数学科学研究所
Insitute of Mathematical Science

葛淑菲 Shufei Ge




Career

 2020.09- present, Assistant Professor, ShanghaiTech University

 2015.04-2015.08, Senior data specialist, Merkle Inc.

 2013.07-2015.03, Junior data analyst, Merkle Inc.


Education

 2016.09-2020.07, Ph.D in Statistics, Simon Fraser University

 2011.09-2013.06, M.S. in Probability and Statistics, Wuhan University

• 2007.09-2011.06, B.S. in Statistics, Central South University  


Research Interest

My research interests include Bayesian statistics, statistical machine learning methods, bioinformatics, statistical genetics. Particularly, I am interested in developing more scalable methods to address important tasks in computational biology, e.g. genetic similarity analysis, neuroimaging genetics, disease prediction.

 

Selected Publications

[9] S. Ge, S. Wang, L. Elliott. Shape Modeling with Spline Partitions.Statistics and Computing. In press.

[8] S. Wang#, S. Ge#, et al. Genome-wide association with uncertainty in the genetic similarity matrix. Journal of Computational Biology. In press. (#joint first authors).

[7] Y. Song#, S.Ge#, J. Cao, L. Wang, F. S. Nathoo. (2022). A Bayesian Spatial Model for Imaging Genetics. Biometrics, 78(2), 742-753. (#joint first authors)

[6] S. Ge, S. Wang, F. S. Nathoo, L. Wang. (2022).  Online Bayesian learning for mixtures of spatial spline regressions. Journal of Statistical Computation and Simulation, 92(7), 1530-1566.

[5] S. Wang, S. Ge, R. Doig, L. Wang. (2022). Adaptive semiparametric Bayesian differential equations via sequential Monte Carlo. Journal of Computational and Graphical Statistics, 31(2), 600-613.

[4] S. Ge, M. Cai, G. Pei. (2022). Frequency distribution of the hereditary Alzheimer’s Disease related genes seems to fit Poisson distribution, why? . Cell Discovery, 8(1):73.

[3] S. Wang, S. Ge, C. Coljin, P. Biller, L. Wang, L. Elliott. (2021). Estimating Genetic Similarity Matrices using Phylogenies. Journal of Computational Biology,28(6), 587–600.

[2] S. Ge, S. Wang, Y.W. Teh, L. Wang, L. Elliott. (2019). Random Tessellation Forests. Advances in Neural Information Processing Systems32.

[1] B. Kirkpatrick,  S. Ge,  L. Wang. (2019). Efficient computation of the kinship coefficients. Bioinformatics, 35(6), 1002-1008.





 


  


Email:


geshf@shanghaitech.edu.cn


Office: S519, School of Creativity & Arts







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