Zachary Slepian

Assistant Professor


Office
Phone
Fax
Email

302 Bryant Space Science Center
(352) 294–1851
(352) 392–5089
zslepian@ufl.edu

Homepage
http://www.astro.ufl.edu/~zslepian

Publications

Educational Background
  • PhD, Astronomy & Astrophysics, Harvard, 2016
Areas of Specialty
Observational and theoretical cosmology

Research Interests
Dark energy, dark matter, structure formation, large surveys, analytic methods, star formation

Biography
Originally from Fairfield, Connecticut, an early interest in philosophy led to my current interest in cosmology. I attended public high school, received a BA summa cum laude from Princeton (2011), working with J. Richard Gott, III on my senior thesis, an MSt in philosophy of physics at Oxford (2012), and a PhD in Astrophysics (2016) from Harvard, advised by Daniel J. Eisenstein. During my PhD, I focused on Baryon Acoustic Oscillations (BAO) in the 2-point and 3-point correlation function (3PCF) of galaxies, constraining a possible systematic sourced by high-redshift baryon-dark matter relative velocities using the 3PCF. This entailed developing a transformatively fast 3PCF algorithm, enabling the first high-significance detection of BAO in the 3PCF and a measurement of the cosmic distance scale six billion years ago to percent precision. Post-PhD, I spent one year as a Chamberlain Fellow and one year as an Einstein Fellow at Lawrence Berkeley National Laboratory, where highlights included an implementation of the 3PCF algorithm capable of computing the 3PCF for the entire observable Universe in 20 hours on NERSC's Cori supercomputer, application of the 3PCF to MHD turbulence, and novel analytic solutions for the Friedmann equation in the presence of neutrinos or warm dark matter. My current research follows three broad paths: creating theoretical models for large-scale structure, designing fast algorithms to measure it, and applying them to datasets such as BOSS, eBOSS, and DESI. Cutting across these areas are a strong attraction to analytic methods and excitement about effective use of high-performance computing.