个人简介

史建清，南方科技大学统计与数据科学系教授。曾任英国纽卡斯尔大学（Newcastle University）统计学教授,英国国家艾伦图灵研究院图灵研究员。主要研究方向包括函数型数据分析，生物医学统计，缺失数据分析等。在国际学术刊物上发表高水平学术论文多篇，包括统计顶级期刊JRSSB，JASA，Biometrika和Biostatistics。曾任英国皇家统计协会《应用统计》副主编，英国纽卡斯尔大学云计算和大数据研究培训中心副主任。曾获邀任剑桥大学世界最顶级数学学院之一的牛顿学院访问研究员，获美国统计协会非参数统计分会年度最佳论文奖，2012年获英国 Wellcome trust Health Innovation Challenge Fund，共计210万英镑。2011年在著名统计学出版社Chapman & Hall 出版专著：Gaussian Process Regression Analysis for Functional Data。

教育背景

1993-1996   博士（统计学），香港中文大学

1987-1989   硕士（统计学），东南大学

1980-1984   学士（计算数学），南京大学

工作经历

2020.8至今  教授，南方科技大学统计与数据科学系

2018.10-2020.7  图灵研究员，英国国家艾伦图灵研究院

2015.7-2015.8  访问研究员，美国统计与应用数学科学研究所

2012.8-2020.7  教授，英国纽卡斯尔大学数学与统计系

2006.8-2012.8  高级讲师，英国纽卡斯尔大学数学与统计系

2008.2-2008.3  访问研究员，英国剑桥大学

2002-2006  讲师，英国纽卡斯尔大学数学与统计系

2000-2002  博士后，英国格拉斯哥大学

1998-2000  博士后，华威大学

1996-1998  助教，香港中文大学

1990-1993 and 1984-1987 助理讲师，东南大学

成员资格

Fellow of the Royal Statistical Society, ICSA member

学术任职经历

· Associate editor, Statistical and Probability letters (04/2016-)

· Associate editor, JRSSC (Applied Statistics, 01/2010-12/2013)

· Guest AE for JRSS discussion paper

· Mathematics prioritization panel member (2011,2012,2013)

· Member of APTS executive committee (10/2011-09/2012)

· Academic Editor. The Open Medical Informatics Journal.

· Academic Editor. British journal of Mathematics & Computer Sciences

科研项目

1. Handling missing data and time-varying confounding in causal inference for observational event history data. MRC council, PI (£270,510, Aug 2017 to Oct 2020).

2. Limbs alive – monitoring of upper limb rehabilitation and recovery after stroke through gaming, Wellcome trust grant on HICF (Health Innovation Challenge Fund) with PI Prof. J A Eyre (total of 2.1 million pounds, Jan. 2012—Jan.2015).

-- One three-year full-time postdoctoral RA and one three-year half-time RA are based in the school of Maths & Stats and are under my supervision, working on statistical modelling, data analysis and validation.

-- I am the leader of Work Package 1: Data Analysis and Validation (one of four work packages).

3. Predictive Dynamic Modelling for Next Generation Processing (PI, EPSRC Case Project, Jan. 2008-Jun. 2011, £60,864)

4. Nonparametric Methods for Curve Fitting and Prediction with Large Data-set (PI, EPSRC, Jan. 2005-June 2007, £121,691, sole investigator)

5. Predictive Dynamic Modelling for Next Generation Processing (PI, £83,500 from BP Oil international Ltd, Jan. 2008-Jun. 2011)

6. Predictive Analysis (BP Postgraduate Project, Sept. 2007-Aug. 2009, £21,000, Principal Supervisor)

7. Optimal Dynamic Control in Gaussian Process Regression Model (overseas collaborator), SG$ 50,420, PI: Dr. J. Li, NUS, Singapore

获奖经历

1. `Gait Analytics’ has been successfully selected to demo at AI UK 2020 (leading by Prof. Paul Watson, Dr J. Q Shi is the leader of mathematical modelling).

2. Guest Professor (from March 2013), Department of Mathematics, Southeast University (member of 985 group, a group of the top 39 Chinese universities).

3. Serradilla, J. Shi, J. Q., Cheng, Y., Morgan, G., Lambden, C. and Eyre, J. A. (2014). The best paper winner in the IEEE 3rd International Conference on Serious Games and Applications for Health, held on Rio de Janeiro, Brazil, May 14-16, 2014 ).

