I am an Assistant Professor of Epidemiology at the Department of Public Health Sciences of the University of Rochester Medical Center. I completed my pre-and post-doctoral training in various domains, including epidemiology, interventional trial design and implementation, infectious diseases prevention research, social behavioral science, and global health. I have strong expertise in using various complex data-driven and epidemiologic methods to assess research gaps, intervention opportunities, and the efficacy/effectiveness of existing interventions and health programs in various research contexts. Furthermore, I have an unparalleled passion for cultivating organic, diverse, inclusive, and sustainable environments across various settings through my professional services, research, and teaching.
My current research focuses on leveraging advanced quantitative/qualitative methods, social-behavioral theories, implementation science, and technology (e.g., social media) to design/implement novel prevention interventions to tackle a wide array of research priorities, such as social stigmatization among marginalized populations (e.g., LGBTQ populations), health messaging, substance use, mental health, social determinants of health, quality-of-life, and infectious disease prevention (e.g., HIV, STD, and COVID). I have served as the PI/Co-PI/Co-I of multiple UR and NIH-funded studies and published frequently in reputable peer-reviewed journals. My research involves close collaboration with multidisciplinary teams in academia, medical/clinical institutions, community-based organizations, and government (e.g., CDC, Department of Health).
I am currently teaching PM451: Infectious Diseases Epidemiology and PM510: Causal Inference in Epidemiology. I enjoy mentoring and working with students of various backgrounds and helping my students to become academically competent.
• Infectious disease prevention research (e.g., HIV, STDs, and COVID)
• Advanced epidemiologic methods (e.g., causal inference, bias/confounding control,
quantitative, measurement, participant sampling and recruitment, survey design, and
implementation, and data collection)
• Statistical analyses (e.g., various regression-based analyses, propensity score
application, causal mediation/moderation analyses, and longitudinal analyses)
• Statistical programming (e.g., Stata, SAS)
• Qualitative methods (e.g., focus groups, in-depth interviews, social-behavioral theories)
• Scientific finding communication (e.g., written report and oral presentation)