I am a postdoctoral research scientist working with Liam Paninski at Department of Statistics and Grossman Center for the Statistics of Mind, Columbia University. I completed my Ph.D. in biostatistics under the supervision of Ali Shojaie and Daniela Witten at the University of Washington, and I obtained my B.S. in statistics from Yuanpei College, Peking University. I am broadly interested in emerging statistical problems in learning large complex biological systems from massive data. I address these problems using statistical theory and methods in high-dimensional statistics and graphical models. Recently, I have developed an interest in applications in neuroscience.
Manuscripts in progress
- Shizhe Chen, Ben Shababo, Xinyi Deng, Johannes Friedrich, Hillel Adesnik, and Liam Paninski. Mapping neural microcircuits: Design and inference. In progress. [slides]
- Shizhe Chen, Ali Shojaie, Eric Shea-Brown, and Daniela M. Witten. The multivariate Hawkes process in high dimensions: Beyond mutual excitation. Requested revision by Annals of Statistics. [arXiv]
- Xinyi Deng, Kenneth Kay, Shizhe Chen, Mattias Karlsson, Liam Paninski, and Loren Frank. Clusterless decoding of hippocampal replay content. In progress
- Zhichao Jiang, Shizhe Chen, and Kosuke Imai. Estimating complier average causal effects from multiple data sources. In progress.
- Fang Han, Shizhe Chen, and Han Liu. Distribution-free tests of independence in high dimensions. Biometrika, 104(4): 813-828, 2017.[link][arXiv]
- Shizhe Chen, Daniela M. Witten, and Ali Shojaie. Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process. Electronic Journal of Statistics, 11(1):1207-1234, 2017. [link][MR]
- Shizhe Chen, Ali Shojaie, and Daniela M. Witten. Network reconstruction from high-dimensional ordinary differential equations. Journal of the American Statistical Association, 2017 (online version).[link][arXiv][code]
- Linbo Wang, Shizhe Chen, and Ali Shojaie. Discussion on "Causal inference by using invariant prediction: identification and confidence intervals". Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(5):1004-1005, 2016. [link]