张佛德
- 教授 博士生导师
- 职务 教授 博士生导师
Email：fredzh@swufe.edu.cn
四川省成都市温江区柳台大道555号
研究方向
统计学习、深度学习、迁移学习、可靠性统计
张佛德，西南财经大学统计研究中心、统计与数据科学学院教授，博士生导师。新加坡国立大学博士后研究员，西北工业大学与美国南卫理公会大学联合培养博士。2020年入选四川省高层次人才，2022年入选西南财经大学“光华英才工程”学术类人才。当前研究兴趣涉及统计学习、深度学习、迁移学习、可靠性统计等领域。在《IEEE TPAMI》《IEEE TIT》《IEEE TR》《IEEE TNNLS》《Inverse Problems》等学术期刊上发表论文30余篇。现主持完成国家自然科学基金面上项目1项，主持四川省科技厅中央引导地方自由探索项目1项，主持完成4项中央高校基金，主研多项国家自然科学基金。现任中国现场统计研究会贝叶斯统计分会常务理事、中国现场统计研究会旅游大数据分会常务理事、全国工业统计学教学研究会理事、中国统计教育学会理事，统计学期刊《Communications in Statistics》和美国计算机协会旗下期刊《 ACM Transactions on Probabilistic Machine Learning 》副主编等。
热忱欢迎数学基础扎实、编程能力强、具有吃苦耐劳精神的本科生、硕士生和博士生加入。更多信息参见GitHub个人主页https://fodezhang.github.io/
热忱欢迎数学基础扎实、编程能力强、具有吃苦耐劳精神的本科生、硕士生和博士生加入。更多信息参见GitHub个人主页https://fodezhang.github.io/
主持项目
1、2024.8—2026.8，四川省科技厅中央引导地方专项“自由探索项目”，项目编号：2024ZYD0135（主持）
2、2021.1—2024.12，国家自然科学基金“面上项目”，项目编号：12071372（主持）
3、2020.1—2020.12，中央高校基本科研业务费专项资金“青年教师成长项目”， 项目编号：JBK2001001（主持）
4、2019.1—2019.12，中央高校基本科研业务费专项资金“青年教师成长项目”，项目编号：JBK1901053（主持）
5、2018.1—2018.12，中央高校基本科研业务费专项资金“青年教师成长项目”，项目编号：JBK130167（主持）
学术论文（部分）
[32] Q. Shu F.D. Zhang, L.J. Shen, H. K.T. Ng. RUL Prediction With Cross-Domain Adaptation Based on Reproducing Kernel Hilbert Space, IEEE Transactions on Reliability, 74（2025）3871 - 3883.
[31] J.L. Wang, F.D. Zhang, H. K.T. Ng, Y.M. Shi. Remaining Useful Life Prediction via Information Enhanced Domain Adversarial Generalization, IEEE Transactions on Reliability, 74（2025）2837 - 2850.
[30] M. Jiang, K. Zhou, J. Gao, F.D. Zhang . Integrating causal representations with domain adaptation for fault diagnosis, Reliability Engineering & System Safety, 260 (2025) 110999.
[29] F.D. Zhang, H. K.T. Ng, L.J. Shen. Robust Estimation and Selection for Degradation Modeling With Inhomogeneous Increments, IEEE Transactions on Reliability, 73 (2024) 560 - 575.
[28] J.M. Liu, W.L. Xu, F.D. Zhang, H. Lian. Properties of Standard and Sketched Kernel Fisher Discriminant, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (2023) 10596 - 10602.
[27] J.L. Wang, F.D. Zhang, J.C. Zhang, W. Liu, K.Zhou. A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data. Reliability Engineering & System Safety, 231 (2023) 108976
[26] F.D. Zhang, J.L. Li, H.K.T. Ng. Minimum $f$-Divergence Estimation with Applications to Degradation Data Analysis. IEEE Transactions on Information Theory, 68 (2022) 6774 -- 6789
[25] L. Wang, Y. Lio, Y. Tripathi, S. Dey, F.D. Zhang . Inference of dependent left-truncated and right-censored competing risks data from a general bivariate class of inverse exponentiated distributions. Statistics, 56 (2022) 347-374
[24] F. Zhang , R. Li , H. Lian, Dipankar Bandyopadhyay. Sparse reduced-rank regression for multivariate varying-coefficient models. Journal of Statistical Computation and Simulation. 91(2021) 752-767
[23] R. Wang, M. Wu, Y. Shi, HKT Ng, F. Zhang*. The geometric structure on a degradation model with application to optimal design under a cost constraint. Journal of Computational and Applied Mathematics 382 (2021) 113081.