4. Choi, Shi and Wang (2011) won 2011 best paper award by the Journal of Nonparametric Statistics (JNSP) and American Statistical Association on Nonparametric Statistics. The most read article of JNSP (465 views since the website was launch in June 2011, updated 04/11/2013)

5. ESRC-funded Project R000237498: Methodology for Meta-analysis and the What Works Debate is graded as Outstanding: ‘High quality research making an important contribution to the development of the subject’. (J.B. Copas and J.Q. Shi, 2000).

6. Award of Advanced Science and Technology, Chinese National Education Committee, 1996. Topic: Nonlinear Statistical Models and Nonlinear Diagnostics Methods (B.C. Wei, J.Q. Shi, G.B. Lu, F.H. Wan and Y.Q. Hu).

7. Kam Ngan Stock Exchange Scholarship 1994/95. The Chinese University of Hong Kong.

受邀报告（部分）

1.  Invited speaker in Internal Statistical Conference in memory of Prof. SY Lee (18-19, Dec, Hong Kong, China)

2. Invited talk: 2019 IMS China Meeting - Institute of Mathematical Statistics (6-10, July 2019, Dalian, China)

3. Invited talk: ICSA China Conference (July 1-4, 2019, Tianjin, China)

4. Invited participant of MATRIX programme: functional data analysis and beyond. 2- 14, Dec, 2018

5. CMStatistics, 16-18 Dec 2017, London. Invited talk and organizer of one invited session.

6. July, ISI 2017. Invited talk, Morocco, Marrakech.

7. June 2016. invited talk, Symposium on Frontiers of Statistics and Data Sciences, Hong Kong.

8. March 2016. Invited short course on Functional Data Analysis, National School of Statistics and Information Analysis (ENSAI), Rennes, France

9. Jan. 2016. 2nd UCL Workshop on the Theory of Big Data, invited talk

10. Dec. 2015 CFE-CMStatistics 2015. Member of scientific program committee. Organizer and speaker of invited talk sessions.

11. Invited participant and speaker. BIRS Workshop. Frontiers in Functional Data Analysis, Canada, 2014 (local expense including accommodation is supported by the organizer).

12. ERCIM 2014. Invited talk in Bayesian semiparametric inference session: Nonlinear mixed-effects GP functional regression models with applications to motion data.

13. John Nelder Memorial Session: Functional data analysis for high dimensional and complex data. Joint meeting of the IASC Satellite conference and the 8th conference of the Asian Regional Section of the IASC. August 2013, Seoul, South Korea.

14. ASC Satellite Conference SRC SYMPOSIUM. Invited talk (2 hours): Gaussian process regression analysis for functional data, August 2103, Seoul, South Korea.

15. ICSA 2013 Applied Statistics Symposium / ISBS International Symposium on Biopharmaceutical Statistics Joint Meeting, invited talk: Bayesian GP regression analysis for large functional data. June 2013, Bethesda, Maryland, USA.

16. International Workshop on the Perspectives on High-dimensional Data Analysis III, invited talk: Nonlinear Curve Fitting and Clustering Using GPR Models May 2013,

17. Vancouver. Functional regression and classification (3 talks). April 2013, Shandong University. China.

18. Gaussian process regression analysis for functional data and applications. April 2013, Fudan University, China.

19. Model misspecification and bias analysis (4 talks). March 2013, Southeast University, China.

20. 12/09/2012, invited talk given in the workshop of “High dimensional and dependent functional data”, 10-12 September 2012, Bristol, UK (all expenses are paid by the organizer).

21. 28/08/2012, Invited talk given in the session “Novel Mixture Modelling and Likelihood methods in Modern Biomedical Applications”, IBC, Kobe, 26-30/08/2012.

22. 16/08/2012, invited talk: “Nonlinear Curve Fitting and Clustering Using GPR Models”, Korea university.

23. 09/04/2012, Open lecture for undergraduate students: “Statistics and Applications”, Nanjing University of Information Science and Technology.

24. 26/03--09/04/2012, invited 3 series talks: “Missing Data, Model Misspecification and Sensitivity Analysis”, Nanjing University of Information Science and Technology .

25. 27/03—08/04/2012, invited 4 series talks “Nonlinear Functional Data Analysis”, Southeast University.

专著

1. Shi, J. Q. and Choi. T. (2011). Gaussian Process Regression Analysis for Functional Data. Chapman & Hall, CRC.

2. Wei, B.C., Lu.,G.P. and Shi, J.Q. (1991). Introduction of Statistical Diagnostics.Southeast University Press, Nanjing. (in Chinese)

R语言

1. Cheng, Y. and Shi, J. Q. (2016). R-package: fLARS: functional variable selection using fLARS.

2. Shi, J. Q. and Cheng, Y. (2015). R-package GPFDA: Gaussian Process in Functional Data Analysis

技术报告

1. Konzen, E., Shi, J. Q. and Wang, Z. (2019). Modelling Function-valued Processes with Nonseparable Covariance Structure. arXiv:1903.09981.

