Prof. Wang’s lab uses theory and mathematical modeling in the highly cross-disciplinary field of Computational Neuroscience. In close collaborations with experimentalists, Prof. Wang’s research focuses on neural circuit mechanisms of cognitive functions such as how we make difficult decisions, with a special interest in the prefrontal cortex which plays a central role in intelligence and executive control of flexible behavior. He is a founder of the nascent field of Computational Psychiatry. More recently, his group developed connectome-based modeling of large-scale brain circuits to investigate whole-brain dynamics and distributed cognition. His research bridges neuroscience, artificial intelligence and psychiatry. As of August 2026, his h-index is 111, with more than 55,000 total citations. Prof. Wang was recognized as Highly Cited Researcher by Clarivate Analytics, Web of Science Group in 2021 and 2024. He is the author of “Theoretical Neuroscience: Understanding Cognition” published by CRC/Taylor & Francis (2025).

Prof. Xiao-Jing Wang is Chair Professor starting in September 2026 in the School of Computing and Data Science, and School of Biomedical Sciences in The University of Hong Kong. Before joining HKU, Prof. Wang was Distinguished Global Professor of Neural Science of New York University (NYU), and the former founding Provost and Vice President of Research at NYU portal campus in Shanghai at its inception (2012-2017). And prior moving to NYU, Prof. Wang was full Professor of Neurobiology and Director of Swartz Center of Theoretical Neuroscience at Yale University. Prof. Wang earned his PhD degree in Theoretical Physics from the University of Brussels in Belgium with the Highest Distinction with the congratulations of the jury. Prof. Wang is a recipient of Alfred P. Sloan Research Fellowship, Guggenheim Fellowship, Swartz Prize for Theoretical and Computational Neuroscience (2017), Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience (2018), Valentin Braitenberg Award for Computational Neuroscience, Germany (2026); he was elected to the Royal Academy of Belgium (2021). He was Visiting Professor at MIT, Collège de France and Stanford University.  

Mentorship. He is known to have mentored more than 35 graduate students and postdoctoral fellows who went on to be professors at the US institutions such as Yale University, Columbia University, Duke University, University of Chicago, MIT, Princeton University, Dartmouth College; as well as in China and Europe including at Beijing Normal University in Beijing, Tsinghua University in Taiwan, Oxford University in the UK, Champalimaud Center of the Unknown in Lisbon, and Bocconi University in Italy. 

Leadership and service in Neuroscience. In 2010 Prof. Wang co-founded (with Bob Desimone) the Gordon Research Conference on the Neurobiology of Cognition and (with Si Wu, Zach Mainen and Upi Bhalla) the international Computational and Cognitive Neuroscience (CCN) Summer School, Cold-Spring Harbor-Asi a; in 2019 he started the annual Chinese Computational and Cognitive Neuroscience (CCCN) meeting. Prof. Wang served on the Scientific Advisory Board for the International Brain Laboratory consortium supported by the Simons Foundation and the Wellcome Trust (2018-2025); Co-director (with Steve Baccus), Methods in Computational Neuroscience summer course, Marine Biological Laboratory, Woods Hole (2018-2023). Selection committee, Swartz Prize for Theoretical and Computational Neuroscience, Society for Neuroscience (2020-2022). Scientific Advisory Board, McGovern Institute for Brain Research, MIT (2022-present).

1. Wang, X.-J. (2026). Toward a foundational brain model of intelligence. Current Opinion in Neurobiology 98, 103198. 
2. Wang, X.-J. (2025). The missing half of the neurodynamical systems theory. the Transmitter (Simons Foundation), https://www.thetransmitter.org/neural-dynamics/the-missing-half-of-the-neurodynamical-systems-theory/.
3. Koechlin E and X-J Wang (2024) Computational models of prefrontal cortex: two complementary approaches. In The Frontal Cortex: Organization, Networks and Function (Ernst Strüngmann Forum).  Edited by MT Banich, SN Haber and TW Robbins. MIT Press. pp. 179-201.
4. Wang X-J (2022) Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition. Ann. Rev. Neurosci. 45, 533-560.
5. Wang, X-J (2021) 50 years of mnemonic persistent activity: Quo Vadis? Trends in Neuroscience, 44, 888-902.
6. Wang X-J (2020) Macroscopic gradients of synaptic excitation and inhibition across the neocortex. Nature Rev. Neurosci., 21, 169-178.

