I am a Quantitative Methods post-doc at University of California, Davis. My passion is building new models and extending old ones to improve predictions, decision making, and inferences. Unique questions deserve unique models.
I specialize in Bayesian model building and assessment. Broadly, Bayesian generative models let you build the model that your problem and process deserves. In particular, I currently work on generalized mixed effects location scale models, latent factor models, nonparametric predictive models, and any combination therein. These approaches can drastically improve predictive performance and insights. For example, variance submodels allow you to predict more than just outcomes, but the certainty and volatility of those outcomes. They also make models more robust by automatically weighing noisy data less, and by guarding against overconfidence in inferences.
My research, statistical, and programming background work in concert to solve statistical and prediction problems. I want new challenges, and I enjoy independently learning and developing new tools and methods. Hire me as your next data scientist or statistical consultant.