Prospective Students

Presently I am open to directing one or two new Ph.D. students. I also have some non-thesis projects for students interested in a research experience. Students interested in any of the topics described below can contact me via email ( or drop by during my office hours.


Thesis Projects: Topics of current interest for Ph.D. dissertations include data integration involving genomics and proteomics; data integration involving imaging data and molecular data; approaches for jointly analyzing multiple genomic sites with respect to various genetic and proteomic data. I am also open to projects in the social sciences. See my Research page for examples of projects in which I am interested.

Non-thesis Projects: I have a few projects suitable for research experience, but not necessarily for Ph.D. dissertations. These are unfunded projects, but they will give students an opportunity to co-author papers. Below are some current projects suitable for Ph.D. students.

Functional image analysis. Patients undergoing radiation therapy for cancer risk the possibility of radiation toxicity. Dr. Richard Castillo (UTMB) and I are interested in analyzing imaging data to develop a statistical prediction method for radiation toxicity. The project involves regression analysis from both frequentist and Bayesian perspectives. This project has the potential to develop into a Ph.D. dissertation topic depending on findings. A background in regression and Bayesian analysis are desirable.

Proteomics in Antibiotic Resistance: Humans are becoming more resistant to antibiotics. This project concerns analyzing some proteins involved in such resistance. Along with Dr. Tim Palzkill (BCM) we are interested in analyzing the amino acid sequences of proteins known to be involved in bacterial resistance to antibiotics. Previous work has identified single sites associate with resistance and this work aims to identify multiple sites (“interactions”). This project involves analysis of discrete data.

Analysis of horse injuries. Horses experience several types of injuries that prevent them from walking, running or racing. This project concerns developing predictive models for return to racing performance after sustaining certain injuries. This project involves analysis of discrete data.


I am open to working with undergraduate students on existing or new projects. I am especially open to students interested in the biosciences and social sciences. Interested students should have at least STAT 310, 405, and 2-3 additional statistics courses at the 300 of higher level (excluding 305, 385). Advanced students may be considered for the non-thesis projects listed above.

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