معرفی
Eric Ford is a Distinguished Professor and ICDS Co-Hire in the Department of Astronomy and Astrophysics at Pennsylvania State University. His research focuses on exoplanet surveys, orbital dynamics, and astrostatistics. He is affiliated with the Institute for Computational & Data Sciences, Center for Exoplanets & Habitable Worlds, and Center for Astrostatistics. Ford earned his Ph.D. in Astrophysical Sciences from Princeton University and holds dual B.S. degrees in Physics and Mathematics from MIT. His NASA Hubble Fellowship (2006–2007) and Miller Institute Fellowship (2003–2006) underscore his contributions to theoretical astrophysics.
Education highlights include a NASA Hubble Fellowship at Harvard-Smithsonian Center for Astrophysics and a Miller Research Fellowship at UC Berkeley. His work bridges theory and observation, emphasizing radial velocity surveys, exoplanet demographics, and high-precision data analysis. Notable collaborations include the NEID and HPF radial velocity projects, and NASA's Kepler mission.
Research interests include exoplanet population studies, extreme precision radial velocity techniques, and the application of astrostatistics to exoplanet detection. His lab develops computational tools like GPLinearOdeMaker.jl and StellarSpectraObservationFitting.jl to model stellar activity and improve exoplanet characterization. Ford has led teams in analyzing Kepler data, uncovering planetary architectures and occurrence rates.
His awards include the Simons Fellowship (2020–2021), Helen B. Warner Prize (2012), and Urey Prize (2011). Advising spans PhD and master’s students involved in NEID solar observations, granulation noise mitigation, and exoplanet dynamics. Grants support projects like the NEID Earth Twin Survey and EPRV research coordination networks.
Lab affiliations include the Center for Exoplanets & Habitable Worlds and the NEID Science Team. Current work explores strategies to detect Earth-analog planets and mitigate stellar variability using 3.5 years of Sun-as-a-Star observations. Future research aims to advance data science applications in astronomy and prepare for direct imaging missions.
