Team

Chong Wu, PhD

Assistant Professor, Department of Biostatistics (Sept. 2022 - Present)
Affiliate Faculty, Institute for Data Science in Oncology (IDSO)
The University of Texas MD Anderson Cancer Center

Adjunct Assistant Professor, Department of Statistics, Florida State University (Sept. 2022 - Present)
Adjunct Assistant Professor, Dept. of Biostatistics and Data Science, UTHealth Houston School of Public Health (Apr. 2023 - Present)
Adjunct Assistant Professor, Department of Statistics, Rice University (Jul. 2023 - Present)

Research Interests: Statistical genetics/genomics (TWAS, PWAS, multi-omics integration, PRS), causal inference (Mendelian randomization, potential outcome models), machine learning, AI for science (AI for genetics, AI for drug discovery, AI for cancer treatment prediction, AI for medicine), high-dimensional inference.

Curriculum Vitae | GitHub | Google Scholar | Email

Short Bio

I am an Assistant Professor in the Department of Biostatistics and an affiliated faculty member of the Institute for Data Science in Oncology (IDSO) at The University of Texas MD Anderson Cancer Center. Previously, I was an Assistant Professor in the Department of Statistics at Florida State University (Aug. 2018 - Aug. 2022). I completed my PhD in Biostatistics at the University of Minnesota (2013 - 2018), co-advised by Profs. Weihua Guan and Wei Pan. My academic path began with a BS in Applied Mathematics (2013) from Huazhong University of Science and Technology, advised by Prof. Xiaoyang Zhou.

My research program focuses on developing pioneering statistical methodologies and computational tools to analyze complex, large-scale biomedical data, particularly in genomics and genetics. My expertise spans statistical genetics (GWAS, TWAS, multi-omics integration, PRS), causal inference (Mendelian randomization, potential outcome frameworks), machine learning (clustering, prediction), and high-dimensional statistical inference. A major current focus is leveraging AI for scientific discovery, including developing foundational models for genetics, predicting cancer treatment outcomes using AI, exploring AI-driven drug discovery, and advancing AI applications in medicine more broadly. I am deeply committed to reproducible research, actively developing and maintaining open-source software (primarily R packages) to facilitate the adoption of our methods.