朱圣鑫 - 北京师范大学-香港浸会大学

个人简介

朱圣鑫先后获厦门大学信息与计算科学学士、中国工程物理研究院计算数学硕士、牛津数学博士。他曾获得牛津大学应用数学协同创新中心（OCCAM）国际学生奖学金。博士期间，他在 Andy Wathen 教授的指导下撰写了论文《径向基函数逼近中的数值线性代数问题》。在他攻读博士学位期间学习期间，他曾在 VSN International Ltd 担任知识转化专员，参与转化生物育种软件中的高性能数值算法。该职位由 EPSRC、VSNi 和 Oxford 资助。他的硕士论文是关于分布式超级计算机的高性能并行迭代方法。在2020年加入UIC之前，他曾在西安交利物浦大学任职。朱圣鑫博士于2020年9月加入北京师范大学和UIC数学研究中心，并担任UIC统计与数据科学系副教授。朱圣鑫博士是具有丰富的教学经验。他先后教授过微积分 I、微积分 II、数学类线性代数、理工类线性代数、计算机类离散数学、数学类数值分析、数值线性代数、Python编程、数值最优化和推荐系统等课程。朱圣鑫博士现在为中国数学学会，中国工业与应用数学学会，计算机学会会员， AMS，CSIAM， IEEE，ACM会员。中国工业与应用数学学会金融科技与算法专业委员会委员，广东省工业与应用数学学会理事，广东省数字化学会理事。

研究领域

逼近论与学习理论数值代数与高性能计算数据科学与人工智能基础算法数据压缩、大数据分析、推荐系统等

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

S. Zhu, T.Gu, X. Xu, Z. Mo: Information Splitting for Big Data Analytics. IEEE CyberC 2016: 294-302 A.. Wathen, S. Zhu*: On spectral distribution of kernel matrices related to radial basis functions. Numer. Algorithms 70(4): 709-726 (2015) S. Zhu, A. Wathen: Convexity and Solvability for Compactly Supported Radial Basis Functions with Different Shapes. J. Sci. Comput. 63(3): 862-884 (2015) S. Zhu∗, T. Gu and X. Liu, Minimizing synchronization for sparse iterative methods on distributed supercomputers Computers & Mathematics With Applications, (SCI). 67: 199-209, 2014. S. Zhu*, T. Gu and X. Liu, Solving inverse eigenvalue problem via Householder and rank one matrices, Linear Algebra and its Applications 430:318–334, 2009. X. Lu, S. Zhu*, Q. Niu and Z. Chen, Profile Inference from Heterogeneous data: fundamentals and New Trends, BIS(1), 2019:122-136 Z. Chen, S. Zhu*, Q. Niu and X. Lu, Censorious Young: Knowledge Discovery from High-Throughput Movie Rating Data with LME4, 2019 IEEE ICBDA, pp. 32-36. B. Gao, G. Zhan, H. Wang, Y. Wang, S. Zhu*: Learning with Linear Mixed Model for Group Recommendation Systems, ICMLC 2019:81-85. X., Xu, S. Zhu (2018) Symmetric Sweeping Algorithms for Overlaps of Quadrilateral Meshes of the Same Connectivity. In: Shi Y. et al. (eds) Computational Science ICCS(3) 2018:709-726. Lecture Notes in Computer Science, vol 10862. Springer, Cham. S. Zhu, Fast calculation of restricted maximum likelihood methods for unstructured high-throughput data, 2017 IEEE International Conference on Big Data Analytics, pp.40-43, 2017 B. Shen, Q. Niu and S. Zhu*, Fabricated pictures detection with graph matching, 2020 2nd Asia Paciﬁc Information Technology Conference (AIPT2020), pp.46-51, ACM. Chen, S. Zhu*, Q. Niu and T. Zuo, Knowledge discovery and recommendation with linear mixed models, IEEE-ACCESS, vol. 8, pp. 38304-38317, 2020 Y.Wang, T. Wu, F. Ma, S. Zhu*, Personalized Recommender Systems with Multi-Source Data, SAI (1) 2020:219-233. S. Zhu∗, T. Gu and X. Liu, AIMS: Average information matrix splitting, Mathematical Foundation of Computing, 2020, 3(4): 301-308. T. Zuo, S. Zhu*, J. Lu, A Hybrid Recommender System Combing Singular Value Decomposition and Linear Mixed Models SAI(1) 2020:347-362 X. Liu, L. Zhang and S. Zhu*, Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients. Numer.Math. Theor. Meth. Appl, 2021 Accepted 刘新亮，张镭，朱圣鑫，一类基于带约束能量最小基函数的数值均匀化方法的数值实现，数值计算与计算机应用，已接收，2021 B.Kai, Xu, S. Bu, X. Li Y. Lin and S. Zhu*, xDeepFiG: an extreme Deep Model with Feature Interactions and Generation for CTR Prediction, 2021 3rd International Conference on Big-data Service and Intelligent Computation (BDSIC 2021), accepted. Y. Wang, L. Chen, W. Yang and S. Zhu*, How to choose an online financial product, IEEE International Conference on Decision Aid Science and Applications(DASA), 2020 1103-1109, S. Zhu, Summation of Gaussian shifts as Jacobi’s third Theta function, Mathematical Foundations of Computing, 2020, 3(3):157-163.