张俊
联系方式：jzhang3@saif.sjtu.edu.cn
秘书：金程英
邮箱：cyjin@saif.sjtu.edu.cn


教授简介
研究领域
学术成果
SAIF所授课程



教育背景：博士学位：加利福尼亚大学伯克利分校神经生物学，1992
学士学位：复旦大学物理学，1985
教授介绍：张俊教授是上海高级金融学院****，美国密西根大学心理学系终身教授、数学系终身教授、大数据研究所核心教员。他广泛的研究兴趣包括行为博弈、行为金融、概率与不确定性、风险与模糊、效用与偏好的数学刻画、认知科学和人工智能等。他擅长于认知建模、大数据挖掘、机器学习、信息几何等领域。
张俊教授是美国心理学协会会士，心理规律学会会士。历任数学心理学协会副主席、主席、执行委员会委员，全美脑与行为科学协会联盟（FABBS）理事及执行董事。他1992年在加州伯克利大学获得博士，随即受聘于密西根大学任终身教职。在学术休假年度，他在澳洲墨尔本大学、法国科学院马赛所、加拿大滑铁卢大学、日本理研脑科学研究所、英国剑桥牛顿研究所、美国哈佛大学等，担任客座研究员、访问教授等席位。现任《信息几何学》期刊创刊联合主编，历任《数学心理学》期刊副主编，并一直为联邦政府做评议、咨询。
张俊教授主持的M3实验室（“Mind，Machine，Mathematics”）长期开展认知建模、机器学习、人机界面、累脑人工智能算法研究，持续获得美国自然科学基金会、国防部等部门的科研经费支持和支撑。近年来，他在社交、金融、交通等应用领域开展行为大数据研究，为滴滴出行等国内公司提供咨询服务。


认知建模、大数据挖掘、机器学习、信息几何。
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. Zhang, Jun, 2019, Characterizing projective geometry of binocular visual space through Mobius transformation, Journal of Mathematical Psychology.
4. Grigorian, S. and Jun Zhang, 2019, (Para)-holomorphic and conjugate on (para- )Hermitian and (para-)Kahler manifolds, Results in Mathematics.
5. Zhang, Jun and G. Khan, 2019, From Hessian to Weitzenbock: Manifolds with torsioncarrying connections, Information Geometry.
6. Qian, N. and Jun Zhang, 2019, Neuronal Firing Rate As Code Length: a Hypothesis, Computation Brain & Behavior.
7. Naudts, Jan, and Jun Zhang, 2018, Rho–tau embedding and gauge freedom in information geometry, Information Geometry.
8. Zhang, Jun，and H. Zhang, 2018, Categorization based on similarity and features: The reproducing kernel Banach space (RKBS) approach. In W. Batchelder, H. Colonius, E.N. Dzhafarov, J. Myung (Eds.), New Handbook of Mathematical Psychology.
9. Greenfield, M., and Jun Zhang, 2018, Resolution to the topological social choice paradox, Mathematical Social Sciences.
10. Fei, T., and Jun Zhang, 2017, Interaction of Codazzi couplings with (para)-Kahler geometry, Results in Mathematics.
11. Leok, M., and Jun Zhang, 2017, Connecting information geometry to geometric mechanics, Entropy.
12. Zhang, H., and Jun Zhang, 2017, Learning with reproducing kernel Banach spaces, New Trends in Analysis and Interdisciplinary Applications.
13. Zhang, Jun, and Y. Sun, 2016, Subset systems: Mathematical abstraction of object and context. In Houpt and Blaha (Eds.), Mathematical Models of Perception and Cognition.
14. Tao, James, and Jun Zhang, 2016, Transformations and coupling relations for affine connections, Journal of Differential Geometry and Applications.
15. Zhang, Jun, 2015, On monotone embedding in information geometry, Entropy.
16. Ilin, R., Jun Zhang, L. Perlovsky, and R. Kozma, 2014, Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process, Cognitive Neurodynamics.
17. Zhang, Jun, 2013, Nonparametric information geometry: From divergence function to referential-representational biduality on statistical manifolds, Entropy.
18. Zhang, H. and Jun hang, 2013, Vector-valued Reproducing Kernel Banach Spaces with applications to multi-task learning, Journal of Complexity.
19. Zhang, H. and Jun Zhang, 2012, Regularized learning in Banach space as an optimization problem: Representer theorems, ournal of Global Optimization.
20. Zhang, Jun, T. Hedden and A. Chia, 2012, Perspective-taking and depth of theory-ofmind reasoning in sequential-move games, Cognitive Science.
21. Yin, G. and Jun Zhang, 2011, On decomposing stimulus and response waveforms in event-related potentials (ERP) recordings, Ieee Transactions on Biomedical Engineering.
22. Zhang, H. and Jun Zhang, 2011, Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products, Applied and Computational Harmonic Analysis.
23. Zhang, H. and Jun Zhang, 2010, Generalized semi-inner products with application to regularized learning, Journal of Mathematical Analysis and Application.
24. Park, J and Jun Zhang, 2010, Sensorimotor locus of the buildup activity in monkey LIP neurons, Journal of Neurophysiology.
25. 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.
26. Stevens, G. and Jun Zhang, 2009, A dynamic systems model of infant attachment, IEEE Transaction of Autonomous Mental Development.
27. Zhang, H., Y. Xu, and Jun Zhang, 2009, Reproducing kernel Banach spaces for machine learning, Journal of Machine Learning Research.
28. Zhang, Jun, K.C. Berridge, A.J. Tindell, K.S. Smith, and J.W. Aldridge, 2009, Modeling the neural computation of incentive salience, Plos Computational Biology.
29. Zhang, Jun, 2009, Adaptive learning via selectionism and Bayesianism Part II: The sequential case, Neural Networks.
30. Zhang, Jun, 2009, Adaptive learning via selectionism and Bayesianism. Part I: A connection, Neural Networks.
31. Yin, G., Jun Zhang, Y. Tian, and D-Z Yao, 2009, A multi-component decomposition algorithm for event-related potentials, Journal of Neuroscience Methods.
32. He, Lixia, Jun Zhang, Tiangang Zhou, and Lin Chen, 2009, Connectedness affects dot numerosity judgment: implications for configural processing, Psychonomic Bulletin and Review.
33. Zhang, Jun and G. Khan, 2020, Statistical mirror symmetry.
34. Sun, Y. and Jun Zhang, 2020, Characterizing the structure of interval and semi-order.
35. Lei, Y. and Jun Zhang, 2020, Limit and convergence of a sequence.
36. 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.
37. Lin, R., Zhang, J. and Zhang, H., 2020, On Reproducing Kernel Banach Spaces: Generic definitions and unified framework of construction.
38. Zhang, Jun, 2020, Sufficiency, necessity, and causal powers in probabilistic causation.

数学心理学、神经计算理论、行为数据挖掘、基础心理学、信息几何。

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