POSITION/TITLE

Lab Director of Medical Big Data Laboratory

EDUCATION BACKGROUND

Ph.D. Computing Science, University of Alberta, 2006

M.S. Computing Science, University of Alberta, 2001

B.S. Information System, Renmin University, 1994

RESEARCH FIELD

Meta-analysis, Data Mining, Large-scale Genomic Data Analysis, High Performance Computing, Evidence-based Medicine

EMAIL

wanxiang@sribd.cn

BIOGRAPHY

Professor Wan received his BA in Information System from Renmin University and his MA and Ph.D. in Computing Science from University of Alberta. Professor Wan was a research assistant professor at Hong Kong Baptist University from 2012 to 2018. And he is now working concurrently as a research scientist at Shenzhen Research Institute of Big Data since 2018.

Professor Wan has been mainly working on meta-analysis and statistical learning, particularly in the field of large-scale genomic data analysis. He has published more than 40 papers in many top-tier journals, including Nature Genetics, American Journal of Human Genetics, BMC Genetics, Bioinformatics, BMC Bioinformatics, Neuro-informatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. Professor Wan is currently the director of Medical Big Data Lab. The main research goal of this lab is to integrate electronic medical records, medical imaging, health check reports and multi-omics data to help the pre-diagnosis and the personalized treatment.

ACADEMIC PUBLICATIONS

1. Can Yang, Xiang Wan*, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information, Bioinformatics 35(10) 1644-1652, 2019. (co-first author)

2. Jingsi Ming, Mingwei Dai, Mingxuan Cai, Xiang Wan, Jin Liu, Can Yang. LSMM: a statistical approach to integrating functional annotations with genome-wide association studies. Bioinformatics, 2019, 34 (16), 2788-2796.

3. Mingwei Dai, Xiang Wan, Heng Peng, Yao Wang, Yue Liu, Jin Liu, Zongben Xu, Can Yang. Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics, 2019, Bioinformatics 35 (10), 1729-1736.

4. Lili Yue, Gaorong Li, Heng Lian, Xiang Wan. Regression adjustment for treatment effect with multicollinearity in high dimensions. Computational Statistics & Data Analysis, 2019, 134:17-35.

5. Guanying Wu, Xiang Wan*, Baohua Xu. A new estimation of protein-level false discovery rate. BMC Genomics, 2018, 2018 Aug 13;19 (Suppl 6):567. doi: 10.1186/s12864-018-4923-3. (co-first author)

6. Dehui Luo, Xiang Wan*, Jiming Liu, Tiejun Tong,Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range,Statistical methods in medical research, 2018, 27 (6), 1785-1805. (co-correspondence author)

7. Yan Zhou, Xiang Wan*, Baoxue Zhang, Tiejun Tong, Classifying next-generation sequencing data using a zero-inflated Poisson model, Bioinformatics,2018, 15;34(8):1329-1335. (co-correspondence author)

8. Jin Liu, Xiang Wan*, Chaolong Wang, Chao Yang, Xiaowen Zhou, Can Yang, LLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS,Bioinformatics, 2017, 33(24):3878-3886. (co-first author)

9. Mingwei Dai, Jingsi Ming, Mingxuan Cai, Jin Liu, Can Yang, Xiang Wan*, ZongbenXue, IGESS: A Statistical Approach to Integrating Individual-Level Genotype Data and Summary Statistics in Genome-Wide Association Studies, Bioinformatics,2017,33(18): 2882-2889. (co-correspondence author)

10. Bin Zhang, Xiang Wan*, Yuhao Dong, Dehui Luo, Jing Liu, Long Liang, Wenbo Chen, Xiaoning Luo, Xiaokai Mo, Lu Zhang, Wenhui Huang, Shufang Pei, Fusheng Ouyang, Baoliang Guo, Changhong Liang, Zhouyang Lian, Shuixing Zhang, Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children, Scientific Report, 2017 Jul 14;7(1):5368. (co-first author)

11. Yan Zhou, Baoxue Zhou, Tiejun Tong, Xiang Wan*. GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data, Journal of Computational Biology, 2017,24 (11), 1099-1111. (correspondence author)

12. Kai Dong, Hongyu Zhao, Tiejun Tong, Xiang Wan*. NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data,BMC Bioinformatics,2016, 17(1):369. (co-correspondence author)

13. Ruixing Ming, Jiming Liu, William K.W. Cheung, Xiang Wan*. Stochastic Modeling of Infectious Diseases for Heterogeneous Populations. BMC Infectious Disease, 2016,5(1):107. (correspondence author)

14. Jin Liu, Xiang Wan, Shuangge Ma, Can Yang. EPS: An empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes, Bioinformatics, 2016, 32(12):1856-64

15. Ben Teng, Can Yang, Jiming Liu, Zhipeng Cai, Xiang Wan*. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 13(3): 557-664. (correspondence author)

16. Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC medical research methodology, 14 (1), 135, 2014.

17. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Learning to improve medical decision making from imbalanced data without a priori cost. BMC medical informatics and decision making, 14 (1), 1, 2014.

18. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Inferring Epidemic Network Topology from Surveillance Data. Plos ONE, 9(6):e100661, 2014.

19. Xiaowei Zhou, Jiming Liu, Xiang Wan*, Weichuan Yu. Piecewise-constant and low-rank approximation for identification of recurrent copy number variations. Bioinformatics, 30(14):1943-1949, 2014. (correspondence author)

20. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 230-235, 2013.

21. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (1): 207-212, 2013.

22. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. The complete compositional epistasis detection in genome-wide association studies, BMC Genetics, 14(1):7, 2013.

23. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 207-212, 2013.

24. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization, IEEE/ACM Transactions on Computational Biology and Bioinformatics , 10(1), 207-212, 2013.

25. Xiang Wan, Can Yang, Weichuan Yu. Comments on 'An empirical comparison of several recent epistatic interaction detection methods', Bioinformatics, 28(1):145-146, 2012.

26. Geng Cui, Man Leung Wong, Xiang Wan. Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM. Journal of Management Information System, 29(1):341-374, 2012.

27. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. A new two-locus disease association pattern identified in genome-wide association studies, BMC Bioinformatics, 12:156, 2011. (co-first author)

28. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies, BMC Bioinformatics, 12(Suppl 1):S26, 2011. (co-first author)

29. Lingxing Yung, Can Yang, Xiang Wan, Weichuan Yu. GBOOST: A GPU-Based Tool for Detecting Gene-Gene Interactions in Genome-Wide Case Control Studies, Bioinformatics, 28(1):145-146, 2011

30. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L.S. Tang, Weichuan Yu. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, American Journal of Human Genetics, 87(3), 325-340, 2010.

31. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Detecting two-locus associations allowing for interactions in genome-wide association studies, Bioinformatics, 26(20): 2517-2525, 2010.

32. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Predictive rule inference for epistatic interaction detection in genome-wide association study, Bioinformatics, 26(1):30-37, 2010.

33. Can Yang, Xiang Wan, Qiang Yang, Hong Xue, Weichuan Yu. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group lasso, BMC Bioinformatics, 11(Suppl 1):S18, 2010.

34. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome-wide association study, BMC Bioinformatics, 10:13, 2009.

35. Kimberly L Stark, Bin Xu, Anindya Bagchi, Wen-Sung Lai, Hui Liu, Ruby Hsu, Xiang Wan, Paul Pavlidis, Alea A Mills, Maria Karayiorgou1 & Joseph A Gogos Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model, Nature Genetics, 40, 751 – 760, 2008.

36. Can Yang, Zengyou He, Xiang Wan, Weichuan Yu, Qiang Yang, Hong Xue, SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies, Bioinformatics, doi:10.1093, 2008.

37. Xiang Wan, Paul Pavlidis. Sharing and reusing gene expression profiling data in neuroscience, Neuroinformatics, 5(3), 161-175, 2007.

