As exoplanet science moves towards bigger, noisier datasets, smaller signals, and more complicated physical models, statistical inference is becoming more and more essential. In this talk, I’ll give a tour of my group’s work applying cutting-edge statistical methods in order to: 1) push the limits of exoplanet detection methods-- particularly radial velocity and astrometric methods-- expanding our understanding of what types of planets can exist;  2) look for patterns across populations of planets, informing theories about how planets form and evolve; and 3) maximize the utility of future data, particularly from the Gaia, Roman, and Habitable Worlds Observatories. I will also discuss my work on open-source software, highlighting the widely-used Bayesian orbit-fitting package orbitize! and the summer school Code/Astro.

This lecture was made possible by the William C. Ferguson Fund.