[22] F. Zhang, R. Li, H. Lian. Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection. Information Sciences 547 (2021) 244-254
[21] F. Zhang, X. Shi, H.K.T. Ng. Information Geometry of the Exponential Family of Distributions with Progressive Type-II Censoring. Entropy 23 (2021) 687.
[20] F. Zhang, W. Zhang, R. Li, H. Lian. Faster convergence rate for functional linear regression in reproducing kernel Hilbert spaces. Statistics, 54 (2020) 167-181
[19] F. Zhang, X. Wang, R. Li, H. Lian. Randomized sketches for sparse additive models. Neurocomputing 385 ( 2020) 80-87
[18] H. Lian, F. Zhang, W. Lu. Randomized sketches for kernel CCA. Neural Networks 127 (2020) 29-37
[17] W. Zhao, F. Zhang, H. Lian, Debiasing and Distributed Estimation for High-Dimensional Quantile Regression. IEEE transactions on neural networks and learning systems 31 (2020) 2569 - 2577
[16] F. Zhang*, HKT. Ng, Y. Shi, Mis-Specification Analysis of Wiener Degradation Models by Using f-Divergence with Outliers. Reliability Engineering & System Safety, 195 (2020)106751
[15] F.D. Zhang*, Y.M. Shi. Geometry on the statistical manifold induced by the degradation model with soft failure data. Journal of Computational and Applied Mathematics, 363 (2020) 211-222.
[14]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Geometry on degradation models and mis-specification analysis by using α-divergence. Physica A 527(2020) 121343
[13] G. Wang, F.D. Zhang, H. Lian. Directional regression for functional data. Journal of Statistical Planning and Inference 204 (2020) 1-17
[12] F.D. Zhang, H.K.T. Ng, Y.M. Shi, R.B. Wang. Amari–Chentsov structure on the statistical manifold of models for accelerated life tests. TEST 28 (2019) 77-105.
[11] W Zhao, F Zhang, X Wang, R Li, H Lian, Principal varying coefficient estimator for high-dimensional models. Statistics 53 (2019) 1234-1250
[10] F.D. Zhang, H. Lian. Partially Functional Linear Regression with Quadratic Regularization. Inverse Problems 35 (2019) 105002.
[9] F.D. Zhang, Y.M. Shi, H.K.T. Ng, R.B. Wang. Information Geometry of Generalized Bayesian Prediction Using alpha-divergences as Loss Functions. IEEE Transactions on Information Theory 64 (2018) 1812 - 1824.
[8]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Information geometry on the curved q-exponential family with application to survival data analysis. Physica A: Statistical Mechanics and its Applications 512 (2018) 788--802.
[7] F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Bayesian duality and risk analysis on the statistical manifold of exponential family with censored data. Journal of Computational and Applied Mathematics 342 (2018) 534–549.
[6]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. On alternative q-Weibull and q-extreme value distributions: Properties and applications. Physica A: Statistical Mechanics and its Applications 490 (2018) 1171-1190.
[5]F.D. Zhang*, Y.M. Shi, C.F. Zhang. Geometry of an accelerated model with censored data. Journal of Computational and Applied Mathematics 317 (2017) 137–145.
[4]F.D. Zhang*, Y.M. Shi, R.B. Wang. Geometry of the q-exponential distribution with dependent competing risks and accelerated life testing. Physica A: Statistical Mechanics and its Applications 468 (2017) 552–565.
[3]F.D. Zhang*, Y.M. Shi, H.K.T. Ng, R.B. Wang. Tsallis statistics in reliability analysis: Theory and methods. The European Physical Journal Plus 131 (2016) 379.
[2]F.D. Zhang*, Y.M. Shi. Geometry of exponential family with competing risks and censored data. Physica A: Statistical Mechanics and its Applications 446 (2016) 234–245.