2. Wang, Z., Noh, M., Lee, Y. and Shi, J. Q. (2019). A robust t-process estimation approach for functional regression model with batch data. arXiv:1707.02014

3. Wang, Z, Li, K. and Shi, J. Q. (2019). A robust estimation for the extended t-process regression models.

4. Yang, X, Shi, J. Q. and Fu, B. (2019) Identification of Causal Effects and Sensitivity Analysis with Confounders Missing Not at Random

5. Zeng, P., Tang, L., Shi, J. Q. and Kim, W-S (2019) Joint Curve Registration and Classification with Two-level Functional Models

6. Tang, L., Halloran, S., Shi, J. Q., Guan, Y., Cao, C. and Eyre, J. (2019). Evaluating upper limb function after stroke using the free-living accelerometer data

7. Yin, P., Zhu, R. and Shi, J. Q. (2019) Interpretable drivers of sensitivity analysis for non- ignorable missing covariate in linear regression models _

学术讨论

· Shi, J and Konzeri, E (2018) discussion on ‘The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages’ by D. Pigoli, P. Z. Hadjipantelis, J. S. Coleman and J. A. D. Aston, Appl. Statist. 67.

代表性论文

1.      Tang, L., Halloran, S., Shi, J. Q., Guan, Y., Cao, C. and Eyre, J. (2020). Evaluating upper limb function after stroke using the free-living accelerometer data. Statistical Methods in Medical Research (accepted.)

2.      Cheng, Y., Shi, J. Q. and Eyre, J. (2020) Nonlinear Mixed-effects Scalar-on-function Models and Variable Selection for Kinematic Upper Limb Movement Data. arXiv:1605.06779 Statistics and Computing, 30, 129-140. Available on line now. https://link.springer.com/article/10.1007%2Fs11222-019-09871-3

3.      Wang, Z., Ding, H, Chen, Z and Shi, J. Q. (2020). Nonparametric Random Effect Functional Regression Model with Gaussian process priors. Statistica Sinica.  Available on line now: http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2018-0296_na.pdf

4.      Sofro, A., Shi, J. Q. and Cao, C. (2020) Regression Analysis for Multivariate Process Data of Counts using Convolved Gaussian Processes. Journal of Statistical Planning and Inference. 206 , 57-74 (online version is available now).

5.      Chunzheng Cao, Ziyue Wang, Jian Qing SHI and Yunjie Chen (2020) Robust Task Learning Based on Nonlinear Regression with Mixtures of Student-t Distributions. IEEE Access.

6.      Coates, L, Shi, J. Q., Rochester, L, Del Din, S. and Pantall, A. (2020). Entropy of Real-World Gait in Parkinson’s Disease Determined from Wearable Sensors as a Digital Marker of Altered Ambulatory Behavior. Sensors, 20(9). 2631; https://doi.org/10.3390/s20092631.

7.      Zeng, P., Shi, J. Q. and Kim, W-S (2019) Simultaneous registration and clustering for multidimensional functional data. of Computational and Graphical Statistics. 28, 943-953. arXiv:1711.04761. Available on line now https://www.tandfonline.com/doi/full/10.1080/10618600.2019.1607744.

8.      Yin, P. and Shi, J. Q. (2019) Simulation-based Sensitivity Analysis for Non-ignorable Missing Data.Statistical Methods in Medical Research, 28(1) 289–308. http://journals.sagepub.com/doi/pdf/10.1177/0962280217722382. arXiv:1501.05788.

9.      Xu, P, Lee, Y., Shi, J.Q. and Eyre, J. (2019) Automatic Detection of Significant Areas for Functional Data with Directional Error Control. arXiv: 08164. Statistics in medicine, 38, 376-397. https://doi.org/10.1002/sim.7968. Available online now. https://onlinelibrary.wiley.com/doi/full/10.1002/sim.7968

10.    Rehman, R. Z. U., Del Din, S., Guan, Y., Yarnall, A. J., Shi, J. Q. and Rochester, L. (2019). Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach. Scientific Reports. https://www.nature.com/articles/s41598-019-53656-7

11.    Rehman, R. Z. U., Del Din, S., Shi, J. Q., Galna, B., Lord, S., Yarnall, A. J., Guan, Y. and Rochester, L. (2019). Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease. Sensors. https://www.mdpi.com/1424-8220/19/24/5363

12.    Rehman, R. Z. U., Buckley, B., Mico-Amigo, M. E., Kirk, C., Dunne-Willows, M., Mazza, C., Shi, J. Q., Alcock, L., Rochester, L. and Del Din, S. (2019).  Accelerometry-based digital gait characteristics for classification of Parkinson’s disease: what counts? IEEE Open Journal of Engineering in Medicine and Biology (accepted).

