I completed my Ph.D at the University of Pennsylvania, focusing on the development and application of new techniques for the discovery and characterization of the most distant bodies in our Solar System, trans-Neptunian objects, as part of the Dark Energy Survey (DES). At the University of Washington, I am expanding this research to current surveys, as well as upcoming projects such as the Rubin Observatory’s Legacy Survey of Space and Time (LSST).
Discovering these objects is inherently a computationally expensive task, requiring these bodies to be tracked across multiple telescope images during several years of observations, as these move against the background of stars in the night sky. My research focuses on the development of computationally efficient and effective algorithms to enable such discoveries. I am also interested in the relationship between models of formation of the Solar System and the population of trans-Neptunian bodies, and I am carrying studies of photometric colors as well as comparisons of theoretical populations to observed data.

I am interested in problems and projects that are influenced by and require large-scale computing resources and knowledge of advanced statistical techniques. Usually anything that requires me to write a novel piece of code will pique my interest. While a graduate student at the University of Washington, I’ve worked on building a science platform for performing distributed computing in the cloud with an intent to make feasible the analysis of terabytes of catalog data from the Zwicky Transient Facility [1], I’ve developed software that integrates JupyterHub and Jupyter notebooks with the checkpoint/restore functionality of the Podman container software [2], I’ve developed pipelines for processing image data from the Dark Energy Camera to search for solar system objects, and I’ve worked on developing methods using e.g. principal component analysis for predicting the metallicity of RR Lyrae stars from their light curves. With the Statistical Sciences group at Los Alamos National Laboratory, I’ve helped evaluate the effectiveness of approximate Gaussian process models for building statistical emulators of expensive computer simulations.mical data sets in the Cloud.

John Franklin is a graduate student in the Physics Department, and a member of the Dark Energy Science Collaboration (DESC) and the Informatics and Statistics Science Collaboration (ISSC) of the Vera C. Rubin Observatory. He is interested in developing machine learning methods to solve interesting problems in cosmology, such as photometric redshift estimation, deblending, and lensing. Some of the overarching themes of this research are forward modeling, reconstructing high-resolution information from low-resolution data, and incorporating physical information into the structure of machine learning models.

Lynne Jones is the LSST Performance Scientist, working with Rubin Observatory. She is currently working on the optimization of the LSST survey strategy. She studies small objects throughout the Solar System, with a particular interest in surveys for distant TransNeptunian Objects and lightcurve properties of asteroids. She is currently located in Victoria, BC.