[1]F.D. Zhang. Periodic solutions for a Cauchy problem on time scales. Arab Journal of Mathematical Sciences. 21 (2015) 237–252.
1、2024.8—2026.8，四川省科技厅中央引导地方专项“自由探索项目”，项目编号：2024ZYD0135（主持）
2、2021.1—2024.12，国家自然科学基金“面上项目”，项目编号：12071372（主持）
3、2020.1—2020.12，中央高校基本科研业务费专项资金“青年教师成长项目”， 项目编号：JBK2001001（主持）
4、2019.1—2019.12，中央高校基本科研业务费专项资金“青年教师成长项目”，项目编号：JBK1901053（主持）
5、2018.1—2018.12，中央高校基本科研业务费专项资金“青年教师成长项目”，项目编号：JBK130167（主持）
学术论文（部分）
[32] Q. Shu F.D. Zhang, L.J. Shen, H. K.T. Ng. RUL Prediction With Cross-Domain Adaptation Based on Reproducing Kernel Hilbert Space, IEEE Transactions on Reliability, 74（2025）3871 - 3883.
[31] J.L. Wang, F.D. Zhang, H. K.T. Ng, Y.M. Shi. Remaining Useful Life Prediction via Information Enhanced Domain Adversarial Generalization, IEEE Transactions on Reliability, 74（2025）2837 - 2850.
[30] M. Jiang, K. Zhou, J. Gao, F.D. Zhang . Integrating causal representations with domain adaptation for fault diagnosis, Reliability Engineering & System Safety, 260 (2025) 110999.
[29] F.D. Zhang, H. K.T. Ng, L.J. Shen. Robust Estimation and Selection for Degradation Modeling With Inhomogeneous Increments, IEEE Transactions on Reliability, 73 (2024) 560 - 575.
[28] J.M. Liu, W.L. Xu, F.D. Zhang, H. Lian. Properties of Standard and Sketched Kernel Fisher Discriminant, IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (2023) 10596 - 10602.
[27] J.L. Wang, F.D. Zhang, J.C. Zhang, W. Liu, K.Zhou. A flexible RUL prediction method based on poly-cell LSTM with applications to lithium battery data. Reliability Engineering & System Safety, 231 (2023) 108976
[26] F.D. Zhang, J.L. Li, H.K.T. Ng. Minimum $f$-Divergence Estimation with Applications to Degradation Data Analysis. IEEE Transactions on Information Theory, 68 (2022) 6774 -- 6789
[25] L. Wang, Y. Lio, Y. Tripathi, S. Dey, F.D. Zhang . Inference of dependent left-truncated and right-censored competing risks data from a general bivariate class of inverse exponentiated distributions. Statistics, 56 (2022) 347-374
[24] F. Zhang , R. Li , H. Lian, Dipankar Bandyopadhyay. Sparse reduced-rank regression for multivariate varying-coefficient models. Journal of Statistical Computation and Simulation. 91(2021) 752-767
[23] R. Wang, M. Wu, Y. Shi, HKT Ng, F. Zhang*. The geometric structure on a degradation model with application to optimal design under a cost constraint. Journal of Computational and Applied Mathematics 382 (2021) 113081.
[22] F. Zhang, R. Li, H. Lian. Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection. Information Sciences 547 (2021) 244-254
[21] F. Zhang, X. Shi, H.K.T. Ng. Information Geometry of the Exponential Family of Distributions with Progressive Type-II Censoring. Entropy 23 (2021) 687.
[20] F. Zhang, W. Zhang, R. Li, H. Lian. Faster convergence rate for functional linear regression in reproducing kernel Hilbert spaces. Statistics, 54 (2020) 167-181
[19] F. Zhang, X. Wang, R. Li, H. Lian. Randomized sketches for sparse additive models. Neurocomputing 385 ( 2020) 80-87
[18] H. Lian, F. Zhang, W. Lu. Randomized sketches for kernel CCA. Neural Networks 127 (2020) 29-37
[17] W. Zhao, F. Zhang, H. Lian, Debiasing and Distributed Estimation for High-Dimensional Quantile Regression. IEEE transactions on neural networks and learning systems 31 (2020) 2569 - 2577
[16] F. Zhang*, HKT. Ng, Y. Shi, Mis-Specification Analysis of Wiener Degradation Models by Using f-Divergence with Outliers. Reliability Engineering & System Safety, 195 (2020)106751
[15] F.D. Zhang*, Y.M. Shi. Geometry on the statistical manifold induced by the degradation model with soft failure data. Journal of Computational and Applied Mathematics, 363 (2020) 211-222.
