I am only able to serve as the PhD advisor for students who are currently enrolled in the Statistics PhD program at the University of South Carolina (USC). If you are interested in applying to the Statistics PhD program at USC but are not yet enrolled there as a student, please visit the department webpage for information about applying. Note that PhD students are admitted by the department, not by individual faculty, and I do not agree to supervise applicants who have not yet been accepted to the USC Statistics PhD program. I am also unable to provide feedback on anyone’s application.
Priority for PhD advising is given to students who have strong interest or prior experience in one or more of the following areas: (i) high-dimensional modeling and scalable inference, (ii) Bayesian statistics, or (iii) deep learning. Strong programming skills and solid training in mathematical analysis, linear/matrix algebra, and numerical optimization or Bayesian computation (e.g. MCMC) are also very helpful. However, potential advisees who are highly self-motivated and willing to independently develop the necessary mathematical foundation and coding skills will be considered. Please have a look at my research to get an idea of what my PhD advisees may work on.
I am also happy to collaborate with other graduate students who are not my PhD advisees. Please feel free to reach out to me if you have any research ideas.
Undergraduate and Masters students
Feel free to reach out to me about research opportunities. Priority is given to students with whom I have interacted before through courses or other opportunities. You must have taken at the minimum STAT 511-512 and MATH 344 or MATH 544 (or their equivalent at another institution) to work with me. Students who have experience with R or Python are preferred.