I am a fourth-year PhD candidate in Biostatistics at Boston University. I also serve as a graduate student fellow at the Rafik B. Hariri Institute for Computing and Computational Science & Engineering at BU, and a graduate student fellow at Boston Medical Center, working in the HIV/HCV epidemiology and outcome research group.

I am interested in developing statistical tools to overcome challenges in public health surveillance and address critical problems in population health and epidemiology. Specifically, I work on developing Bayesian multi-level models for population size estimation and spatial disease mapping that leverage incomplete surveillance data to address tangled epidemiological and social problems, such as the opioid epidemic. Through my research, I have developed methodological research interests in Bayesian methods, small area estimation, spatial statistics, multivariate modeling, indirect methods for population size estimation, and complex computer simulator calibration. I primarily focus on modeling observational data, especially large administrative data, claims data, and electronic health records. I am interested in connecting large-scale health data to clinical trial and genomics data to inform the study design and improve the translational pipeline.

In addition, I work as a dedicated researcher in various interdisciplinary collaborative research that integrates quantitative approaches to improve population health outcomes, promote the equitable clinical practice, and inform clinical decision-making and health policy. I have participated in research on populations with infectious diseases (e.g., HIV and HCV), cancer, and substance use disorders. I am also engaged in research funded by NIH HEAL initiative using innovative methods to help understand the impact of and improve upon policies that affect people who are drug-use with or at high risk for infections of associated diseases. I look forward to applying the developed techniques and advancing the methods in a broader scope of health sciences problems and new diseases and discovering the methodological and practical connections between different contexts.