[14]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Geometry on degradation models and mis-specification analysis by using α-divergence. Physica A 527(2020) 121343
[13] G. Wang, F.D. Zhang, H. Lian. Directional regression for functional data. Journal of Statistical Planning and Inference 204 (2020) 1-17
[12] F.D. Zhang, H.K.T. Ng, Y.M. Shi, R.B. Wang. Amari–Chentsov structure on the statistical manifold of models for accelerated life tests. TEST 28 (2019) 77-105.
[11] W Zhao, F Zhang, X Wang, R Li, H Lian, Principal varying coefficient estimator for high-dimensional models. Statistics 53 (2019) 1234-1250
[10] F.D. Zhang, H. Lian. Partially Functional Linear Regression with Quadratic Regularization. Inverse Problems 35 (2019) 105002.
[9] F.D. Zhang, Y.M. Shi, H.K.T. Ng, R.B. Wang. Information Geometry of Generalized Bayesian Prediction Using alpha-divergences as Loss Functions. IEEE Transactions on Information Theory 64 (2018) 1812 - 1824.
[8]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Information geometry on the curved q-exponential family with application to survival data analysis. Physica A: Statistical Mechanics and its Applications 512 (2018) 788--802.
[7] F.D. Zhang*, H.K.T. Ng, Y.M. Shi. Bayesian duality and risk analysis on the statistical manifold of exponential family with censored data. Journal of Computational and Applied Mathematics 342 (2018) 534–549.
[6]F.D. Zhang*, H.K.T. Ng, Y.M. Shi. On alternative q-Weibull and q-extreme value distributions: Properties and applications. Physica A: Statistical Mechanics and its Applications 490 (2018) 1171-1190.
[5]F.D. Zhang*, Y.M. Shi, C.F. Zhang. Geometry of an accelerated model with censored data. Journal of Computational and Applied Mathematics 317 (2017) 137–145.
[4]F.D. Zhang*, Y.M. Shi, R.B. Wang. Geometry of the q-exponential distribution with dependent competing risks and accelerated life testing. Physica A: Statistical Mechanics and its Applications 468 (2017) 552–565.
[3]F.D. Zhang*, Y.M. Shi, H.K.T. Ng, R.B. Wang. Tsallis statistics in reliability analysis: Theory and methods. The European Physical Journal Plus 131 (2016) 379.
[2]F.D. Zhang*, Y.M. Shi. Geometry of exponential family with competing risks and censored data. Physica A: Statistical Mechanics and its Applications 446 (2016) 234–245.
[1]F.D. Zhang. Periodic solutions for a Cauchy problem on time scales. Arab Journal of Mathematical Sciences. 21 (2015) 237–252.
期刊《 Communications in Statistics》副主编 （2025.9--至今）
期刊《 ACM Transactions on Probabilistic Machine Learning 》副主编 （2023.1--至今）
中国现场统计研究会贝叶斯统计分会常务理事
中国现场统计研究会旅游大数据分会常务理事
全国工业统计学教学研究会理事
国家自然科学基金项目评审专家
JMLR、IEEE TIT等20余本期刊通讯审稿人
期刊《 ACM Transactions on Probabilistic Machine Learning 》副主编 （2023.1--至今）
中国现场统计研究会贝叶斯统计分会常务理事
中国现场统计研究会旅游大数据分会常务理事
全国工业统计学教学研究会理事
国家自然科学基金项目评审专家
JMLR、IEEE TIT等20余本期刊通讯审稿人
本科生：数理统计、实变函数与泛函分析、统计软件与编程(with R)、数据挖掘与应用(with Python)
硕士生：高等数理统计I、大样本理论I、半监督学习、非参数统计
博士生：高等数理统计II、大样本理论II
硕士生：高等数理统计I、大样本理论I、半监督学习、非参数统计
博士生：高等数理统计II、大样本理论II