38. Xiang Wan, Guohui Lin. CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(3), 336-348, 2007.

39. Xiang Wan, Guohui Lin. A Graph-Based Automated NMR Backbone Resonance Sequential Assignment, Journal of Bioinformatics and Computational Biology, 5(2), 313-333, 2007.

40. Guohui Lin, Xiang Wan, Theodos Tegos, Yingshu Li. Statistical Evaluation of NMR Backbone Resonance Assignment, International Journal of Bioinformatics Research and Applications, 2(2), 147-160, 2006.

41. Xiang Wan, Theodos Tegos, Guohui Lin. Histogram-based Scoring Schemes for Protein NMR Resonance Assignment, Journal of Bioinformatics and Computational Biology, 2.

 

POSITION/TITLE

Lab Director of Medical Big Data Laboratory

EDUCATION BACKGROUND

Ph.D. Computing Science, University of Alberta, 2006

M.S. Computing Science, University of Alberta, 2001

B.S. Information System, Renmin University, 1994

RESEARCH FIELD

Meta-analysis, Data Mining, Large-scale Genomic Data Analysis, High Performance Computing, Evidence-based Medicine

EMAIL

wanxiang@sribd.cn

BIOGRAPHY

Professor Wan received his BA in Information System from Renmin University and his MA and Ph.D. in Computing Science from University of Alberta. Professor Wan was a research assistant professor at Hong Kong Baptist University from 2012 to 2018. And he is now working concurrently as a research scientist at Shenzhen Research Institute of Big Data since 2018.

Professor Wan has been mainly working on meta-analysis and statistical learning, particularly in the field of large-scale genomic data analysis. He has published more than 40 papers in many top-tier journals, including Nature Genetics, American Journal of Human Genetics, BMC Genetics, Bioinformatics, BMC Bioinformatics, Neuro-informatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. Professor Wan is currently the director of Medical Big Data Lab. The main research goal of this lab is to integrate electronic medical records, medical imaging, health check reports and multi-omics data to help the pre-diagnosis and the personalized treatment.

ACADEMIC PUBLICATIONS

1. Can Yang, Xiang Wan*, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information, Bioinformatics 35(10) 1644-1652, 2019. (co-first author)

2. Jingsi Ming, Mingwei Dai, Mingxuan Cai, Xiang Wan, Jin Liu, Can Yang. LSMM: a statistical approach to integrating functional annotations with genome-wide association studies. Bioinformatics, 2019, 34 (16), 2788-2796.

3. Mingwei Dai, Xiang Wan, Heng Peng, Yao Wang, Yue Liu, Jin Liu, Zongben Xu, Can Yang. Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics, 2019, Bioinformatics 35 (10), 1729-1736.

4. Lili Yue, Gaorong Li, Heng Lian, Xiang Wan. Regression adjustment for treatment effect with multicollinearity in high dimensions. Computational Statistics & Data Analysis, 2019, 134:17-35.

5. Guanying Wu, Xiang Wan*, Baohua Xu. A new estimation of protein-level false discovery rate. BMC Genomics, 2018, 2018 Aug 13;19 (Suppl 6):567. doi: 10.1186/s12864-018-4923-3. (co-first author)

6. Dehui Luo, Xiang Wan*, Jiming Liu, Tiejun Tong,Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range,Statistical methods in medical research, 2018, 27 (6), 1785-1805. (co-correspondence author)

7. Yan Zhou, Xiang Wan*, Baoxue Zhang, Tiejun Tong, Classifying next-generation sequencing data using a zero-inflated Poisson model, Bioinformatics,2018, 15;34(8):1329-1335. (co-correspondence author)

8. Jin Liu, Xiang Wan*, Chaolong Wang, Chao Yang, Xiaowen Zhou, Can Yang, LLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS,Bioinformatics, 2017, 33(24):3878-3886. (co-first author)

9. Mingwei Dai, Jingsi Ming, Mingxuan Cai, Jin Liu, Can Yang, Xiang Wan*, ZongbenXue, IGESS: A Statistical Approach to Integrating Individual-Level Genotype Data and Summary Statistics in Genome-Wide Association Studies, Bioinformatics,2017,33(18): 2882-2889. (co-correspondence author)

10. Bin Zhang, Xiang Wan*, Yuhao Dong, Dehui Luo, Jing Liu, Long Liang, Wenbo Chen, Xiaoning Luo, Xiaokai Mo, Lu Zhang, Wenhui Huang, Shufang Pei, Fusheng Ouyang, Baoliang Guo, Changhong Liang, Zhouyang Lian, Shuixing Zhang, Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children, Scientific Report, 2017 Jul 14;7(1):5368. (co-first author)

11. Yan Zhou, Baoxue Zhou, Tiejun Tong, Xiang Wan*. GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data, Journal of Computational Biology, 2017,24 (11), 1099-1111. (correspondence author)

12. Kai Dong, Hongyu Zhao, Tiejun Tong, Xiang Wan*. NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data,BMC Bioinformatics,2016, 17(1):369. (co-correspondence author)

13. Ruixing Ming, Jiming Liu, William K.W. Cheung, Xiang Wan*. Stochastic Modeling of Infectious Diseases for Heterogeneous Populations. BMC Infectious Disease, 2016,5(1):107. (correspondence author)

14. Jin Liu, Xiang Wan, Shuangge Ma, Can Yang. EPS: An empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes, Bioinformatics, 2016, 32(12):1856-64

15. Ben Teng, Can Yang, Jiming Liu, Zhipeng Cai, Xiang Wan*. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 13(3): 557-664. (correspondence author)

16. Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC medical research methodology, 14 (1), 135, 2014.

17. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Learning to improve medical decision making from imbalanced data without a priori cost. BMC medical informatics and decision making, 14 (1), 1, 2014.

18. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Inferring Epidemic Network Topology from Surveillance Data. Plos ONE, 9(6):e100661, 2014.

19. Xiaowei Zhou, Jiming Liu, Xiang Wan*, Weichuan Yu. Piecewise-constant and low-rank approximation for identification of recurrent copy number variations. Bioinformatics, 30(14):1943-1949, 2014. (correspondence author)

20. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 230-235, 2013.

21. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (1): 207-212, 2013.

22. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. The complete compositional epistasis detection in genome-wide association studies, BMC Genetics, 14(1):7, 2013.

23. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 207-212, 2013.

24. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization, IEEE/ACM Transactions on Computational Biology and Bioinformatics , 10(1), 207-212, 2013.

25. Xiang Wan, Can Yang, Weichuan Yu. Comments on 'An empirical comparison of several recent epistatic interaction detection methods', Bioinformatics, 28(1):145-146, 2012.

26. Geng Cui, Man Leung Wong, Xiang Wan. Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM. Journal of Management Information System, 29(1):341-374, 2012.

27. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. A new two-locus disease association pattern identified in genome-wide association studies, BMC Bioinformatics, 12:156, 2011. (co-first author)

28. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies, BMC Bioinformatics, 12(Suppl 1):S26, 2011. (co-first author)

29. Lingxing Yung, Can Yang, Xiang Wan, Weichuan Yu. GBOOST: A GPU-Based Tool for Detecting Gene-Gene Interactions in Genome-Wide Case Control Studies, Bioinformatics, 28(1):145-146, 2011

30. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L.S. Tang, Weichuan Yu. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, American Journal of Human Genetics, 87(3), 325-340, 2010.

31. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Detecting two-locus associations allowing for interactions in genome-wide association studies, Bioinformatics, 26(20): 2517-2525, 2010.

32. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Predictive rule inference for epistatic interaction detection in genome-wide association study, Bioinformatics, 26(1):30-37, 2010.

33. Can Yang, Xiang Wan, Qiang Yang, Hong Xue, Weichuan Yu. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group lasso, BMC Bioinformatics, 11(Suppl 1):S18, 2010.

34. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome-wide association study, BMC Bioinformatics, 10:13, 2009.

35. Kimberly L Stark, Bin Xu, Anindya Bagchi, Wen-Sung Lai, Hui Liu, Ruby Hsu, Xiang Wan, Paul Pavlidis, Alea A Mills, Maria Karayiorgou1 & Joseph A Gogos Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model, Nature Genetics, 40, 751 – 760, 2008.

36. Can Yang, Zengyou He, Xiang Wan, Weichuan Yu, Qiang Yang, Hong Xue, SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies, Bioinformatics, doi:10.1093, 2008.

37. Xiang Wan, Paul Pavlidis. Sharing and reusing gene expression profiling data in neuroscience, Neuroinformatics, 5(3), 161-175, 2007.

38. Xiang Wan, Guohui Lin. CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(3), 336-348, 2007.

39. Xiang Wan, Guohui Lin. A Graph-Based Automated NMR Backbone Resonance Sequential Assignment, Journal of Bioinformatics and Computational Biology, 5(2), 313-333, 2007.

40. Guohui Lin, Xiang Wan, Theodos Tegos, Yingshu Li. Statistical Evaluation of NMR Backbone Resonance Assignment, International Journal of Bioinformatics Research and Applications, 2(2), 147-160, 2006.

41. Xiang Wan, Theodos Tegos, Guohui Lin. Histogram-based Scoring Schemes for Protein NMR Resonance Assignment, Journal of Bioinformatics and Computational Biology, 2.

 

POSITION/TITLE

Lab Director of Medical Big Data Laboratory

EDUCATION BACKGROUND

Ph.D. Computing Science, University of Alberta, 2006

M.S. Computing Science, University of Alberta, 2001

B.S. Information System, Renmin University, 1994

RESEARCH FIELD

Meta-analysis, Data Mining, Large-scale Genomic Data Analysis, High Performance Computing, Evidence-based Medicine

EMAIL

wanxiang@sribd.cn

BIOGRAPHY

Professor Wan received his BA in Information System from Renmin University and his MA and Ph.D. in Computing Science from University of Alberta. Professor Wan was a research assistant professor at Hong Kong Baptist University from 2012 to 2018. And he is now working concurrently as a research scientist at Shenzhen Research Institute of Big Data since 2018.

Professor Wan has been mainly working on meta-analysis and statistical learning, particularly in the field of large-scale genomic data analysis. He has published more than 40 papers in many top-tier journals, including Nature Genetics, American Journal of Human Genetics, BMC Genetics, Bioinformatics, BMC Bioinformatics, Neuro-informatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. Professor Wan is currently the director of Medical Big Data Lab. The main research goal of this lab is to integrate electronic medical records, medical imaging, health check reports and multi-omics data to help the pre-diagnosis and the personalized treatment.

ACADEMIC PUBLICATIONS

1. Can Yang, Xiang Wan*, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information, Bioinformatics 35(10) 1644-1652, 2019. (co-first author)

2. Jingsi Ming, Mingwei Dai, Mingxuan Cai, Xiang Wan, Jin Liu, Can Yang. LSMM: a statistical approach to integrating functional annotations with genome-wide association studies. Bioinformatics, 2019, 34 (16), 2788-2796.

3. Mingwei Dai, Xiang Wan, Heng Peng, Yao Wang, Yue Liu, Jin Liu, Zongben Xu, Can Yang. Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics, 2019, Bioinformatics 35 (10), 1729-1736.

4. Lili Yue, Gaorong Li, Heng Lian, Xiang Wan. Regression adjustment for treatment effect with multicollinearity in high dimensions. Computational Statistics & Data Analysis, 2019, 134:17-35.

5. Guanying Wu, Xiang Wan*, Baohua Xu. A new estimation of protein-level false discovery rate. BMC Genomics, 2018, 2018 Aug 13;19 (Suppl 6):567. doi: 10.1186/s12864-018-4923-3. (co-first author)

6. Dehui Luo, Xiang Wan*, Jiming Liu, Tiejun Tong,Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range,Statistical methods in medical research, 2018, 27 (6), 1785-1805. (co-correspondence author)

7. Yan Zhou, Xiang Wan*, Baoxue Zhang, Tiejun Tong, Classifying next-generation sequencing data using a zero-inflated Poisson model, Bioinformatics,2018, 15;34(8):1329-1335. (co-correspondence author)

8. Jin Liu, Xiang Wan*, Chaolong Wang, Chao Yang, Xiaowen Zhou, Can Yang, LLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS,Bioinformatics, 2017, 33(24):3878-3886. (co-first author)

9. Mingwei Dai, Jingsi Ming, Mingxuan Cai, Jin Liu, Can Yang, Xiang Wan*, ZongbenXue, IGESS: A Statistical Approach to Integrating Individual-Level Genotype Data and Summary Statistics in Genome-Wide Association Studies, Bioinformatics,2017,33(18): 2882-2889. (co-correspondence author)

10. Bin Zhang, Xiang Wan*, Yuhao Dong, Dehui Luo, Jing Liu, Long Liang, Wenbo Chen, Xiaoning Luo, Xiaokai Mo, Lu Zhang, Wenhui Huang, Shufang Pei, Fusheng Ouyang, Baoliang Guo, Changhong Liang, Zhouyang Lian, Shuixing Zhang, Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children, Scientific Report, 2017 Jul 14;7(1):5368. (co-first author)

11. Yan Zhou, Baoxue Zhou, Tiejun Tong, Xiang Wan*. GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data, Journal of Computational Biology, 2017,24 (11), 1099-1111. (correspondence author)

12. Kai Dong, Hongyu Zhao, Tiejun Tong, Xiang Wan*. NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data,BMC Bioinformatics,2016, 17(1):369. (co-correspondence author)

13. Ruixing Ming, Jiming Liu, William K.W. Cheung, Xiang Wan*. Stochastic Modeling of Infectious Diseases for Heterogeneous Populations. BMC Infectious Disease, 2016,5(1):107. (correspondence author)

14. Jin Liu, Xiang Wan, Shuangge Ma, Can Yang. EPS: An empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes, Bioinformatics, 2016, 32(12):1856-64

15. Ben Teng, Can Yang, Jiming Liu, Zhipeng Cai, Xiang Wan*. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 13(3): 557-664. (correspondence author)

16. Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC medical research methodology, 14 (1), 135, 2014.

17. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Learning to improve medical decision making from imbalanced data without a priori cost. BMC medical informatics and decision making, 14 (1), 1, 2014.

18. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Inferring Epidemic Network Topology from Surveillance Data. Plos ONE, 9(6):e100661, 2014.

19. Xiaowei Zhou, Jiming Liu, Xiang Wan*, Weichuan Yu. Piecewise-constant and low-rank approximation for identification of recurrent copy number variations. Bioinformatics, 30(14):1943-1949, 2014. (correspondence author)

20. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 230-235, 2013.

21. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (1): 207-212, 2013.

22. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. The complete compositional epistasis detection in genome-wide association studies, BMC Genetics, 14(1):7, 2013.

23. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 207-212, 2013.

24. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization, IEEE/ACM Transactions on Computational Biology and Bioinformatics , 10(1), 207-212, 2013.

25. Xiang Wan, Can Yang, Weichuan Yu. Comments on 'An empirical comparison of several recent epistatic interaction detection methods', Bioinformatics, 28(1):145-146, 2012.

26. Geng Cui, Man Leung Wong, Xiang Wan. Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM. Journal of Management Information System, 29(1):341-374, 2012.

27. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. A new two-locus disease association pattern identified in genome-wide association studies, BMC Bioinformatics, 12:156, 2011. (co-first author)

28. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies, BMC Bioinformatics, 12(Suppl 1):S26, 2011. (co-first author)

29. Lingxing Yung, Can Yang, Xiang Wan, Weichuan Yu. GBOOST: A GPU-Based Tool for Detecting Gene-Gene Interactions in Genome-Wide Case Control Studies, Bioinformatics, 28(1):145-146, 2011

30. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L.S. Tang, Weichuan Yu. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, American Journal of Human Genetics, 87(3), 325-340, 2010.

31. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Detecting two-locus associations allowing for interactions in genome-wide association studies, Bioinformatics, 26(20): 2517-2525, 2010.

32. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Predictive rule inference for epistatic interaction detection in genome-wide association study, Bioinformatics, 26(1):30-37, 2010.

33. Can Yang, Xiang Wan, Qiang Yang, Hong Xue, Weichuan Yu. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group lasso, BMC Bioinformatics, 11(Suppl 1):S18, 2010.

34. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome-wide association study, BMC Bioinformatics, 10:13, 2009.

35. Kimberly L Stark, Bin Xu, Anindya Bagchi, Wen-Sung Lai, Hui Liu, Ruby Hsu, Xiang Wan, Paul Pavlidis, Alea A Mills, Maria Karayiorgou1 & Joseph A Gogos Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model, Nature Genetics, 40, 751 – 760, 2008.

36. Can Yang, Zengyou He, Xiang Wan, Weichuan Yu, Qiang Yang, Hong Xue, SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies, Bioinformatics, doi:10.1093, 2008.

37. Xiang Wan, Paul Pavlidis. Sharing and reusing gene expression profiling data in neuroscience, Neuroinformatics, 5(3), 161-175, 2007.

38. Xiang Wan, Guohui Lin. CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(3), 336-348, 2007.

39. Xiang Wan, Guohui Lin. A Graph-Based Automated NMR Backbone Resonance Sequential Assignment, Journal of Bioinformatics and Computational Biology, 5(2), 313-333, 2007.

40. Guohui Lin, Xiang Wan, Theodos Tegos, Yingshu Li. Statistical Evaluation of NMR Backbone Resonance Assignment, International Journal of Bioinformatics Research and Applications, 2(2), 147-160, 2006.

41. Xiang Wan, Theodos Tegos, Guohui Lin. Histogram-based Scoring Schemes for Protein NMR Resonance Assignment, Journal of Bioinformatics and Computational Biology, 2.

 

POSITION/TITLE

Lab Director of Medical Big Data Laboratory

EDUCATION BACKGROUND

Ph.D. Computing Science, University of Alberta, 2006

M.S. Computing Science, University of Alberta, 2001

B.S. Information System, Renmin University, 1994

RESEARCH FIELD

Meta-analysis, Data Mining, Large-scale Genomic Data Analysis, High Performance Computing, Evidence-based Medicine

EMAIL

wanxiang@sribd.cn

BIOGRAPHY

Professor Wan received his BA in Information System from Renmin University and his MA and Ph.D. in Computing Science from University of Alberta. Professor Wan was a research assistant professor at Hong Kong Baptist University from 2012 to 2018. And he is now working concurrently as a research scientist at Shenzhen Research Institute of Big Data since 2018.

Professor Wan has been mainly working on meta-analysis and statistical learning, particularly in the field of large-scale genomic data analysis. He has published more than 40 papers in many top-tier journals, including Nature Genetics, American Journal of Human Genetics, BMC Genetics, Bioinformatics, BMC Bioinformatics, Neuro-informatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. Professor Wan is currently the director of Medical Big Data Lab. The main research goal of this lab is to integrate electronic medical records, medical imaging, health check reports and multi-omics data to help the pre-diagnosis and the personalized treatment.

ACADEMIC PUBLICATIONS

1. Can Yang, Xiang Wan*, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu. CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information, Bioinformatics 35(10) 1644-1652, 2019. (co-first author)

2. Jingsi Ming, Mingwei Dai, Mingxuan Cai, Xiang Wan, Jin Liu, Can Yang. LSMM: a statistical approach to integrating functional annotations with genome-wide association studies. Bioinformatics, 2019, 34 (16), 2788-2796.

3. Mingwei Dai, Xiang Wan, Heng Peng, Yao Wang, Yue Liu, Jin Liu, Zongben Xu, Can Yang. Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy. Bioinformatics, 2019, Bioinformatics 35 (10), 1729-1736.

4. Lili Yue, Gaorong Li, Heng Lian, Xiang Wan. Regression adjustment for treatment effect with multicollinearity in high dimensions. Computational Statistics & Data Analysis, 2019, 134:17-35.

5. Guanying Wu, Xiang Wan*, Baohua Xu. A new estimation of protein-level false discovery rate. BMC Genomics, 2018, 2018 Aug 13;19 (Suppl 6):567. doi: 10.1186/s12864-018-4923-3. (co-first author)

6. Dehui Luo, Xiang Wan*, Jiming Liu, Tiejun Tong,Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range,Statistical methods in medical research, 2018, 27 (6), 1785-1805. (co-correspondence author)

7. Yan Zhou, Xiang Wan*, Baoxue Zhang, Tiejun Tong, Classifying next-generation sequencing data using a zero-inflated Poisson model, Bioinformatics,2018, 15;34(8):1329-1335. (co-correspondence author)

8. Jin Liu, Xiang Wan*, Chaolong Wang, Chao Yang, Xiaowen Zhou, Can Yang, LLR: A latent low-rank approach to colocalizing genetic risk variants in multiple GWAS,Bioinformatics, 2017, 33(24):3878-3886. (co-first author)

9. Mingwei Dai, Jingsi Ming, Mingxuan Cai, Jin Liu, Can Yang, Xiang Wan*, ZongbenXue, IGESS: A Statistical Approach to Integrating Individual-Level Genotype Data and Summary Statistics in Genome-Wide Association Studies, Bioinformatics,2017,33(18): 2882-2889. (co-correspondence author)

10. Bin Zhang, Xiang Wan*, Yuhao Dong, Dehui Luo, Jing Liu, Long Liang, Wenbo Chen, Xiaoning Luo, Xiaokai Mo, Lu Zhang, Wenhui Huang, Shufang Pei, Fusheng Ouyang, Baoliang Guo, Changhong Liang, Zhouyang Lian, Shuixing Zhang, Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children, Scientific Report, 2017 Jul 14;7(1):5368. (co-first author)

11. Yan Zhou, Baoxue Zhou, Tiejun Tong, Xiang Wan*. GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data, Journal of Computational Biology, 2017,24 (11), 1099-1111. (correspondence author)

12. Kai Dong, Hongyu Zhao, Tiejun Tong, Xiang Wan*. NBLDA: Negative Binomial Linear Discriminant Analysis for RNA-Seq Data,BMC Bioinformatics,2016, 17(1):369. (co-correspondence author)

13. Ruixing Ming, Jiming Liu, William K.W. Cheung, Xiang Wan*. Stochastic Modeling of Infectious Diseases for Heterogeneous Populations. BMC Infectious Disease, 2016,5(1):107. (correspondence author)

14. Jin Liu, Xiang Wan, Shuangge Ma, Can Yang. EPS: An empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes, Bioinformatics, 2016, 32(12):1856-64

15. Ben Teng, Can Yang, Jiming Liu, Zhipeng Cai, Xiang Wan*. Exploring the genetic patterns of complex diseases via the integrative genome-wide approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 13(3): 557-664. (correspondence author)

16. Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC medical research methodology, 14 (1), 135, 2014.

17. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Learning to improve medical decision making from imbalanced data without a priori cost. BMC medical informatics and decision making, 14 (1), 1, 2014.

18. Xiang Wan, Jiming Liu, William Cheung, Tiejun Tong. Inferring Epidemic Network Topology from Surveillance Data. Plos ONE, 9(6):e100661, 2014.

