingming Gong, and Ignavier Ng.
Kun Zhang's research output demonstrates engagement with computational approaches that integrate learning theory, causal inference, and practical applications in intelligent systems and fault diagnosis. The scientist's work contributes to advancing methodologies in areas intersecting machine learning, signal processing, and artificial intelligence.
Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer
Jakob Runge;Jakob Runge;Sebastian Bathiany;Erik Bollt;Gustau Camps-Valls
Clark Glymour;Kun Zhang;Peter Spirtes
Ya Li;Xinmei Tian;Mingming Gong;Yajing Liu
Min-Ling Zhang;Kun Zhang
Kun Zhang;Bernhard Schlkopf;Krikamol Muandet;Zhikun Wang
Kun Zhang;Jonas Peters;Dominik Janzing;Bernhard Schölkopf
Dominik Janzing;Jonas Peters;Eleni Sgouritsa;Kun Zhang
Aapo Hyvärinen;Kun Zhang;Shohei Shimizu;Patrik O. Hoyer
Kun Zhang;Aapo Hyvärinen
Peter Spirtes;Kun Zhang
Dominik Janzing;Joris Mooij;Kun Zhang;Jan Lemeire
Han Zhao;Remi Tachet des Combes;Kun Zhang;Geoffrey J. Gordon;Geoffrey J. Gordon
Mingming Gong;Kun Zhang;Tongliang Liu;Dacheng Tao
Huan Fu;Mingming Gong;Chaohui Wang;Kayhan Batmanghelich
Han Zhao;Remi Tachet des Combes;Kun Zhang;Geoffrey J. Gordon
Bernhard Schoelkopf;Dominik Janzing;Jonas Peters;Eleni Sgouritsa
Povilas Daniušis;Dominik Janzing;Joris Mooij;Jakob Zscheischler
Kun Zhang;Mingming Gong;Bernhard Scholkopf
Eric V. Strobl;Kun Zhang;Shyam Visweswaran
Kun Zhang;Jonas Peters;Dominik Janzing;Bernhard Schoelkopf
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