Jun Zhang is a Visiting Research Professor of Finance at SAIF. He is a tenured Full Professor of Psychology and Full Professor of Statistics at the University of Michigan Ann Arbor, and is a core faculty of Michigan Institute of Data Science. His broad research interests include behavioral game theory, behavioral finance, mathematical modeling of probability and uncertainty, risk and ambiguity, utility and preference, cognitive science and AI, etc. He specializes in cognitive modeling, behavioral data mining, machine learning, and information geometry.
Jun Zhang is a fellow of Association of Psychological Sciences and a fellow of Psychonomic Society. He has served as the President, Vice President, and member of Executive Board of the Society for Mathematical Psychology, and as a Council Member and elected Governing Board Member of the Federation of Associations in Brain and Behavioral Sciences (FABBS). He obtained his Ph.D. from University of California at Berkeley in 1992, and has since worked at the University of Michigan as a tenured faculty. During sabbatical years, he held visiting positions at the University of Melbourne (Australia), CNRS Marseille (France), University of Waterloo (Canada), RIKEN Brain Science Institute (Japan), Newton Institute at Cambridge (UK), and Center for Mathematical Sciences and Applications (CMSA) at Harvard University. He is a founding Co-Editor of the journal Information Geometry and has served as an Associate Editor of the Journal of Mathematical Psychology. He has served on numerous government panels and committees.
Jun Zhang directs the M3 Lab (“Mind, Machine and Mathematics”) which conducts research spanning cognitive science, machine learning, human-system interface, brain-like computation and artificial intelligence, under continuous grant support from US National Science Foundation (NSF) and Department of Defense (DOD). In recent years, he also spent time on big data research in social network, finance, and transportation, with consultancy projects for companies like DiDi Chuxing.
Journal Publications
1. Zhang, Jun and Z. Shi, 2020, Bayesian inference as probability transfer across sample spaces, Decision.
2. Kim, D.-Y., Jung, E.K., Zhang, J., Lee, S.-Y., Lee, and J.-H., 2020, Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning, Human Brain Mapping.
3. Grigorian, S. and Jun Zhang, 2019, (Para)-holomorphic and conjugate on (para- )Hermitian and (para-)Kahler manifolds, Results in Mathematics.
4. Zhang, Jun and G. Khan, 2019, From Hessian to Weitzenbock: Manifolds with torsioncarrying connections, Information Geometry.
5. Qian, N. and Jun Zhang, 2019, Neuronal Firing Rate As Code Length: a Hypothesis, Computation Brain & Behavior.
6. Zhang, Jun, 2019, Characterizing projective geometry of binocular visual space through Mobius transformation, Journal of Mathematical Psychology.
7. Naudts, Jan, and Jun Zhang, 2018, Rho–tau embedding and gauge freedom in information geometry, Information Geometry.
8. Greenfield, M., and Jun Zhang, 2018, Null preference and the resolution of the topological social choice paradox, Mathematical Social Sciences.
9. Fei, T., and Jun Zhang, 2017, Interaction of Codazzi couplings with (para)-Kahler geometry, Results in Mathematics.
10. Leok, M., and Jun Zhang, 2017, Connecting information geometry to geometric mechanics, Entropy.
11. Tao, James, and Jun Zhang, 2016, Transformations and coupling relations for affine connections, Differential Geometry and Its Applications.
12. Zhang, Jun, 2015, On monotone embedding in information geometry, Entropy.
13. Ilin, R., Jun Zhang, L. Perlovsky, and R. Kozma, 2014, Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process, Cognitive Neurodynamics.
14. Zhang, Jun, 2013, Nonparametric information geometry: From divergence function to referential-representational biduality on statistical manifolds, Entropy.
15. Zhang, H. and Jun hang, 2013, Vector-valued Reproducing Kernel Banach Spaces with applications to multi-task learning, Journal of Complexity.
16. Zhang, H. and Jun Zhang, 2012, Regularized learning in Banach space as an optimization problem: Representer theorems, Journal of Global Optimization.
17. Zhang, Jun, T. Hedden and A. Chia, 2012, Perspective-taking and depth of theory-ofmind reasoning in sequential-move games, Cognitive Science.
18. Yin, G. and Jun Zhang, 2011, On decomposing stimulus and response waveforms in event-related potentials (ERP) recordings, Ieee Transactions on Biomedical Engineering.
19. Zhang, H. and Jun Zhang, 2011, Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products, Applied and Computational Harmonic Analysis.
20. Zhang, H. and Jun Zhang, 2010, Generalized semi-inner products with application to regularized learning, Journal of Mathematical Analysis and Applications.
21. Park, J and Jun Zhang, 2010, Sensorimotor locus of the buildup activity in monkey LIP neurons, Journal of Neurophysiology.
22. Stern, E., Y. Liu, W. Gehring, J. Lister, G. Yin, Jun Zhang, K. Fitzgerald, J. Himle, J. Abelson, and S. Taylor, 2010, Chronic medication does not affect hyperactive error responses in obsessive-compulsive disorder, Psychophysiology.
23. Stevens, G. and Jun Zhang, 2009, A dynamic systems model of infant attachment, IEEE Transactions on Autonomous Mental Development.
24. Zhang, H., Y. Xu, and Jun Zhang, 2009, Reproducing kernel Banach spaces for machine learning, Journal of Machine Learning Research.
25. Zhang, Jun, K.C. Berridge, A.J. Tindell, K.S. Smith, and J.W. Aldridge, 2009, A Neural Computational Model of Incentive Salience, Plos Computational Biology.
26. Zhang, Jun, 2009, Adaptive learning via selectionism and Bayesianism Part II: The sequential case, Neural Networks.
27. Zhang, Jun, 2009, Adaptive learning via selectionism and Bayesianism. Part I: A connection, Neural Networks.
28. Yin, G., Jun Zhang, Y. Tian, and D-Z Yao, 2009, A multi-component decomposition algorithm for event-related potentials, Journal of Neuroscience Methods.
29. He, Lixia, Jun Zhang, Tiangang Zhou, and Lin Chen, 2009, Connectedness affects dot numerosity judgment: implications for configural processing, Psychonomic Bulletin & Review.
Working Papers
1. Zhang, Jun and G. Khan, 2020, Statistical mirror symmetry.2. Sun, Y. and Jun Zhang, 2020, Characterizing the structure of interval and semi-order.
3. Lei, Y. and Jun Zhang, 2020, Limit and convergence of a sequence.
4. Zhang, J. Ke, A., Wang, H., Gong, Y., Tan, X., and Liu, W., 2020, Dynamics of boom and bust: Analysis of investor behavior during the 2015 Chinese stock market crash.
5. Lin, R., Zhang, J. and Zhang, H., 2020, On Reproducing Kernel Banach Spaces: Generic definitions and unified framework of construction.
6. Zhang, Jun, 2020, Sufficiency, necessity, and causal powers in probabilistic causation.
