Teaching & advising
Graduate instruction and advising
Core teaching includes BIOS 667 (longitudinal modeling) and BIOS 735 (statistical computing), complemented by advising, office hours, and workshops for Gillings and Lineberger students.
Course lineup
Courses taught
Graduate core
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.
Computing
BIOS 735 · Statistical Computing
Machine learning, git/GitHub, simulation, optimization, and package development for translational teams.
Workshops
Reproducible oncology analytics
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 Positions
Faculty & Research Staff
- Pedro Baldoni (2020) — Assistant Professor, University of Pittsburgh Biostatistics
- Hillary Heiling (2023) — Senior Biostatistician, Dana-Farber Cancer Institute
Industry Positions
Pharmaceutical, Biotech & Genomics
- 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.
Technical Skills
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.
Meeting Structure
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.
Authorship & Projects
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.
Funding & Support
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.
Research Scope
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.
Lab Environment
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.
For Prospective PhD Students
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.
For current UNC Biostatistics PhD trainees: Email directly to discuss joining the lab or participating in collaborative projects.
Last updated: November 2025