1.基本信息
姓名：周池春，
性别：男，
出生年月：1989.11，
政治面貌：预备党员，
学历：博士研究生，
职称：副教授，
硕士生导师，
成果概况:天津大学理论物理硕士，天津大学材料物理与化学博士，阿里巴巴算法专家岗位offer(P7)。主持国家级项目1项，参与2项。主持省部级项目1项。主持校级项目1项。参与横向项目一项。正式发表SCI 及EI文章15篇，其中一作或通讯10篇。授权实用新型专利2项，软著1项。
Email: zhouchichun@dali.edu.cn
zhouchichun@tju.edu.cn
2. 研究方向：
[1] 深度学习原理，包括自然语言处理、图像识别、语音识别、生成模型、多模态与流形学习
[2] 深度学习应用，包括在生态环保、金融、生物信息、影像医疗、数学以及物理上的应用
[3] 统计力学、量子信息与数学物理
3.主持科研课题
[1] 国家自然科学基金青年基金，主持，国家级，在研（30万）
[2] 云南省科技厅青年基础研究，人工智能+生活垃圾分类解决方案，主持，省部级，在研（5万）
[3] 大理大学，人工智能实验室建设，主持，校级，在研（10万）
4.代表性成果
统计力学、量子信息以及数学物理代表成果
[1] Shen, Y., Zhang, F. L.*, Chen, Y. Z*., & Zhou, C. C*. (2023). Masking quantum information in the Kitaev Abelian anyons. Physica A: Statistical Mechanics and its Applications, 128495.
[2] Shen, Y., Zhou, C. C., & Chen, Y. Z*. (2022). The elementary excitation of spin lattice models: The quasiparticles of Gentile statistics. Physica A: Statistical Mechanics and its Applications, 596, 127223.
[3] Yao Shen, Chi-Chun Zhou*, Wu-sheng Dai and Mi Xie. A group method solving many-body systems in intermediate statistical representation. EPL, 135 (2021) 50001
[4] Zhou, C. C., Chen, Y. Z., & Dai, W. S. (2022). Unified Framework for Generalized Statistics: Canonical Partition Function, Maximum Occupation Number, and Permutation Phase of Wave Function. Journal of Statistical Physics, 186(1), 19.
[5] Zhao, Y. L., Zhou, C. C., Li, W. D., & Dai, W. S*. (2020). Bose-like few-fermion systems. Physics Letters A, 384(31), 126791.
[6] Zhou C C, Dai W S. Canonical partition functions: ideal quantum gases, interacting classical gases, and interacting quantum gases[J]. Journal of Statistical Mechanics: Theory and Experiment, 2018, 2018(2): 023105
[7] Zhou C C, Dai W S. A statistical mechanical approach to restricted integer partition functions[J]. Journal of Statistical Mechanics: Theory and Experiment，2018， 2018(5): 053111
[8] Zhou C C, Dai W S. Calculating eigenvalues of many-body systems from partition functions[J]. Journal of Statistical Mechanics: Theory and Experiment, 2018, 2018(8): 083103.
[9] Zhou C C, Li W D, Dai W S. Acoustic scattering theory without large-distance asymptotics[J]. Journal of Physics Communications, 2018, 2(4): 041002.
[10] Ma, P. F., Zhou, C. C., Chen, Y. Z., & Pang, H. (2012). Relations Between Intermediate Statistics and Quantum Statistics. Modern Physics Letters B, 26(32), 1250212.
[11] Zhou C C, Dai W S. The Brownian Motion in an Ideal Quantum Qas[J]. arXiv preprint arXiv:2003.07171,2020 - arxiv.org.
[12] Zhou C C, et al. Converting Lattices into Networks: The Heisenberg Model and Its Generalizations with Long-Range Interactions. arXiv preprint arXiv:2012.12074, 2020.
深度学习方法在天文等领域应用
[13] Fang, G., Ba, S(研究生)., Gu, Y., Lin, Z., Hou, Y., Qin, C., ... Zhou,C.C,…& Kong, X. (2023). Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set. The Astronomical Journal, 165(2), 35.
[14] Gao, L., Guo, Q., Xu, R., Dong, H., Zhou, C., Yu, Z.(研究生), ... & Wu, X. (2023). Loop optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade‐off through cross‐strategy between machine learning and the B‐factor analysis. GCB Bioenergy, 15(2), 128-142.
[15] An, L., Ji, F., Yin, Y., Liu, Y*., & Zhou, C. (2022). Modeling of Red Blood Cells in Capillary Flow Using Fluid–Structure Interaction and Gas Diffusion. Cells, 11(24), 3987.
[16] ZHOU, Chichun, et al. A Distance-deviation Consistency and Model-independent Method to Test the Cosmic Distance–Duality Relation. The Astrophysical Journal, 2021, 909.2: 118.
[17] Zhou, C., Gu, Y., Fang, G., & Lin, Z. (2022). Automatic morphological classification of galaxies: convolutional autoencoder and bagging-based multiclustering model. The Astronomical Journal, 163(2), 86.
[18] Zhang, P., Yang, J. Y., Zhu, H., Hou, Y. J., Liu, Y., & Zhou, C. C*. (2021). Failure in Stock Price Prediction: A Comparison between the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms. International Journal of Computers, Communications & Control, 16(6).
[19] Qin, C. X., Liu, R. H., Li, M. C., & Zhou, C. C. (2021). An Effective and Efficient Method to Solve the High-Order and the Non-Linear Ordinary Differential Equations: the Ratio Net[J]. arXiv preprint arXiv:2105.11309, 2021 - arxiv.org. （under review）
[20] Hou, Y. J., Xie, Z. X., & Zhou, C. C. (2021). An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy. arXiv preprint arXiv:2109.05526. （under review）
[21] Zhang, Z. Y., Shao, G. X., Qiu, C. M., Hou, Y. J., Zhao, E. M., & Zhou, C. C*. (2022). Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms. arXiv preprint arXiv:2206.03321.
[22] Wang, T., Li, G. Y., Li, X. H., Zhou, C. C., Wang, Y. Y., Li, L. J., & Yang, Y. T. (2022). To Simulate the Spread of Infectious Diseases by the Random Matrix. arXiv preprint arXiv:2204.10188.
深度学习原理
[23] Zhou, C. C., Tu, H. L., Liu, Y., & Hua, J. (2020). Activation functions are not needed: the ratio net. arXiv preprint arXiv:2005.06678. （under review）
[24] Zhou, C. C., & Liu, Y. (2020). The pade approximant based network for variational problems. arXiv preprint arXiv:2004.00711. （under review）
[25] Zhou, C. C., Tu, H. L., Liu, Y., & Zhang, F. L. (2020). Networks with pixels embedding: a method to improve noise resistance in images classification. arXiv preprint