I completed my Ph.D. work at Harvard University in 2014 with Lars Hernquist studying galaxy formation.
I am now a joint postdoctoral fellow at MIT and Caltech working with Mark Vogelsberger and Phil Hopkins on AGN/Quasar feedback.
My main research interests include galaxy formation and evolution, star formation, feedback processes, AGN, and numerical methods.
I did my undergrad at Cornell University, where I received a B.S. in Applied and Engineering Physics in 2008.
Most of my work during that time had an engineering focus.
I worked on the mechanical design team and the navigation subsystems team for CUSat: the winner for the Air Force NanoSat4 competition.
I spent a summer working as a Space Grant summer intern with Lee Johnson at JPL on advanced propulsion systems.
During my senior year, I worked with Joe Burns and Matt Tiscareno on image analysis from the Cassini spacecraft.
I studied the structure of the Enke and Keeler gaps in Saturn's rings -- characterizing the wavy patterns in their edges.
I did my grad work at Harvard University under the supervision of Lars Hernquist.
I studied galaxy formation using numerical simulations.
My thesis work involved running and analyzing some of the first cosmological simulations using the new simulation code AREPO.
My first AREPO related project was to understand the impact of the new hydro solver included in AREPO on the properties of galactic gas disks that formed in cosmological simulations.
After that, I explored a number of galaxy relations (stellar mass function, star formation main sequence, mass-metallicity relation, etc.) as a function of redshift in our AREPO simulations with stellar and AGN feedback included.
That work culminated in the running of the Illustris simulation -- led by Mark Vogelsberger, Shy Genel, and Debora Sijacki.
During grad school I began working on understanding the heavy element distribution in galaxies.
Heavy elements serve as tracers of gas flows, and are known to be being rapidly redistributed in merging/interacting galaxies.
I worked with Lisa Kewley on the metallicity evolution of interacting galaxies by applying idealized galaxy merger simulations to build theoretical expectations for the nuclear metallicity and metallicity gradient evolution.
I continued this work with Sara Ellison and Dave Patton by comparing our idealized merger models against observations of enhanced star formation rates and depressed nuclear metallicities as seen through the Sloan Digital Sky Survey.
Using heavy elements as a probe of galaxy formation remains one of my main scientific interests.
I am now a postdoctoral fellow splitting my time between MIT and Caltech working with Mark Vogelsberger and Phil Hopkins.
The primary focus of my research is on quasar feedback.
Our goal is to use simulations to gain a clearer understanding of how various feedback processes associated directly with black holes with high accretion rates can result in the large and rapid mass outflows seen around quasars.
Cosmological Image Pipeline
I produced a catalog of 7,000 synthetic images and 40,000 integrated spectra of redshift z=0 galaxies from the Illustris Simulation that can be used for a wide range of science topics.
The mock data products are produced by using stellar population synthesis models to assign spectral energy distributions (SEDs) to each star particle in the galaxies.
The resulting synthetic images and integrated SEDs therefore properly reflect the spatial distribution, stellar metallicity distribution, and star formation history of the galaxies.
From the synthetic data products it is possible to produce monochromatic or color-composite images, perform SED fitting, classify morphology, determine galaxy structural properties, and evaluate the impacts of galaxy viewing angle.
In our paper on this topic we derived galactic stellar mass estimates by applying the SED fitting code FAST to the synthetic galaxy products, and compared the derived stellar masses against the true stellar masses from the simulation.
We found that systematic biases exist in the photometrically derived stellar mass values that can be reduced by using a fixed metallicity in conjunction with a minimum galaxy age restriction.
Galaxy Number Density Evolution
A simple python module for calculating the cumulative stellar mass function and non-constant cumulative number density evolution of galaxy populations can be downloaded here.
Galaxy comoving number-density is commonly used to link galaxy populations across different epochs in order to infer galaxy evolution properties.
By assuming galaxy populations preserve their number-density in time, one can infer the mass, size, and morphological evolution of galaxies.
However, galaxies will not preserve their number-density in the presence of galaxy mergers or when their rank ordering is broken owing to variable growth rates.
I analyzed the evolving comoving number-density of galaxy populations found in the Illustris simulation and found that
1) Inferring stellar mass evolution via constant comoving number-density selection gives qualitatively correct results, but introduces systematic errors at the factor of 3-5 level;
2) An evolution in the median number-density of tracked galaxy populations is observed regardless of whether number-density is assigned via stellar mass or velocity dispersion;
3) The median evolution in the number-density of tracked galaxy populations is driven by both galaxy mergers and galaxy rank reordering; and
4) The significant scatter in galaxy linking methods can only be marginally reduced by using multiple galaxy properties.
We provide fits for the median non-constant comoving number-density tracks that galaxies follow in time (can be downloaded here) and encourage their use when interpreting observational data.