19. Xiaowei Zhou, Jiming Liu, Xiang Wan*, Weichuan Yu. Piecewise-constant and low-rank approximation for identification of recurrent copy number variations. Bioinformatics, 30(14):1943-1949, 2014. (correspondence author)

20. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 230-235, 2013.

21. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics, (1): 207-212, 2013.

22. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. The complete compositional epistasis detection in genome-wide association studies, BMC Genetics, 14(1):7, 2013.

23. Xiang Wan, Can Yang, Qiang Yang, Hongyu Zhao, Weichuan Yu. HapBoost: A Fast Approach to Boosting Haplotype Association Analyses in Genome-Wide Association Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(1): 207-212, 2013.

24. Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao, Weichuan Yu. Multisample aCGH Data Analysis via Total Variation and Spectral Regularization, IEEE/ACM Transactions on Computational Biology and Bioinformatics , 10(1), 207-212, 2013.

25. Xiang Wan, Can Yang, Weichuan Yu. Comments on 'An empirical comparison of several recent epistatic interaction detection methods', Bioinformatics, 28(1):145-146, 2012.

26. Geng Cui, Man Leung Wong, Xiang Wan. Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM. Journal of Management Information System, 29(1):341-374, 2012.

27. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. A new two-locus disease association pattern identified in genome-wide association studies, BMC Bioinformatics, 12:156, 2011. (co-first author)

28. Can Yang*, Xiang Wan*, Qiang Yang, Hong Xue, Weichuan Yu. The choice of null distributions for detecting gene-gene interactions in genome-wide association studies, BMC Bioinformatics, 12(Suppl 1):S26, 2011. (co-first author)

29. Lingxing Yung, Can Yang, Xiang Wan, Weichuan Yu. GBOOST: A GPU-Based Tool for Detecting Gene-Gene Interactions in Genome-Wide Case Control Studies, Bioinformatics, 28(1):145-146, 2011

30. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Xiaodan Fan, Nelson L.S. Tang, Weichuan Yu. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies, American Journal of Human Genetics, 87(3), 325-340, 2010.

31. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Detecting two-locus associations allowing for interactions in genome-wide association studies, Bioinformatics, 26(20): 2517-2525, 2010.

32. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. Predictive rule inference for epistatic interaction detection in genome-wide association study, Bioinformatics, 26(1):30-37, 2010.

33. Can Yang, Xiang Wan, Qiang Yang, Hong Xue, Weichuan Yu. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group lasso, BMC Bioinformatics, 11(Suppl 1):S18, 2010.

34. Xiang Wan, Can Yang, Qiang Yang, Hong Xue, Nelson L.S. Tang, Weichuan Yu. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome-wide association study, BMC Bioinformatics, 10:13, 2009.

35. Kimberly L Stark, Bin Xu, Anindya Bagchi, Wen-Sung Lai, Hui Liu, Ruby Hsu, Xiang Wan, Paul Pavlidis, Alea A Mills, Maria Karayiorgou1 & Joseph A Gogos Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model, Nature Genetics, 40, 751 – 760, 2008.

36. Can Yang, Zengyou He, Xiang Wan, Weichuan Yu, Qiang Yang, Hong Xue, SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies, Bioinformatics, doi:10.1093, 2008.

37. Xiang Wan, Paul Pavlidis. Sharing and reusing gene expression profiling data in neuroscience, Neuroinformatics, 5(3), 161-175, 2007.

38. Xiang Wan, Guohui Lin. CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(3), 336-348, 2007.

39. Xiang Wan, Guohui Lin. A Graph-Based Automated NMR Backbone Resonance Sequential Assignment, Journal of Bioinformatics and Computational Biology, 5(2), 313-333, 2007.

40. Guohui Lin, Xiang Wan, Theodos Tegos, Yingshu Li. Statistical Evaluation of NMR Backbone Resonance Assignment, International Journal of Bioinformatics Research and Applications, 2(2), 147-160, 2006.

41. Xiang Wan, Theodos Tegos, Guohui Lin. Histogram-based Scoring Schemes for Protein NMR Resonance Assignment, Journal of Bioinformatics and Computational Biology, 2.

 

POSITION/TITLE

Research Scientist

Assistant Professor

RESEARCH FIELD

Natural Language Processing, Information Retrieval, Applied Machine Learning, Quantum Machine Learning

EMAIL

wangbenyou@cuhk.edu.cn

PERSONAL WEBSITE

https://wabyking.github.io/old.html

EDUCATION BACKGROUND 

Ph.D. Information Engineering (Information Science and Technology), University of Padua, 2022
M.Eng. Pattern Recognition and Intelligent Systems, Tianjin University, 2017
B.Eng. Software Engineering, Hubei University of Automotive Technology, 2014

Major Achievements/Honors

NLPCC 2022 Best Paper

Best Explainable NLP Paper in NAACL 2019

Best Paper Award Honorable Mention in SIGIR 2017

Huawei Spark Award

BIOGRAPHY

Benyou Wang received Ph.D. degree at the University of Padua, Italy, funded by the Marie Curie Fellowship. He obtained a master's degree from Tianjin University. In his research career, he has visited University of Copenhagen (Denmark), University of Montreal (Canada), University of Amsterdam (the Netherlands), Huawei Noah's Ark Laboratory (Shenzhen, China), Institute of Theoretical Physics (Chinese Academy of Sciences, Beijing China), and Language Institute (Chinese Academy of Social Sciences, Beijing, China). He is committed to building explainable, robust, and efficient natural language processing approaches that are with both technical rationality and linguistic motivation. So far, he and his collaborators have won the Best Paper Nomination Award in SIGIR 2017 (the top conference in information retrieval) and Best Explainable NLP Paper in NAACL 2019 (a top conference on natural language processing). He had many articles in top international conferences NeurIPS/ICLR/SIGIR/WWW/NAACL and top international journals such as IEEE Transactions on Information System (TOIS), IEEE Transaction on Cybernetics (TOC), and Theoretical Computer Science (TCS). According to Google Scholar, he had achieved nearly 1000 citations with an h-index of 16 when he receives his Ph.D. degree.

ACADEMIC PUBLICATIONS

 [1] Jianquan Li, Xiangbo Wu, Xiaokang Liu,  Prayag Tiwari, Qianqian Xie. Benyou Wang. Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk. ACL 2023. (CCF A)

[2] Yajiao Liu, Xin Jiang, Yichun Yin, Yasheng Wang, Fei Mi, Qun Liu, Xiang Wan, Benyou Wang. One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems. ACL 2023. (CCF A)

[3] Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Fu Jie. Pre-trained language models in biomedical domain: A systematic survey. ACM Computing Surveys. (SCI Q1 top)

[4] Benyou Wang, Yuxin Ren, Lifeng Shang, Xin Jiang, Qun Liu. Exploring extreme parameter compression for pre-trained language models. ICLR 2022

[5] Peng Zhang, Wenjie Hui, Benyou Wang, Donghao Zhao, Dawei Song, Christina Lioma, Jakob Grue Simonsen. Complex-valued Neural Network-based Quantum Language Models.  ACM Transactions on Information Systems. 2022. (CCF A)

[6] Benyou Wang, Emanuele Di Buccio, Massimo Melucci. Word2Fun: Modelling Words as Functions for Diachronic Word Representation. NeurIPS 2021 (CCF A)

[7] Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen. On position embeddings in BERT. ICLR 2020.

