Epidemiology & Public Health
Education and Training
Postdoctoral researcher, University of Pennsylvania, 2021.
Ph.D. in Biostatistics, Pennsylvania State University, 2020.
Please find details from my Personal Website: https://sites.google.com/view/chixiangchen/home
Data/Information Integration; Causal machine learning; Network Analysis; High-dimensional Data; Cell Type Deconvolution and single cell data analysis.
- Chen, C., Leung, Y., Ionita, M., Wang, L.-S., and Li, M. (2022). Omnibus and Robust Deconvolution Scheme of Bulk RNA Sequencing Data via Integrating Multiple Single-cell reference sets and Prior Biological Knowledge. Bioinformatics. Accepted and to be appear.
- Chen, C., Yu, T., Shen, B, and Wang, M. (2022). Synthesizing secondary data into survival analysis to improve estimation efficiency. Biometrical Journal. Accepted and to be appear.
- Chen, C., Shen,B., Ma, T., Wang, M., and Wu, R. (2022). A statistical learning framework for recovering dynamic networks from static-state data. Bioinformatics. 38.9 (2022): 2481-2487.
- Chen, C., Han, P., and He, F. (2022). Improving main analysis by borrowing information from auxiliary data. Statistics in Medicine. 41(3):567-579. doi: 10.1002/sim.9252.
- Chen, C., Jiang, L., Shen, B., Wang, M., Griffin, C., Chinchilli, V., and Wu, R. (2021). A computational atlas of tissue-specific regulatory networks. Frontiers in Systems Biology. 1:764161. doi:10.3389/fsysb.2021.764161.
- Chen, C., Wang, M., Wu, R. and Li, R. (2021). A robust consistent information criterion for model selection based on empirical likelihood, Statistica Sinica. doi:10.5705/ss.202020.0254.
- Chen, C., Shen, B., Liu, A., Wu, R. and Wang, M. (2021). A multiple robust propensity score method for longitudinal analysis with intermittent missing data. Biometrics, 77(2), 519-532.
- Chen, C., Shen, B., Zhang, L., Xue, Y. and Wang, M. (2020). Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.Biometrics, 75(3), 950-965.
- Chen, C., Jiang, L., Fu, G., Wang, M., Wang, Y., Shen, B., Liu, Z., Wang, Z., Hou, W., Berceli, S. and Wu, R. (2019). An omnidirectional visualization model of personalized gene regulatory networks, npj Systems Biology and Applications,5(1), 1-11.
Dr. Chen has broad interests in both theoretical methodology development and statistical application in multidisciplinary areas. Recently, he is especially interested in developing robust statistical frameworks to integrate information from multiple data sources, with applications in both clinical and genomics studies. Many ongoing works involve survival analysis, causal inference, single-cell data analysis, among others, under the umbrella of the proposed framework.
Another aspect he is working on is to develop a statistical framework to recover dynamic networks from static-state data. The collection of temporal or perturbed data is a prerequisite for reconstructing dynamic networks. However, these types of data are seldom available for genomic studies in medicine, significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. One proposed framework from Dr. Chen's team makes the reconstruction possible from steady-state data by introducing an agent and incorporating a varying coefficient model with ordinal differential equations. Multiple networks can be inferred corresponding to covariate effects that are linked to known or latent agents, such as disease risk.
In addition, Dr. Chen has experience on model selection, robust longitudinal data analysis, missing data, and other statistical topics with applications in biomedical studies. Besides statistical methodology development, Dr. Chen has worked on multiple projects with collaborators in various disciplines. It ranges from angiology, biochemistry, neuroscience, among others. From past five years with expertise in biostatistics, he denoted himself to data management and preprocessing, modeling and statistical analysis, result evaluation and interpretation as well as intellectual contributions to the work and any publications that result from it.
Dean's Award for Scholarly Achievement, College of Medicine, Penn State Univ. May.2021.
Alumni Society Award, College of Medicine, Penn State Univ. Sep.2019.
JSM Student Paper Award, ASA Nonparametric Section. Jul.2019.
Scholarship Award, the 24th Summer Institute in Statistical Genetics, Univ. of Washington. Jul.2019.
Biopharm-Deming Student Scholar Award, ASA Biopharmaceutical Section and the 74th Annual Deming Conference on Applied Statistics. Dec.2018.
Student Paper Award, ICSA Applied Statistics Symposium. Jun.2018.
Editorial Assistant, ICSA Bulletin 2021 Issue. Dec.2020-present
Session Chair, ENAR 2021 Spring Meeting, Online. Mar.2021
Session Chair, ENAR 2019 Spring Meeting, Philadelphia, PA. Mar.2019
Organizer for Biostatistics Student Seminar, Penn State Univ. Sep.2018-May.2020
Chair, Hershey Chinese Students and Scholars Association. Jan.2018-Sep.2019
Paper Review Service as a Reviewer:
Bioinformatics, Biometrics, Statistics in Medicine, Journal of Biopharmaceutical Statistics, Biometrical Journal, BMC Medical Research Methodology, European journal of neuroscience, Computers in Biology and Medicine, among others
- Invited talk. An Efficient Data Integration Scheme to Synthesize Information from Multiple Secondary Outcomes into the Main Data Analysis, SLAM, JHU, April 2022.
- Invited talk. Robust Schemes for Incorporating Multiple Auxiliary Information in Biomedical Studies, BRIGHT, UMD, Nov. 2021.
- Contributed paper presentation. A Generalized Weighting Scheme to Integrate Information from Multiple Auxiliary Records to the Main Study, ENAR Spring Meeting,
Baltimore, MA. Mar.2021
- Contributed paper presentation. Informative dynamic ODE-based-network learning (IDOL) from steady data, ENAR Spring Meeting, Nashville, TN. Mar.2020
- Paper award presentation. A robust consistent information criterion for model selection based on empirical likelihood, JSM, Denver, CO. Jul.2019
- Contributed paper presentation. A robust consistent information criterion for model selection based on empirical likelihood, ENAR Spring Meeting, Philadelphia, PA. Mar.2019
- Scholar award poster. A robust consistent information criterion for model selection based on empirical likelihood, The 74th Annual Deming Conference, Atlantic City, NJ. Dec.2018
- Paper award presentation. Empirical likelihood based criteria for model selection on marginal analysis of longitudinal data with dropout missingness, ICSA Applied Statistics Symposium, New Brunswick, NJ. Jun.2018
- Contributed paper presentation. Empirical likelihood based criteria for model selection on marginal analysis of longitudinal data with dropout missingness, ENAR Spring Meeting, Atlanta, GA. Mar.2018