James P. Long

James P. Long serves as an Associate Professor of Biostatistics at the University of Texas MD Anderson Cancer Center, with a primary appointment in the Division of Discovery Science. He also holds adjunct roles as Adjunct Associate Professor at the Department of Statistics, Rice University, and Associate Professor in the Department of Quantitative Sciences at the Graduate School of Biological Sciences, Houston, TX.

His research focuses on causal models for intervention prediction in biological applications, including cell perturbation experiments and rational cancer therapies. He collaborates with clinicians and biologists on cancer early detection, omics data analysis, and clinical trial design. Long teaches advanced statistical courses such as Foundations of Statistical Inference II (Rice/GSBS), Survival Analysis (UTHealth), and Statistical Computing with R/Python (Texas A&M). His educational background is not explicitly detailed in the provided texts, but his teaching history spans institutions like Rice, UTHealth, Texas A&M, and SAMSI.

Long has presented at multiple academic venues, including WNAR (2024), UT Health Houston (2024), University of Texas San Antonio (2023), and conferences like the American Academy for the Advancement of Science (2018). He actively contributes to open-source statistical software projects, with 20 repositories on GitHub and collaborations in astrostatistics and biostatistics.

Though no specific scientific awards are listed, his academic roles and contributions indicate recognition in his field. Grant support is mentioned but details are not provided in the texts. His advising includes students from Rice, UTHealth, and Texas A&M, with notable collaborations in multidisciplinary research teams. Long’s work integrates statistical methodologies with both biomedical and astrophysical applications, reflecting his dual expertise in biostatistics and astrostatistics.

Labs and teams associated with his work include collaborations at MD Anderson’s Biostatistics Department and partnerships through SAMSI’s astrostatistics programs. He maintains an active GitHub profile, emphasizing reproducible research and software development in statistical applications.