[8] Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen. Encoding word order in complex embeddings. ICLR 2019

[9] Benyou Wang, Qiuchi Li, Massimo Melucci, Dawei Song. Semantic Hilbert Space for Text Representation Learning. The Web Conference 2018 (WWW). (CCF A)

[10] Qiuchi Li, Benyou Wang, Massimo Melucci. CNM: An Interpretable Complex-valued Network for Matching. NAACL 2018.  Best Explainable NLP Paper.(CCF B)

POSITION/TITLE

Research Scientist

Assistant Professor

RESEARCH FIELD

Natural Language Processing, Information Retrieval, Applied Machine Learning, Quantum Machine Learning

EMAIL

wangbenyou@cuhk.edu.cn

PERSONAL WEBSITE

https://wabyking.github.io/old.html

EDUCATION BACKGROUND 

Ph.D. Information Engineering (Information Science and Technology), University of Padua, 2022
M.Eng. Pattern Recognition and Intelligent Systems, Tianjin University, 2017
B.Eng. Software Engineering, Hubei University of Automotive Technology, 2014

Major Achievements/Honors

NLPCC 2022 Best Paper

Best Explainable NLP Paper in NAACL 2019

Best Paper Award Honorable Mention in SIGIR 2017

Huawei Spark Award

BIOGRAPHY

Benyou Wang received Ph.D. degree at the University of Padua, Italy, funded by the Marie Curie Fellowship. He obtained a master's degree from Tianjin University. In his research career, he has visited University of Copenhagen (Denmark), University of Montreal (Canada), University of Amsterdam (the Netherlands), Huawei Noah's Ark Laboratory (Shenzhen, China), Institute of Theoretical Physics (Chinese Academy of Sciences, Beijing China), and Language Institute (Chinese Academy of Social Sciences, Beijing, China). He is committed to building explainable, robust, and efficient natural language processing approaches that are with both technical rationality and linguistic motivation. So far, he and his collaborators have won the Best Paper Nomination Award in SIGIR 2017 (the top conference in information retrieval) and Best Explainable NLP Paper in NAACL 2019 (a top conference on natural language processing). He had many articles in top international conferences NeurIPS/ICLR/SIGIR/WWW/NAACL and top international journals such as IEEE Transactions on Information System (TOIS), IEEE Transaction on Cybernetics (TOC), and Theoretical Computer Science (TCS). According to Google Scholar, he had achieved nearly 1000 citations with an h-index of 16 when he receives his Ph.D. degree.

ACADEMIC PUBLICATIONS

 [1] Jianquan Li, Xiangbo Wu, Xiaokang Liu,  Prayag Tiwari, Qianqian Xie. Benyou Wang. Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk. ACL 2023. (CCF A)

[2] Yajiao Liu, Xin Jiang, Yichun Yin, Yasheng Wang, Fei Mi, Qun Liu, Xiang Wan, Benyou Wang. One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems. ACL 2023. (CCF A)

[3] Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Fu Jie. Pre-trained language models in biomedical domain: A systematic survey. ACM Computing Surveys. (SCI Q1 top)

[4] Benyou Wang, Yuxin Ren, Lifeng Shang, Xin Jiang, Qun Liu. Exploring extreme parameter compression for pre-trained language models. ICLR 2022

[5] Peng Zhang, Wenjie Hui, Benyou Wang, Donghao Zhao, Dawei Song, Christina Lioma, Jakob Grue Simonsen. Complex-valued Neural Network-based Quantum Language Models.  ACM Transactions on Information Systems. 2022. (CCF A)

[6] Benyou Wang, Emanuele Di Buccio, Massimo Melucci. Word2Fun: Modelling Words as Functions for Diachronic Word Representation. NeurIPS 2021 (CCF A)

[7] Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen. On position embeddings in BERT. ICLR 2020.

[8] Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen. Encoding word order in complex embeddings. ICLR 2019

[9] Benyou Wang, Qiuchi Li, Massimo Melucci, Dawei Song. Semantic Hilbert Space for Text Representation Learning. The Web Conference 2018 (WWW). (CCF A)

[10] Qiuchi Li, Benyou Wang, Massimo Melucci. CNM: An Interpretable Complex-valued Network for Matching. NAACL 2018.  Best Explainable NLP Paper.(CCF B)

POSITION/TITLE

Research Scientist

Assistant Professor

RESEARCH FIELD

Natural Language Processing, Information Retrieval, Applied Machine Learning, Quantum Machine Learning

EMAIL

wangbenyou@cuhk.edu.cn

PERSONAL WEBSITE

https://wabyking.github.io/old.html

EDUCATION BACKGROUND 

Ph.D. Information Engineering (Information Science and Technology), University of Padua, 2022
M.Eng. Pattern Recognition and Intelligent Systems, Tianjin University, 2017
B.Eng. Software Engineering, Hubei University of Automotive Technology, 2014

Major Achievements/Honors

NLPCC 2022 Best Paper

Best Explainable NLP Paper in NAACL 2019

Best Paper Award Honorable Mention in SIGIR 2017

Huawei Spark Award

BIOGRAPHY

Benyou Wang received Ph.D. degree at the University of Padua, Italy, funded by the Marie Curie Fellowship. He obtained a master's degree from Tianjin University. In his research career, he has visited University of Copenhagen (Denmark), University of Montreal (Canada), University of Amsterdam (the Netherlands), Huawei Noah's Ark Laboratory (Shenzhen, China), Institute of Theoretical Physics (Chinese Academy of Sciences, Beijing China), and Language Institute (Chinese Academy of Social Sciences, Beijing, China). He is committed to building explainable, robust, and efficient natural language processing approaches that are with both technical rationality and linguistic motivation. So far, he and his collaborators have won the Best Paper Nomination Award in SIGIR 2017 (the top conference in information retrieval) and Best Explainable NLP Paper in NAACL 2019 (a top conference on natural language processing). He had many articles in top international conferences NeurIPS/ICLR/SIGIR/WWW/NAACL and top international journals such as IEEE Transactions on Information System (TOIS), IEEE Transaction on Cybernetics (TOC), and Theoretical Computer Science (TCS). According to Google Scholar, he had achieved nearly 1000 citations with an h-index of 16 when he receives his Ph.D. degree.

ACADEMIC PUBLICATIONS

 [1] Jianquan Li, Xiangbo Wu, Xiaokang Liu,  Prayag Tiwari, Qianqian Xie. Benyou Wang. Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk. ACL 2023. (CCF A)

[2] Yajiao Liu, Xin Jiang, Yichun Yin, Yasheng Wang, Fei Mi, Qun Liu, Xiang Wan, Benyou Wang. One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems. ACL 2023. (CCF A)

[3] Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Fu Jie. Pre-trained language models in biomedical domain: A systematic survey. ACM Computing Surveys. (SCI Q1 top)

[4] Benyou Wang, Yuxin Ren, Lifeng Shang, Xin Jiang, Qun Liu. Exploring extreme parameter compression for pre-trained language models. ICLR 2022

[5] Peng Zhang, Wenjie Hui, Benyou Wang, Donghao Zhao, Dawei Song, Christina Lioma, Jakob Grue Simonsen. Complex-valued Neural Network-based Quantum Language Models.  ACM Transactions on Information Systems. 2022. (CCF A)

[6] Benyou Wang, Emanuele Di Buccio, Massimo Melucci. Word2Fun: Modelling Words as Functions for Diachronic Word Representation. NeurIPS 2021 (CCF A)

[7] Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen. On position embeddings in BERT. ICLR 2020.

