Values, Bayesianism & scientific practice
Whether applied Bayesian inference can avoid inductive risk, where values enter scientific practice, and whether Bayesianism can remain value-free in the relevant sense.
PhD candidate in Philosophy · University of Pennsylvania
I study how values, institutions, and modeling choices shape scientific inference—and build statistical models for concrete problems in sports, public health, and scientific practice.
Whether applied Bayesian inference can avoid inductive risk, where values enter scientific practice, and whether Bayesianism can remain value-free in the relevant sense.
How institutions police plagiarism, p-hacking, and other academic misconduct—and how enforcement can protect inquiry without allowing accusations to be weaponized against scholars because of identity or political viewpoint.
Matchup-adjusted ratings, expected-outcome models, and public-health research where prediction, interpretation, and decision-making must work together.
A public, player-trait-free baseball model that predicts the value of a batted ball, validates it across rolling held-out MLB seasons, and uses residuals to study Sprint Speed.
Read the case study →Public research on interpretable blocker and rusher ratings built from sparse, opponent-dependent tracking interactions.
Read the case study →An M.A. thesis on high-dimensional confounding, unstable county mortality rates, and residual spatial dependence.
Read the case study →Places and people
Away from the desk, I follow the NFL, college football, and baseball; cook at home and have explored more than 200 Philadelphia restaurants; track prediction markets; and bring an almost comically experimental mindset to blind tastings from my American whiskey collection.
Current work
I am a PhD candidate in Philosophy and earned an M.A. in Statistics & Data Science at Penn. I welcome conversations that cross disciplinary boundaries while staying grounded in concrete problems.