Courses
BIOS 667 · Applied Longitudinal Data Analysis
GEEs, mixed models, joint modeling, and missing-data tactics grounded in public health and medical case studies. Course re-implemented from scratch with lectures embedding live R code examples and illustrations, complemented by AI-friendly assignments that emphasize reproducible workflows.
BIOS 735 · Statistical Computing
Machine learning, git/GitHub, simulation, optimization, and package development for translational teams. Project-based with open-source code reviews. Course site
Reproducible Oncology Analytics Workshop
Git, Quarto notebooks, and genomic-study case studies tailored for LCCC T32 trainees.
Mentoring & Student Outcomes
Five completed PhD advisees (co-advised with Drs. Ibrahim, Love, Kosorok).
Academic placements:
- Pedro Baldoni (2020) — Assistant Professor, University of Pittsburgh Biostatistics
- Hillary Heiling (2023) — Senior Biostatistician, Dana-Farber Cancer Institute
Industry placements:
- Euphy Wu (2024) — Biostatistician, Precision Genomics
- David Lim (2022) — Scientist, GSK
- Scott Van Buren (2020) — Scientist, GSK
Training approach: Independent research aims, embedded clinician collaborations, weekly manuscript meetings, and reproducible software deliverables. I help students prepare for the careers they want — whether in academia, industry, or government — and tailor their projects to best position them for their goals. Students typically complete 2-4 first-author publications during their PhD program.
For Prospective PhD Students
Information about training structure, technical expectations, and lab environment for students considering applying.
Coding & software development
Required: R programming (or strong willingness to learn intensively in Year 1). Students should be comfortable with data manipulation, visualization, and basic statistical modeling.
Developed during training: Git/GitHub workflows, R package development, reproducible research practices (Quarto/RMarkdown), high-performance computing. Python, Julia, or C++ for specialized projects as needed.
Software contribution: Most students develop new packages as part of dissertation work. CRAN/Bioconductor submission expected for methods projects.
Mentoring cadence
Individual meetings: Weekly 30-60 min during active project phases; biweekly during writing/revision periods.
Lab meetings: Weekly group manuscript meetings where students present works-in-progress for feedback.
Mentoring approach: Hands-on in Year 1-2 (weekly code review, collaborative writing); increasingly independent in Years 3-5 (student-driven agenda, advisor feedback). Students drive their own research direction by dissertation proposal stage.
Publication expectations
Dissertation projects: Students are first author on all dissertation aims (typically 2-3 papers). Advisor is senior author unless methods are co-developed equally.
Collaborative projects: Authorship determined by contribution; students are first author when leading analysis and writing. Typical timeline: 6-12 months from data to submission for collaborative work; 12-24 months for methods papers.
Typical outcome: Students graduate with 3-4 first-author publications (mix of methods and collaborative) plus 2-4 co-authorships.
Financial support model
Typical funding: Students are typically 100% GRA-funded through lab research grants.
Grant applications: Students are encouraged to apply for F31 (predoctoral fellowship) or departmental training grants. Advisor provides proposal feedback and budget support.
Conference travel: Funding available for 1-2 conferences per year (JSM, ENAR) when presenting work.
Disease areas & flexibility
Current focus: Breast and pancreatic cancer genomics, adaptive trial design, ctDNA modeling, tumor-stroma interactions.
Methodological breadth: Statistical methods are often disease-agnostic (missing data, high-dimensional modeling, Bayesian adaptive designs). Students interested in immunotherapy, radiomics, or other cancer types are welcome.
Student-driven exploration: Students have flexibility to propose new application areas if methodologically aligned with lab expertise.
Work culture & resources
Collaboration: Embedded in UNC Lineberger with regular contact with cancer researchers. Students often contribute and support computational and statistical needs of ongoing Lineberger studies.
Computational resources: Access to Longleaf HPC cluster, lab GitHub organization, shared codebases for common workflows.
Work expectations: Flexible hours; no expectation of evening/weekend work except near deadlines.
Apply through the UNC Biostatistics PhD program. Prospective students interested in the lab can email naim@unc.edu with questions about research directions, lab culture, or training philosophy. Current UNC Biostatistics PhD trainees: email directly to discuss joining the lab or participating in collaborative projects.
Last updated: March 2026