[8] Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen. Encoding word order in complex embeddings. ICLR 2019

[9] Benyou Wang, Qiuchi Li, Massimo Melucci, Dawei Song. Semantic Hilbert Space for Text Representation Learning. The Web Conference 2018 (WWW). (CCF A)

[10] Qiuchi Li, Benyou Wang, Massimo Melucci. CNM: An Interpretable Complex-valued Network for Matching. NAACL 2018.  Best Explainable NLP Paper.(CCF B)

POSITION/TITLE

Research Scientist

Assistant Professor

RESEARCH FIELD

Natural Language Processing, Information Retrieval, Applied Machine Learning, Quantum Machine Learning

EMAIL

wangbenyou@cuhk.edu.cn

PERSONAL WEBSITE

https://wabyking.github.io/old.html

EDUCATION BACKGROUND 

Ph.D. Information Engineering (Information Science and Technology), University of Padua, 2022
M.Eng. Pattern Recognition and Intelligent Systems, Tianjin University, 2017
B.Eng. Software Engineering, Hubei University of Automotive Technology, 2014

Major Achievements/Honors

NLPCC 2022 Best Paper

Best Explainable NLP Paper in NAACL 2019

Best Paper Award Honorable Mention in SIGIR 2017

Huawei Spark Award

BIOGRAPHY

Benyou Wang received Ph.D. degree at the University of Padua, Italy, funded by the Marie Curie Fellowship. He obtained a master's degree from Tianjin University. In his research career, he has visited University of Copenhagen (Denmark), University of Montreal (Canada), University of Amsterdam (the Netherlands), Huawei Noah's Ark Laboratory (Shenzhen, China), Institute of Theoretical Physics (Chinese Academy of Sciences, Beijing China), and Language Institute (Chinese Academy of Social Sciences, Beijing, China). He is committed to building explainable, robust, and efficient natural language processing approaches that are with both technical rationality and linguistic motivation. So far, he and his collaborators have won the Best Paper Nomination Award in SIGIR 2017 (the top conference in information retrieval) and Best Explainable NLP Paper in NAACL 2019 (a top conference on natural language processing). He had many articles in top international conferences NeurIPS/ICLR/SIGIR/WWW/NAACL and top international journals such as IEEE Transactions on Information System (TOIS), IEEE Transaction on Cybernetics (TOC), and Theoretical Computer Science (TCS). According to Google Scholar, he had achieved nearly 1000 citations with an h-index of 16 when he receives his Ph.D. degree.

ACADEMIC PUBLICATIONS

 [1] Jianquan Li, Xiangbo Wu, Xiaokang Liu,  Prayag Tiwari, Qianqian Xie. Benyou Wang. Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk. ACL 2023. (CCF A)

[2] Yajiao Liu, Xin Jiang, Yichun Yin, Yasheng Wang, Fei Mi, Qun Liu, Xiang Wan, Benyou Wang. One Cannot Stand for Everyone! Leveraging Multiple User Simulators to train Task-oriented Dialogue Systems. ACL 2023. (CCF A)

[3] Benyou Wang, Qianqian Xie, Jiahuan Pei, Zhihong Chen, Prayag Tiwari, Zhao Li, Fu Jie. Pre-trained language models in biomedical domain: A systematic survey. ACM Computing Surveys. (SCI Q1 top)

[4] Benyou Wang, Yuxin Ren, Lifeng Shang, Xin Jiang, Qun Liu. Exploring extreme parameter compression for pre-trained language models. ICLR 2022

[5] Peng Zhang, Wenjie Hui, Benyou Wang, Donghao Zhao, Dawei Song, Christina Lioma, Jakob Grue Simonsen. Complex-valued Neural Network-based Quantum Language Models.  ACM Transactions on Information Systems. 2022. (CCF A)

[6] Benyou Wang, Emanuele Di Buccio, Massimo Melucci. Word2Fun: Modelling Words as Functions for Diachronic Word Representation. NeurIPS 2021 (CCF A)

[7] Benyou Wang, Lifeng Shang, Christina Lioma, Xin Jiang, Hao Yang, Qun Liu, Jakob Grue Simonsen. On position embeddings in BERT. ICLR 2020.

[8] Benyou Wang, Donghao Zhao, Christina Lioma, Qiuchi Li, Peng Zhang, Jakob Grue Simonsen. Encoding word order in complex embeddings. ICLR 2019

[9] Benyou Wang, Qiuchi Li, Massimo Melucci, Dawei Song. Semantic Hilbert Space for Text Representation Learning. The Web Conference 2018 (WWW). (CCF A)

[10] Qiuchi Li, Benyou Wang, Massimo Melucci. CNM: An Interpretable Complex-valued Network for Matching. NAACL 2018.  Best Explainable NLP Paper.(CCF B)

POSITION/TITLE

SRIBD, Medical Big Data Lab, Research Scientist

RESEARCH FIELD

Bioinformatics, Genome-wide association analysis (GWAS), statistical and machine learning methods for spatial transcriptome and multi-omics data

EMAIL

xiaojiashun@sribd.cn

EDUCATION BACKGROUND

Ph.D. in Mathematics, Hong Kong University of Science and Technology, 2018-2022

B.S. in Bioinformatics, Southern University of Science and Technology, 2013-2017

BIOGRAPHY

Dr. Xiao Jiashun is currently a research scientist in the Medical Big Data Laboratory of Shenzhen Institute of Big Data Research. He received a Ph.D. in Mathematics from Hong Kong University of Science and Technology in 2022, and a Bachelor's degree in Bioinformatics from Southern University of Science and Technology in 2017. He joined WeGene as a bioinformatics engineer from 207 to 2018. His research aims to develop statistical and machine learning methods for multi-omics data, including trans-ancestry association mapping, polygenic prediction with large-scale genetic data, and integrative analysis of single-cell and spatial transcriptomics data, etc. He has published several papers in international academic journals (AJHG, Bioinformatics, etc.) as the first or co-first author.

ACADEMIC PUBLICATIONS

Xiao, J.#, Cai, M.#, Yu, X., Hu, X., Chen, G., Wan, X., & Yang, C. (2022). Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics, 109(7), 1317-1337.

Xiao, J.#, Cai, M.#, Hu, X., Wan, X., Chen, G., & Yang, C. (2022). XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics, 38(7), 1947-1955.

Cai, M#., Xiao, J#., Zhang, S#., Wan, X., Zhao, H., Chen, G., & Yang, C. (2021). A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics, 108(4), 632-655.

Yu X., Xiao J., Cai M., Jiao Y., Wan X., Liu J., Yang C. (2023). PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics, 39(2).

Yiming Chao, Yang Xiang, Jiashun Xiao, et al. (2023). Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis. Signal Transduction and Targeted Therapy, Under minor revision.

# co-first author

POSITION/TITLE

SRIBD, Medical Big Data Lab, Research Scientist

RESEARCH FIELD

Bioinformatics, Genome-wide association analysis (GWAS), statistical and machine learning methods for spatial transcriptome and multi-omics data

EMAIL

xiaojiashun@sribd.cn

EDUCATION BACKGROUND

Ph.D. in Mathematics, Hong Kong University of Science and Technology, 2018-2022

B.S. in Bioinformatics, Southern University of Science and Technology, 2013-2017

BIOGRAPHY

Dr. Xiao Jiashun is currently a research scientist in the Medical Big Data Laboratory of Shenzhen Institute of Big Data Research. He received a Ph.D. in Mathematics from Hong Kong University of Science and Technology in 2022, and a Bachelor's degree in Bioinformatics from Southern University of Science and Technology in 2017. He joined WeGene as a bioinformatics engineer from 207 to 2018. His research aims to develop statistical and machine learning methods for multi-omics data, including trans-ancestry association mapping, polygenic prediction with large-scale genetic data, and integrative analysis of single-cell and spatial transcriptomics data, etc. He has published several papers in international academic journals (AJHG, Bioinformatics, etc.) as the first or co-first author.

ACADEMIC PUBLICATIONS

Xiao, J.#, Cai, M.#, Yu, X., Hu, X., Chen, G., Wan, X., & Yang, C. (2022). Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics, 109(7), 1317-1337.

Xiao, J.#, Cai, M.#, Hu, X., Wan, X., Chen, G., & Yang, C. (2022). XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics, 38(7), 1947-1955.

Cai, M#., Xiao, J#., Zhang, S#., Wan, X., Zhao, H., Chen, G., & Yang, C. (2021). A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics, 108(4), 632-655.

