I am currently a postdoctoral fellow in the department of Epidemiology at the Johns Hopkins Bloomberg School of Public Health. I received my PhD in Epidemiologic Science at the University of Michigan with certification in Computational Discovery and Engineering. Most recently, my research focuses on the use of mathematical modeling to develop a better understanding of tuberculosis prevention and transmission dynamics. In the near past, my research utilized agent-based modeling, networks modeling, micro-simulation, and multi-level statistical methods to explore and disentangle the complex feedback mechanisms contributing to smoking behavior. From a purely methodological standpoint, I am also interested in the application of natural language processing and supervised/unsupervised machine learning methods in epidemiologic research.
My non-researcher persona is a born and bred, maize and blue bleeding Michigander, a moral and political philosopher, and a lazy maybe-athlete that can do a good amount of pull-ups. When I’m not working, you can typically find me at the local climbing gym, hanging out with my shelter mutt (Kai), playing video games, eating food, napping, or traveling for the dual purpose of eating food/climbing. Once in a while, I also also enjoy drinking beer / wine. My favorite beers include a good hoppy IPA and the occasional Natty Lite (because no one likes a snob). My favorite wines are red and somewhat snobby 🙂 I am broadly interested in all things computing ranging from networks, server administration, hardware, and cluster computing to user interface, design, and programming.
Working in research as a methodologist/epidemiologist/computational modeler has been a great way to meet a lot of very smart people and work on intellectually challenging projects across a variety of disciplines. I’m always excited by new ideas, people, projects, and endeavors, so please feel free to e-mail me (scherng[at]jhu[dot]edu) if you’d like to get in touch.