James Long
Assistant Professor of Biostatistics
MD Anderson Cancer Center

email: jplong + the at symbol + mdanderson.org
office: FCT4.6082

Research Interests

Causal models make strong assumptions. In many cases these assumptions cannot be verified even asymptotically. The impact of violations of these untestable assumptions on parameter inference can be severe. My methodological research focuses on building causal models to maximize causal prediction performance, the ability of the model to predict the effects of system interventions. This form of model evaluation is highly robust to violations in model assumptions and directly relevant to many modern biological applications such as cell line perturbation experiments and design of rational combination therapies in cancer. In addition to my methodological research, I collaborate with clinicians and biologists at MD Anderson on projects in cancer early detection, omics data analysis, and clinical trial design. If you are a GSBS, UT Health, or Rice student interested in my research, please contact me.

Publications

See my google scholar profile for a list of publications. Code is available on github under longjp.

Selected Talks
  • Causal Models, Prediction, and Extrapolation in Cell Line Perturbation Experiments. Texas A&M 5th Annual Bioinformatics Symposium. October 14, 2022.
  • Causal Inference with Hidden Confounders: Instrumental Variables and the Generalized Causal Dantzig. ENAR. March 28, 2022.
  • Causal Mediation Analysis with Non-linear Models, Multiple Mediators, and DAGs. Southern Methodist University Statistics Seminar. November 13, 2020.
  • Parameter Estimation for Approximate Regression Models with Heterskedastic Error. MD Anderson Biostatistics Department Seminar. September 12, 2018.
  • Mapping the Milky Way Halo with RR Lyrae Stars. American Academy for the Advancement of Science Annual Meeting. February 16, 2018.

    Teaching
  • STAT 533 / GSBS 1283 - Foundations of Statistical Inference II: Spring 2020
  • Biostatistics 6082 - Survival Analysis: Spring 2019
  • Statistics 689 - Statistical Computing with R and Python: Spring 2018
  • Statistics 611 - Theory of Statistics II: Spring 2018
  • Astrostatistics: Astrostatistics course taught Fall 2016 at SAMSI.
  • Statistics 689 - Astrostatistics: Astrostatistics course taught Fall 2015 at TAMU.


    Last updated: November 11, 2022