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About

I am a Postdoctoral Research Fellow at the Department of Biostatistics, Harvard University, working with Dr. Rui Duan. I obtained my Ph.D. in statistics from North Carolina State University in 2024, advised by Dr. Shu Yang. Prior to that, I obtained my Bachelor's degree in Statistics from Sun Yat-sen University in 2019.

Email:cgao@hsph.harvard.edu

Interests

• Causal inference and missing data analyses.

• Survey sampling.

• Tensor analysis and deep learning.

Honors

• Student Paper Award, ICSA, 2024

• Paige Plagge Graduate Award for Citizenship, NCSU, 2024

• Best Poster Award, DISS, 2024

• Student and Early-Career Travel Award, JSM, 2023

• Chinese National Scholarship, 2018

Internship & Training

• Ph.D. Statistician Intern, Eli Lilly and Company, 05/2023 - 08/2023

• Academic Researcher, Eli Lilly and Company, 06/2022 - 05/2023, 09/2023-07/2024

• Research Assistant, Department of Biostatistics & Bioinformatics, Duke University , 05/2021 - 08/2021

Publications

Statistics

• Real effect or bias? Best practices for evaluating the robustness of real-world evidence through quantitative sensitivity analysis for unmeasured confounding. [arXiv]
  D. Faries, C. Gao, X. Zhang, C. Hazlett, J. Stamey, S. Yang, et al. (2024), Pharmaceutical Statistics, DOI:10.1002/pst.2457.

• Improving randomized controlled trial analysis with data-adaptive borrowing [arXiv]
  C. Gao, S. Yang, M. Shan, W. Ye, I. Lipkovich, and D. Faries (2024), Biometrika, accepted.

  ** Winner of the 2024 ICSA Student Paper Award

  ** Winners of the 2024 DISS Poster Contest

• Estimating spatially varying health effects in app-based citizen science research [arXiv]
  L. Wu*, C. Gao*, S. Yang, B. J. Reich, and A. Rappold (2024), Journal of the Royal Statistical Society: Series C.

  * equal contribution

  ** Winner of the 2021 ASA Section on Statistics in Epidemiology Young Investigator Award

  ** Winner of the IMB Student Research Award from the 34th New England Statistics Symposium

• Enhancing convolutional neural network generalizability via low-rank weight approximation [arXiv]
  C. Gao, S. Yang, A.R. Zhang (2024), IET Image Processing, DOI:10.1049/ipr2.13205.

• Causal customer churn analysis with low-rank tensor block hazard model [arXiv]
  C. Gao, Z. Zhang, and S. Yang (2024), International Conference on Machine Learning.

• Transporting survival of an HIV clinical trial to the external target populations
  D. Lee, C. Gao, S. Ghosh, and S. Yang (2024), Journal of Biopharmaceutical Statistics, DOI: 10.1080/10543406.2024.2330216.

• Pretest estimation in combining probability and non-probability samples [arXiv]
  C. Gao and S. Yang (2023), Electronic Journal of Statistics, 17 (1), 1492-1546.

• Soft calibration for correcting selection bias under mixed-effects models [arXiv]
  C. Gao, S. Yang, and J. K. Kim (2023), Biometrika, 110 (4), 897-911.

• Elastic integrative analysis of randomized trial and real-world data for treatment heterogeneity estimation [arXiv]
  S. Yang, C. Gao, X. Wang, and D. Zeng (2023), Journal of the Royal Statistical Society: Series B, 85 (3), 575-596.

• Nearest neighbor ratio imputation with incomplete multinomial outcome in survey sampling [arXiv]
  C. Gao, K. J. Thompson, S. Yang and J. K. Kim (2022), Journal of the Royal Statistical Society: Series A, 185 (4), 1903-1930.

Technical Reports

• Causal inference on sequential treatments via tensor completion. [arXiv]
  C. Gao, A.R. Zhang, and S. Yang (202x), Journal of the Royal Statistical Society: Series B, revision.

• Omnibus sensitivity analysis of externally controlled trials with intercurrent events [arXiv]
  C. Gao, X. Zhang, S. Yang (202x), in revision.

• Evaluation of machine learning approaches for estimating optimal individualized treatment regimens for time-to-event outcomes in observational studies [arXiv]
  I. Lipkovich, Z. Kadziola, C. Gao, D. Wang, D. Faries (202x), submitted.

• Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials [arXiv]
  C. Gao, S. Yang, M. Shan, W. Ye, I. Lipkovich, and D. Faries (2024), submitted.

Software

R packages for integrative analysis:

• ElasticIntegrative implements a test-based analysis for the heterogeneous treatment effects combining trials and real-world data [arXiv]
• SelectiveIntegrative implements dynamically penalized borrowing framework to incorporate information from other external-control (EC) datasets with the gold-standard randomized trials [arXiv]

Python codes for tensor completion for causal analysis:

• Low-Rank-BlockTC implements a low-rank latent factor block model for the hazard probability [arXiv]

Service

• Invited Chair for Joint Statistical Meeting, Portland, OR, 2024
• Top reviewer for AISTATS (2022, 2023)
• Website Maintenance Services for Research Lab CIMA (2021-2024)
• Co-organizer for BIRS (Banff International Research Station) 5-day workshop, "Emerging Challenges for Statistics and Data Sciences: Complex Data with Missingness, Measurement Errors, and High Dimensionality", May 22–27, 2022