PhD Candidate in Biostatistics
Department of Epidemiology & Biostatistics, Dornsife School of Public Health, Drexel University · Advisor: Prof. Brisa N. Sánchez
I develop statistical methods for correlated, spatially structured, and zero-inflated count data, with applications in built environment epidemiology and health equity. My dissertation advances penalized GEE with fusion learning for repeated-measures count outcomes and extends quadratic inference functions (QIF) to high-dimensional settings — motivated by characterizing food environments near schools across multiple spatial scales in California.
Recent Highlights
Academic Training
Scholarly Focus
My methodological work centers on marginal modeling approaches — particularly penalized GEE and QIF — for spatially correlated repeated-measures data with excess zeros. Substantively, I apply these methods to characterize the built food environment near schools and examine socioeconomic patterning of health-relevant exposures across geographic scales.
Research Interests
Technical Skills
R, SAS, Python, SQL, Stata, SPSS, MPlus, WinBUGS; statistical simulation
REDCap (administrator), relational databases, EHR workflows, Git/GitHub, reproducible analytical pipelines (RAP)
ggplot2, plotly, Tableau, R Shiny dashboards, LaTeX, Quarto; Statistical Analysis Plans (SAPs)
PSM, IPW, IV, RDD; latent class/transition models; Bayesian hierarchical modeling; clinical trial design; RWE
NIH-Funded Projects
Additional collaborations: MESA Study (latent transition analysis, retail environment & HbA1c) · All of Us Research Program (school racial diversity; Hispanic adult obesity)
Scholarly Output
Peer-Reviewed Publications
Under Review & In Preparation
Pedagogy
| Year | Course | Role | Institution | Responsibilities |
|---|---|---|---|---|
| 2026 | Introduction to Biostatistics (BST 571) — Graduate | Instructor of Record | Drexel University | ↑ See What's New highlights |
| 2025 | Intermediate Biostatistics — Graduate | Guest Lecturer | Drexel University | Delivered invited guest lecture on advanced biostatistics topics to graduate students in epidemiology and public health |
| 2024 | Survival Data Analysis (BST 557 / PBHL 459) | Teaching Assistant | Drexel University | ↓ See Teaching Assistant Responsibilities |
| 2023 | Intermediate Biostatistics I (BST 560) | Teaching Assistant | Drexel University | ↓ See Teaching Assistant Responsibilities |
| 2022 | Introduction to Biostatistics (BST 571) | Teaching Assistant | Drexel University | ↓ See Teaching Assistant Responsibilities |
| 2022 | Biostatistics & Epidemiology (two courses) | Instructor | Govt. Titumir College, Dhaka University | Guest instructor for two undergraduate courses — Biostatistics and Epidemiology; covered descriptive statistics, hypothesis testing, study design, and measures of disease frequency and association |
| 2022 | Intro to Biostatistics, Statistics Lab, Recitation | Teaching Fellow | BRAC James P Grant School of Public Health | Delivered lectures, lab sessions, and recitation classes in Intro to Biostatistics to a multi-national MPH cohort; applied data analysis, course coordination, exam design, grading, and student mentorship for final projects |
| 2017–2018 | Applied Statistics & Data Science | Mentor | Durbin Labs Limited, Dhaka | Delivered Applied Statistics & Data Science workshops for industry professionals; covered statistical programming and applied modeling |
| 2015–2017 | Statistics — College Level | Freelance Tutor | Dhaka, Bangladesh | College-level statistics tutoring; exam preparation, problem-solving support, and concept reinforcement for individual students |
Teaching Assistant Responsibilities — Drexel University (2022–2024)
Certified: International Teaching Assistant (ITA) Training Program, Drexel University — Recommended as Instructor
Dissemination
Lectures by Invitation
Conference Presentations
In the Field
Drexel University — Lectures & Talks
Jahangirnagar University — Academic Events
Community & Profession
Additional Training & Certifications
Teaching & Learning
Practical tutorials on biostatistics, R programming, spatial methods, and data analysis — aimed at graduate students and applied researchers.
More tutorials on survival analysis, zero-inflated models, Bayesian methods, and reproducible research workflows coming soon.
Get in Touch