Job-Seeking PhD Candidates & Postdoctoral Fellows

PhD Degree/Year: PhD in Statistics from Michigan State University (2022)

Doctoral dissertation title: Novel Methods for Functional Data Analysis with Applications to Neuroimaging Studies

Postdoc Adviser: Vadim Zipunnikov

Area(s) of focus: Functional and distributional data analysis, Neuroimaging, Digital Health

Research: My research interests include functional and distributional data analysis, modeling of massive and complex data, nonparametric statistics, statistics in digital health technologies, neuroimaging, and other related health sciences. In my current post-doctoral role at Johns Hopkins Bloomberg School of Public Health, I am deeply engaged in developing cutting edge approaches for analyzing real-time data collected from digital health technologies with a specific emphasis on understanding physical activity and sleep patterns to predict the onset of neurological disorders. My doctoral dissertation focused on introducing innovative methods for functional data analysis, such as developing novel approaches for analyzing complex structured time-varying tensor data coming from neuroimaging studies.

Additional Research Experiences: I have over three years of experience as a research assistant for an NIH-funded project in the College of Nursing at Michigan State University. The goal of the study was to improve symptom management for cancer patients by individualizing care using reflexology and meditative practices, conducted with caregivers using multi-staged interventions based on the sequential multiple-assignment randomized trial (SMART) design. I also participated in a summer research program at Oak Ridge National Lab through the Mathematical Sciences Graduate Internship (MSGI) program funded by the NSF, and had a summer research internship at the Johns Hopkins Bloomberg School of Public Health.

Job type preference: Assistant professor (tenure-track)

JHU Email  Personal Email  github

PhD Degree/Year: PhD in Biostatistics from The University of Texas MD Anderson Cancer Center UTHealth Graduate School (2021)

Doctoral dissertation title: Mixture Model Approaches To Integrative Analysis Of Multi-Omics Data And Spatially Correlated Genomic Data

Postdoc Adviser: Nilanjan Chatterjee

Area(s) of focus: Statistical genetics, multi-omics data integration, cancer genomics

Research: My research focuses on developing novel statistical methods to enhance the interpretation and application of polygenic scores (PGS), measures meant to summarize a person’s genetic predisposition for a trait and/or a disease. I developed methods to jointly model gene-environment correlations and interactions using PGS in case-control studies, with data applications in the UK Biobank; I also developed methods in estimating risk parameters of PGS in family-based studies to understand genetic direct, indirect, and gene-environment interactions between genotype-phenotype associations. Furthermore, I have been working on integrating genetic and genomic data to elucidate the etiologies of complex human diseases in large-scale, diverse consortia.

Job type preference: Assistant professor positions, research and teaching

Geographical preference: Mid-Atlantic, Baltimore-Washington Metropolitan Area, New England

Email   github

PhD Degree/Year: PhD in Statistics from Texas A&M University (2022)

Doctoral dissertation title: Efficient Choice of Priors for Bayesian Hypothesis Tests in Psychology and for Dynamic Modeling of Zero-Inflated Directed Networks

Postdoc Adviser: Abhirup Datta and Scott Zeger

Area(s) of focus: Hierarchical Bayesian modeling; Verbal autopsy (VA) misclassification modeling; VA-based child mortality estimation; Bayesian hypothesis tests; Sequential tests; Non-local Prior; Bayes Factor Function; Bayesian Transfer Learning; Dynamic network modeling; Latent structure modeling; Statistical methods for high dimensions.

Research: During my postdoc, I am collaborating with the CHAMPS project and the Department of International Health to streamline and improve verbal autopsy (VA)-based mortality surveillance. This is based on a novel VA misclassification model that improves mortality estimation in low and middle-income countries (LMICS). Partnering with Mozambique's Instituto Nacional de Saúde and the CA CODE, I am applying the research in nearly 50 high-mortality countries, with estimates to be publicly shared. As a co-investigator for the 2024 Johns Hopkins Data Science and AI Institute Demonstration Projects Award, I am developing a one-stop web portal to improve the accessibility of research outputs.

Job type preference: Assistant professor (Tenure track)

Email    personal website