13.    Zhanfeng Wang, Kai Lia, Jian Qing Shi (2019). A robust estimation for the extended t-process regression model. Statistics and Probability Letters. Available on line now: https://www.sciencedirect.com/science/article/pii/S016771521930272X?dgcid=coauthor.

14.    Halloran, S., Tang, L., Guan, Y., Shi, J. Q., and Eyre, J. (2019). Remote monitoring of stroke patients' rehabilitation using wearable accelerometers. In Proceedings of the 2019 ACM International Symposium on Wearable Computers. ACM.

15.    Shi, J. Q. (2018). How Do Statisticians Analyse Big Data – Our story. Statistics and Probability Letters. 136,130-133.  https://doi.org/10.1016/j.spl.2018.02.043.

16.    Cao, C., Chen, M., Wang, Y and Shi, J. Q. (2018) Heteroscedastic replicated measurement error models under asymmetric heavy-tailed distributions. Computational Statistics, 33, 319-338.

17.    Cao, C., Wang, Y., Shi, J. Q. and Lin, J. (2018) Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions. Computational Economics,  33, 319-338.https://doi.org/10.1007/s10614-017-9702-8.

18.    Cao, C., Shi, J. Q. and Lee, Y. (2018). Robust functional regression model for population-average and subject-specific inferences. Statistical Methods in Medical Research, 27, 3236-3254. http://journals.sagepub.com/doi/10.1177/0962280217695346.

19.    Kim, W-S, Zeng, P., Shi, J. Q., Lee, Y. and Paik, N-J. (2017)Automatic tracking of hyoid bone motion from videofluoroscopic swallowing study with automatic smoothing and segmentation. PLOS ONE, https://doi.org/10.1371/journal.pone.0188684.

20.    Wang, Z., Shi, J. Q. and Lee, Y. (2017) Extended T-process Regression Models. Journal of Statistical Planning and Inference, 189,38-60. arXiv:1705.05125.

21.    Cao, C., Lin, J, Shi, J. Q., Wang, W. and Zhang, X. (2015) Multivariate measurement error models for replicated data under heavy-tailed distributions Journal of Chemometrics, 29, 457-466.

22.    Lu, H., Yin, P., Yue, R. X. and Shi, J. Q. (2015) Robust confidence intervals for trend estimation in meta-analysis with publication bias. Journal of Applied Statistics, 42, 2715-2733.

23.    Wang, B. and Shi, J. Q. (2014). Generalized Gaussian Process Regression Model for non-Gaussian Functional Data. Journal of American Statistical Association, 109, 1123-1133.

24.    Serradilla, J. Shi, J. Q., Cheng, Y., Morgan, G., Lambden, C. and Eyre, J. A. (2014). Automatic Assessment of Upper Limb Function During Play of the Action Video Game, Circus Challenge: Validity and Sensitivity to Change. SEGAH 2014. (The best paper winner in the IEEE 3rd International Conference on Serious Games and Applications for Health, held on Rio de Janeiro, Brazil, May 14-16, 2014 ).

25.    Scott M, Blewitt W, Ushaw G, Shi JQ, Morgan G, Eyre J. Automating assessment in video game teletherapy: Data cutting.In: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). 2014, Orlando, Florida: IEEE. 9-16.

26.    Cao, C. Z., Lin, J. G. and Shi, J. Q. (2014). Diagnostics on nonlinear model with scale mixtures of skew-normal and first-order autoregressive errors. Statistics. ,48, 1033-1047.

27.    Shi, J.Q, Cheng, Y., Serradilla, J., Morgan, G., Lambden, C., Ford, G.A., Price, C., Rodgers, H, Cassidy, T., Rochester, L. and Eyre, J.A. (2013). Evaluating Functional Ability of Upper Limbs after Stroke Using Video Game Data. Imamura et al. (Eds.): BHI 2013, LNAI 8211, pp. 181–192. Springer.

28.    Lin, N. X., Shi, J. Q. and Henderson, R. (2012). Doubly mis-specified models. Biometrika. 99, 285-298.

29.    Shi, J. Q., Wang, B., Will, E. J. and West. R. M. (2012). Mixed-effects GPFR models with application to dose-response curve prediction. Statistics in Medicine. 31, 3165-77.