I use a number of modeling and machine learning techniques to solve difficult problems. I have recently been engaged in a number of projects, including:
- Designing and analyzing experiments focused on improving sales conversions and business operations;
- Developing automated computer vision models for image segmentation, feature detection, and optical character recognition (using OpenCV and Tesseract); and
- Building predictive recommendation models for online sales engines (employing hierarchical and PAM clustering, gradient boosting, and Bayesian hierarchical modeling).
That's my day job. I also maintain an academic research agenda, focused on models for network analysis, particularly latent space models and advanced exponential random graph models, as well as the computational and implementational aspects of estimating these models. I have also done research on causal inference in the context of natural experiments (instrumental variables and regression discontinuity designs).
I earned my Ph.D. at OSU, focusing on applied political methodology and comparative politics. Prior to being at OSU, I earned an M.A. in political science from Boston College and my B.A. in economics and German studies from Lewis & Clark College.
- How Strong is Strong Enough? Strengthening Instruments through Matching and Weak Instrument Tests (with Luke Keele). Annals of Applied Statistics. Abstract.
- Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model (with Jan Box-Steffensmeier and Dino Christenson). Forthcoming in Political Analysis. Abstract.
- Web Timing Attacks Made Practical (with Tim Morgan). Blackhat 2015. Abstract. Video.
- Inferential Network Analysis (with Skyler Cranmer and Bruce Desmarais). Under contract with Cambridge University Press.
- dynnet. An R package providing an alternative implementation latent space models for static and dynamic network models. Meant as a test-bed for exploring new ideas.
- boolean3. A reimplementation of the boolean package in R. Provide multiprocessor support, improve performance, extended capabilities.
- iv_sens. An implementation of Rosenbaum's instrumental variable sensitivity analysis for causal inference. Available in the rbounds package for R (with Luke Keele).