Statistics faculty actively seeking new PhD advisees

Alphabetical by last name

Sujit Ghosh

• 1. Interested to advise or co-advise new students beginning Fall of 2027
  2. Potential research topics: Bayesian machine learning methods with applications in astrostatistics, biostatistics and finance
  3. Some recent papers available on NCSU CI profile (link) and some recent students’ theses that he has supervised:
     1. Bayesian Methods for Dichotomous, Large, and Massively Large Data (Hyoshin Kim, 2025)
     2. Global Optimization and Time Series Forecasting (Xinyu Zhang, 2024)
     3. Subsampling and Active Learning (Qun Sui, 2023)
• Email address: sghosh2@ncsu.edu
• Academic website: https://www4.stat.ncsu.edu/~sghosh2//

Jungeum Kim

• Advising: it would depend on my capacity at the time of contact
• I work on valid uncertainty for models we cannot easily write down, such as generative AI
  1. Conformal prediction: prediction sets that stay valid without assuming the model is correct, including for AI system
  2. Simulation-based inference and uncertainty-aware Bayesian optimization for scientific discovery, such as protein design
  3. Scalable inferential models and imprecise probability
• Email address: jkim255@ncsu.edu
• Academic website: https://jungeumkim.com

Amanda Mejia

• Seeking 1–2 graduate students, with possible funding support from NIH R01 grant
• Potential projects include
  • Spatial Bayesian models for functional MRI
  • Spatial smoothing spline models for functional MRI
  • Bayesian ICA with functional connectivity dynamics
  • Various other projects related to fMRI and Bayesian models
• Email address: afmejia@ncsu.edu
• Academic website: https://www.statmindlab.com/

Jonathan Stallrich

• Can take on 1 student and/or co-advise
• Some funding for students but not full semester funding
• Research mainly focuses on experimental design but has been pushing these beyond the classical methods. In particular, has interesting, novel projects about:
  1. Design and analysis of computer experiments
  2. Experimental design with functional predictors
  3. Optimal designs for generalized linear models
  4. Optimal designs for penalized estimators (see link)
  5. A/B testing in the presence of networks (see link which is a review paper he coauthored that has nearly 13,000 views)
• Email address: jwstalli@ncsu.edu
• Academic website: https://jonstallrich.com

Jonathan Williams

• Will take 1–2 students in Fall 2027
• Email address: jwilli27@ncsu.edu
• Academic website: https://jonathanpw.github.io