About Me

I am an assistant professor of astronomy & astrophysics at The Pennsylvania State University. My research interests focus on understanding the processes of galaxy formation through the use of modern/deep galaxy surveys, statistics, and machine learning.

In particular, I specialize in fitting flexible models to galaxy photometry and spectra, in building and exploring analytical and theoretical models of galaxy evolution, and in using astrostatistics and machine learning to better understand the universe. I've harnessed millions of hours of supercomputer time running specialized code to build a more complete picture of how galaxies form and evolve. I've collaborated on a wide range of projects ranging from understanding the stellar origins of kilonova explosions to developing new methods for wide-field infrared galaxy surveys. I am very lucky to have been afforded the privilege of asking the big questions of the Universe!

Some of my current active projects include the Prime Focus Spectrograph galaxy survey , searching for first-light galaxies in the deep JWST survey UNCOVER , analyzing high signal-to-noise spectroscopy of galaxies at 'cosmic noon' in the Blue Jay survey with JWST, and developing software to analyze galaxy spectra with JWST. I'm also an active member of the Institute for Gravitation and the Cosmos and the Institute for Computational and Data Sciences at Penn State.

My Research Group

Current Group Members

My current group members (August 2022) are below:

Bingjie Wang (postdoc)

Yijia Li (graduate student)

Elijah Mathews (graduate student)

Junyu Zhang (undergraduate student)

Former Group Members

Group members who have ascended to other positions.

Will Bowman (current postdoc at Yale University)


Simple Models of Galaxy Assembly

I started my Ph.D in 2010 at Yale University with Professor Pieter van Dokkum. This was an exciting time to be studying galaxy evolution. We were in the middle of the first wide, deep, and unbiased (mass-selected) surveys of galaxy evolution at "cosmic noon", spear-headed by ambitious programs with the Hubble Space Telescope such as 3D-HST and CANDELS. As a result, astronomers had now surveyed the vast majority of the galaxy population over the majority of cosmic time for the very first time. My advisor and I thought that it was now the ideal time to "put it all together" and write down how galaxies grew up in a simple analytical model. This was the beginning of my thesis.

2013: Processes affecting galaxy evolution at a constant number density

However, after constructing and testing our analytical framework, we found a serious problem with this plan. Applying our model to two independent measurements of galaxy mass growth (the current rate of star formation and the current mass locked up in stars) resulted in galaxy growth rates which were systematically offset by a factor of two. This was a big problem. To put it in persepctive, our model suggested that a typical ancestor of the Milky Way would grow by a factor of 4-16 from z~2, a confidence interval so large as to be almost useless!

2015: Even after changing the SFR(M), observed masses and SFRs disagreed

A New Method to Measure Galaxy Properties

The problem lay in the models we used to convert galaxy observations into galaxy properties. While these models had had many successes over many years, they were not quite precise enough to answer the questions we needed to answer. We identified three specific problems:

  • The models did not include enough of the relevant physics, e.g. emission from black holes or nebulae.
  • The assumptions built into them were not physically self-consistent.
  • The models needed to use more clever statistics to translate the weak observational constraints into very broad parameter estimates
Over the end of my Ph.D and during my postdoctoral research, I worked closely with stellar populations experts Professor Charlie Conroy and Dr. Ben Johnson at the Center for Astrophysics | Harvard & Smithsonian to develop a new model addressing these problems: Prospector-α, built within the Prospector Bayesian inference framework.

2017: Here Prospector fits our new galaxy SED model Prospector-α to photometry

A New Cosmic Consensus

We applied this new Prospector-α model to a mass-complete sample of 58,461 galaxies across 0.5 < z < 2.5 from the 3D-HST survey. These new inferences lowered the observed cosmic star formation rate density by ∼0.2 dex and increase the observed stellar mass growth by ∼0.1 dex, finally bringing these two measurements into agreement and implying an older, more quiescent Universe than found in previous work.

2019: A new consensus in the older, more quiescent Universe inferred by Prospector-α


In additional to my professional mentoring, I believe that knowing more about science and the scientific process improves the lives of everyone (and -- talking to folks about it is lots of fun!).

Flipped Science Fair

I host an annual reverse science fair, where professional researchers present their research to students from local elementary and middle schools. The students serve as “judges” and announce a winner at the end of the event. This encourages and develops outreach skills among researchers while simultaneously engaging the middle school students in a critical form of active learning.

Visiting URJ 6 Points Sci-Tech Academy

Giving talks to middle schoolers at science summer camp about my thesis research, and talking to them in their classrooms!


My office is Davey Laboratory 515. You can reach me electronically at (firstname).(lastname)@psu.edu.