Statistical Learning for Health Data
Cohort definition, feature engineering, validation, calibration, subgroup analysis, and reproducible reporting for clinical prediction.
Teaching & mentoring
Teaching quantitative methods from assumptions to implementation.
I ask students to derive the method, code it carefully, test it, and explain what the result does and does not support.
Teaching experience
Courses taught and supported.
My formal teaching spans undergraduate and graduate mathematics, from algebra and calculus to linear algebra and numerical analysis.
- Algebra with Applications (MATH 124)Instructor · Fall 2021
- Multivariable CalculusTeaching Assistant · Spring 2021
- Applied Linear AlgebraTeaching Assistant · Spring 2020
- Numerical Analysis (MATH 521)Teaching Assistant · Spring 2019
- Advanced Linear AlgebraTeaching Assistant · Fall 2018
- Engineers’ Calculus (MATH 155E)Recitation Leader · Fall 2018
- Advanced Engineering MathematicsInstructor · Spring 2009
- Differential EquationsTeaching Assistant · Fall 2008
Courses prepared to develop
Graduate courses I am prepared to develop.
These are proposed courses, not courses I have already taught. Each course connects statistical or computational methods to health and biomedical applications.
Causal Inference for Observational Health Records
Causal graphs, confounding, treatment effects, sensitivity analysis, and transparent assumptions in EHR-based studies.
Uncertainty-Aware Clinical ML
Calibration, conformal prediction, distribution shift, subgroup performance, and patient-level error analysis.
Bayesian Methods for Biomedical Networks
Structure learning, MCMC over graph spaces, ensemble inference, and bootstrap stability for biomedical networks.
Optimal Transport for Biomedical Data Alignment
Entropic and unbalanced optimal transport for cross-site, cross-platform, and cross-species alignment.
Mentoring
Mentoring through research questions, code, and interpretation.
As a Trainee Mentor and Hackathon Co-Organizer for the MISM Center of Excellence Summer Trainee Program at Duke in 2026, I advised trainees on simulation design, parameter identifiability, and code review.
I have also served as an External Graduate Student Panelist for an NSF REU, an Invited Graduate Judge at WVU research symposia, a mentor to three M.Sc. students, and founder and president of the WVU SIAM Student Chapter.