You can find information about our research scientists below. Click on a name to go to an individual’s profile page.
Note: email addresses are @astro.washington.edu unless otherwise specified.
I am a software developer working on problems at the intersection of software engineering, science, and statistics. I currently work on the Large Synoptic Survey Telescope’s alert production pipeline, a complex framework for turning LSST’s flood of raw data into a flood of science within 60 seconds. I’m involved in a variety of projects for the pipeline, including developer infrastructure, objects and interfaces for managing astronomical coordinate transformations, and a fail-resistant framework for pipeline verification.
I currently work with the LSST Data Management team as a Project Science Analyst. My main research focus is supernovae, especially those of Type Ia.
Planetary atmospheres and dynamics of exoplanets. Applying machine learning to large astronomical data sets.
White dwarf stars are the stellar remains of 98-99% of stars in the sky. Anjum chose to work on pulsating white dwarfs in particular because pulsations allow us to probe deep in the interior of the star, not otherwise accessible for a systematic study. A unique model fit to the observed periods of the variable white dwarf can reveal information about the stellar mass, core composition, age, rotation rate, magnetic field strength, and distance. In collaboration with Dr. Paula Szkody, Anjum also works on accreting white dwarfs that show pulsations. These systems are of great interest to both the pulsating white dwarf community and the cataclysmic variable community.
I am a software developer working on data processing software for the LSST project. I also spent many years working on control software for Apache Point Observatory.
I started mining data in SDSS, and am now am digging in the LSST codebase, looking for beautiful gems of transient knowledge as part of the UW LSST Alerts Production team. Our goal: to find all the things that go bump in the night. Prior to this, I measured galaxy clustering in SDSS and BOSS, and studied galaxies and the black holes that love them for my thesis.
I’m currently working with the LSST’s data management group on the alert production pipeline. In graduate school I studied galaxy evolution and formation through studies of faint gas and stars in galaxy outskirts from deep optical and radio data. My dissertation focused on characterizing star formation in galaxy outer disks and the role of accretion of gas and faint companions in galaxy evolution. As a postdoc, I worked on data intensive analysis pipelines in the cloud before returning to astronomy to work on the LSST transient alert stream.
I am a software engineer for Sloan Digital Sky Survey, Apache Point Observatory, and Las Campanas Observatory. I’m involved in a variety of projects with a focus on telescope and instrument control, and user interfaces. When I’m off-campus, I try to end up in the mountains aiming a pair of skis, a kayak, a mountain bike, or a telescope (trying very hard not crash).
Senior Research Scientist
Jennifer is a participant in the Sloan Digital Sky Survey IV (SDSS-IV) and serves as the Deputy Project Manager for one of its cornerstone projects, the Apache Point Galactic Evolution Experiment 2 (APOGEE-2). Her research is centered on the chemical composition of stellar populations as well as the chemical evolution of various Galactic components. She is also interested in stellar astrophysics and the use of fundamental physics data to improve the derivation of stellar parameters. As a member of a large-scale data project, Jennifer is keen to develop efficient data extraction and utilization techniques. She also attempts to search for patterns and correlations in data.
I work for the APOGEE south survey on the infrastructure side of things. For information on the survey visit the SDSS website.
Peter Yoachim is a staff scientist working with LSST on issues of telescope scheduler optimization and calibration. Scientifically, I work on galaxy formation and evolution, particularly using IFU observations to measure galaxy dynamics and star formation histories.