Statistically speaking, I'm a Bayesian-leaning pragmatist with an interest in epidemiology, human rights, policy, and just about any other interesting questions (or data) you're willing to throw my way. I'm particularly interested in data visualization and scientific writing.
My academic background is in applied statistics and biochemistry. I received my master's degree in Statistical Practice from Carnegie Mellon University. Before that, I studied biochemistry at UCLA.
I've worked on a variety of interdisciplinary research teams, including at the Southern California Injury Prevention Research Center, Gryphon Scientific, The New York Times, and, most recently, at patientslikeme. I enjoy using statistics and machine learning to solve real-world problems, ranging from trying to understand how people read and understand the news, to modeling infectious and chronic diseases.
In my free time, I enjoy cooking, climbing, running, homebrewing, ultimate frisbee, and drinking entirely too much coffee.