Yu X., Xiao J., Cai M., Jiao Y., Wan X., Liu J., Yang C. (2023). PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics, 39(2).

Yiming Chao, Yang Xiang, Jiashun Xiao, et al. (2023). Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis. Signal Transduction and Targeted Therapy, Under minor revision.

# co-first author

POSITION/TITLE

SRIBD, Medical Big Data Lab, Research Scientist

RESEARCH FIELD

Bioinformatics, Genome-wide association analysis (GWAS), statistical and machine learning methods for spatial transcriptome and multi-omics data

EMAIL

xiaojiashun@sribd.cn

EDUCATION BACKGROUND

Ph.D. in Mathematics, Hong Kong University of Science and Technology, 2018-2022

B.S. in Bioinformatics, Southern University of Science and Technology, 2013-2017

BIOGRAPHY

Dr. Xiao Jiashun is currently a research scientist in the Medical Big Data Laboratory of Shenzhen Institute of Big Data Research. He received a Ph.D. in Mathematics from Hong Kong University of Science and Technology in 2022, and a Bachelor's degree in Bioinformatics from Southern University of Science and Technology in 2017. He joined WeGene as a bioinformatics engineer from 207 to 2018. His research aims to develop statistical and machine learning methods for multi-omics data, including trans-ancestry association mapping, polygenic prediction with large-scale genetic data, and integrative analysis of single-cell and spatial transcriptomics data, etc. He has published several papers in international academic journals (AJHG, Bioinformatics, etc.) as the first or co-first author.

ACADEMIC PUBLICATIONS

Xiao, J.#, Cai, M.#, Yu, X., Hu, X., Chen, G., Wan, X., & Yang, C. (2022). Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics, 109(7), 1317-1337.

Xiao, J.#, Cai, M.#, Hu, X., Wan, X., Chen, G., & Yang, C. (2022). XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics, 38(7), 1947-1955.

Cai, M#., Xiao, J#., Zhang, S#., Wan, X., Zhao, H., Chen, G., & Yang, C. (2021). A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics, 108(4), 632-655.

Yu X., Xiao J., Cai M., Jiao Y., Wan X., Liu J., Yang C. (2023). PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics, 39(2).

Yiming Chao, Yang Xiang, Jiashun Xiao, et al. (2023). Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis. Signal Transduction and Targeted Therapy, Under minor revision.

# co-first author

POSITION/TITLE

SRIBD, Medical Big Data Lab, Research Scientist

RESEARCH FIELD

Bioinformatics, Genome-wide association analysis (GWAS), statistical and machine learning methods for spatial transcriptome and multi-omics data

EMAIL

xiaojiashun@sribd.cn

EDUCATION BACKGROUND

Ph.D. in Mathematics, Hong Kong University of Science and Technology, 2018-2022

B.S. in Bioinformatics, Southern University of Science and Technology, 2013-2017

BIOGRAPHY

Dr. Xiao Jiashun is currently a research scientist in the Medical Big Data Laboratory of Shenzhen Institute of Big Data Research. He received a Ph.D. in Mathematics from Hong Kong University of Science and Technology in 2022, and a Bachelor's degree in Bioinformatics from Southern University of Science and Technology in 2017. He joined WeGene as a bioinformatics engineer from 207 to 2018. His research aims to develop statistical and machine learning methods for multi-omics data, including trans-ancestry association mapping, polygenic prediction with large-scale genetic data, and integrative analysis of single-cell and spatial transcriptomics data, etc. He has published several papers in international academic journals (AJHG, Bioinformatics, etc.) as the first or co-first author.

ACADEMIC PUBLICATIONS

Xiao, J.#, Cai, M.#, Yu, X., Hu, X., Chen, G., Wan, X., & Yang, C. (2022). Leveraging the local genetic structure for trans-ancestry association mapping. The American Journal of Human Genetics, 109(7), 1317-1337.

Xiao, J.#, Cai, M.#, Hu, X., Wan, X., Chen, G., & Yang, C. (2022). XPXP: Improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics, 38(7), 1947-1955.

Cai, M#., Xiao, J#., Zhang, S#., Wan, X., Zhao, H., Chen, G., & Yang, C. (2021). A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. The American Journal of Human Genetics, 108(4), 632-655.

Yu X., Xiao J., Cai M., Jiao Y., Wan X., Liu J., Yang C. (2023). PALM: a powerful and adaptive latent model for prioritizing risk variants with functional annotations. Bioinformatics, 39(2).

Yiming Chao, Yang Xiang, Jiashun Xiao, et al. (2023). Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis. Signal Transduction and Targeted Therapy, Under minor revision.

# co-first author

POSITION/TITLE

Research Scientise

RESEARCH FIELD

Data Mining, Deep Neural Networks, Reinforcement Learning

EMAIL

gaoanningzhe@sribd.cn

EDUCATION BACKGROUND

Undergraduate: Department of Mathematics, Tsinghua University, 2016

Ph.D.: Department of Mathematics, University of California, Berkeley, 2021

Main Achievements/Honors:

Outstanding Graduate of Beijing (2016)

BIOGRAPHY

Dr. Gao Anningzhe graduated with a bachelor's degree from the Department of Mathematics at Tsinghua University and earned a Ph.D. from the Department of Mathematics at the University of California, Berkeley. Following graduation, Dr. Gao worked as a Senior Algorithm Researcher at Tencent and currently holds the position of Research Scientist at the Shenzhen Big Data Research Institute. His research interests include data mining, deep learning, and large-scale natural language models.

ACADEMIC PUBLICATIONS

1. Anningzhe Gao, Essential dimension of moduli stack of polarized K3 surfaces , Proceedings of the American Mathematical Society, 2020

POSITION/TITLE

Research Scientise

RESEARCH FIELD

Data Mining, Deep Neural Networks, Reinforcement Learning

EMAIL

gaoanningzhe@sribd.cn

EDUCATION BACKGROUND

Undergraduate: Department of Mathematics, Tsinghua University, 2016

Ph.D.: Department of Mathematics, University of California, Berkeley, 2021

Main Achievements/Honors:

Outstanding Graduate of Beijing (2016)

BIOGRAPHY

Dr. Gao Anningzhe graduated with a bachelor's degree from the Department of Mathematics at Tsinghua University and earned a Ph.D. from the Department of Mathematics at the University of California, Berkeley. Following graduation, Dr. Gao worked as a Senior Algorithm Researcher at Tencent and currently holds the position of Research Scientist at the Shenzhen Big Data Research Institute. His research interests include data mining, deep learning, and large-scale natural language models.

ACADEMIC PUBLICATIONS

1. Anningzhe Gao, Essential dimension of moduli stack of polarized K3 surfaces , Proceedings of the American Mathematical Society, 2020

POSITION/TITLE

Research Scientise

RESEARCH FIELD

Data Mining, Deep Neural Networks, Reinforcement Learning

EMAIL

gaoanningzhe@sribd.cn

EDUCATION BACKGROUND

Undergraduate: Department of Mathematics, Tsinghua University, 2016

Ph.D.: Department of Mathematics, University of California, Berkeley, 2021

Main Achievements/Honors:

Outstanding Graduate of Beijing (2016)

BIOGRAPHY

Dr. Gao Anningzhe graduated with a bachelor's degree from the Department of Mathematics at Tsinghua University and earned a Ph.D. from the Department of Mathematics at the University of California, Berkeley. Following graduation, Dr. Gao worked as a Senior Algorithm Researcher at Tencent and currently holds the position of Research Scientist at the Shenzhen Big Data Research Institute. His research interests include data mining, deep learning, and large-scale natural language models.

ACADEMIC PUBLICATIONS

1. Anningzhe Gao, Essential dimension of moduli stack of polarized K3 surfaces , Proceedings of the American Mathematical Society, 2020