7. Wang X-J （汪小京）, Hu H （胡海岚）,  Huang CC （黄橙橙）, Kennedy H, Li CY（李澄宇）, Logothetis N, Zhong L-L（吕忠林）,  Luo Q （骆清铭）, Poo MM （蒲慕明）, Tsao D （曹颖）， Wu S（吴思）,  Wu Z（吴朝晖）,  Zhang X （张旭），Zhou D （周栋焯）(2020)  Computational Neuroscience: A frontier of the 21st century. National Science Review 7, 1418-1422. 计算神经科学：21世纪的前沿科学 | NSR观点 : https://academic.oup.com/nsr/article/7/9/1418/5856589?login=false 

   http://mp.weixin.qq.com/s?__biz=MzA3MzQ5MzQyNA==&mid=2656805269&idx=1&sn=cdf35f741cdee3e2916cb95477504a5c&chksm=84a13f4ab3d6b65c38c191272429f9e660e07ad889c3ce7811e60767db0a24d46c03b36bb76c&mpshare=1&scene=1&srcid=0718hWZkxDlcoLNLbVsePoN1&sharer_sharetime=1595118783417&sharer_shareid=e45dc09b926487f2dcaed2fb1d8e4360#rd

8. Wang X-J and Yang GR (2018) A disinhibitory motif and flexible information routing in the brain. Current Opinion in Neurobiology, 49: 75-83.
9. Fusi S, Wang X-J (2016) Long-term, short-term and working memory. In From Neuron to Cognition via Computational Neuroscience, edited by M. Arbib, MIT Press, Chapter 11, pp. 319-344.
10. Wang X-J and Krystal J (2014) Computational Psychiatry. Neuron 84, 638-654.
11. Wang X-J (2013) The prefrontal cortex as a quintessential “cognitive-type” neural circuit: working memory and decision making. Principles of Frontal Lobe Function, Edited by DT Stuss and RT Knight, Second Edition, Cambridge University Press, pp. 226-248. 
12. Wang X-J (2013) Neuronal circuit computation of choice, In Neuroeconomics: Decision Making and the Brain, Second Edition, edited by P. W. Glimcher, C. F. Camerer, E Fehr and R. A. Poldrack. Academic Press, pp. 435-453.
13. Buzsáki G and Wang X-J (2012) Mechanisms of gamma oscillations. Annual Review Neurosci. 35, 203-225.
14. Wang X-J (2010) Neurophysiological and computational principles of cortical rhythms in cognition. Physiological Reviews 90, 1195-1268. 
15. 汪小京 (2010) “21世纪的中国计算神经科学展望”， 《科学时报》（Science Times）, 2010年八月25日.
16. 汪小京 (2009) “理论神经科学导论”《神经科学》 (第三版)，韩济生主编), 北京大学出版社, 第53章，1004-1019页.
17. Wang X-J (2008) Decision making in recurrent neural circuits (invited review). Neuron 60, 215-234. 
18. Wang, X-J (2008) Theoretical and Computational Neuroscience: Attractor network models. In New Encyclopedia of Neuroscience, edited by Larry Squire, Tom Albright, Floyd Bloom, Fred Gage and Nick Spitzer. MacMillan Reference Ltd, pp. 667-679. 
19. Renart A, Brunel N and Wang X-J (2003) Mean-field theory of recurrent cortical networks: Working memory circuits with irregularly spiking neurons. Computational Neuroscience: A Comprehensive Approach. J. Feng Ed., CRC Press, Boca Raton, pp. 432-490. 
20. Wang X-J (2001) Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosci 24, 455-463.