Who We Are

Have worn many hats in my life. Most recently obtained MD with training in general surgery. Left medicine to follow my passion researching Epidemiologic methods and causal inference. No regrets.

My research is focused on using techniques from observational studies to obtain better information from randomized trials. In particular in trials evaluating HIV treatments.

I also have a wife and two kids I absolutely adore. In my free time I enjoy spending time with family, reading, book hunting, spending time outdoors and SCUBA diving on wrecks and in caves.

My research interests include different types of stigma (HIV, mental health, and sexual identity) and how the internalization of these feelings impacts mental health and overall well-being, particularly among people with HIV as well as the kink community.

Michael Hudgens is a professor and chair of the Department of Biostatistics at UNC-Chapel Hill and is director of the Biostatistics Core of the UNC Center for AIDS Research (CFAR). He has interest in the development and application of causal inference methods for studies of infectious diseases.

Mark is an Epidemiology PhD Student working with Steve Cole on optimal treatment timing for pregnancy and labor-related conditions, reframing statistical measures with a causal lens, and computationally assessing the causal identification conditions. Mark was an instructor for EPID700: SAS and Data Management in Fall 2023. Prior to UNC, Mark graduated from NC State with two degrees in Computer Science and Science, Technology, and Society.

Taylor Krajewski is a biostatistics Ph.D. candidate at the University of North Carolina (UNC) at Chapel Hill and a graduate research assistant for the UNC Center for AIDS Research (CFAR) Biostatistics Core. In her role with the CFAR, she provides statistical support to infectious disease researchers throughout their research efforts, closely collaborating with scientists and researchers to develop new statistical theory to enhance analyses and further discoveries. She works as an adjunct instructor, teaching biostatistics courses to graduate students from other disciplines, and she has founded several mentorship programs to provide (bio)statistics students with real-world experience and to support students from historically underrepresented groups in starting their (bio)statistics graduate careers.  Her dissertation focuses on developing causal inference methods in the presence of interference to further infectious disease policy analyses.

I am a Ph.D. candidate in the Department of Biostatistics at the University of North Carolina at Chapel Hill, advised by Profs. Michael G. Hudgens and Donglin Zeng. My research interests lie in causal inference, interference, network (clustered) data, precision medicine, statistical machine learning, and semiparametric efficiency theory.

Specifically, I focus on causal inference under interference and its efficient estimation using data adaptive methods (i.e., machine learning) based on semi-parametric efficiency theory and Gaussian process. A standard assumption in causal inference is no interference between units, which supposes that a unit’s treatment does not affect the outcome of other units. However, this assumption might be unrealistic in some circumstances. Based on semi-parametric efficiency theory and Gaussian process, efficient and robust estimators of causal quantities under interference are developed.

I received B.S. in Statistics and Mathematics from Seoul National University and have worked at the Causal Inference Research Lab (UNC) and Center for AIDS Research (UNC), working on grant writing, statistical data analysis, designing and training machine learning models, using various software including R, SAS and Python.