I am an Astronomy and Astrobiology PhD Candidate at the University of Washington. I am interested in the intersection of statistics, data science, and Astronomy. I like to work on problems where computational methods can streamline efficiency and improve result quality.
My thesis work revolves around novel computation methods for detecting very faint moving objects in existing datasets. The KBMOD algorithm (https://github.com/dirac-institute/kbmod) is a “digital tracking” or “track before detect” algorithm that uses massively-parallel GPU-accelerated statistical methods to detect faint Solar System objects in stacks of images. Candidate trajectories are then analyzed using a myriad of filtering techniques ranging from clipped mean filtering to DEEP learning. KBMOD is written in CUDA, C++, and python.
I am a research astronomer whose main task is “make LSST find asteroids” — i.e., solve the asteroid linking problem in exciting new ways. I’m also interested in variable stars and Kuiper Belt objects, and in asteroid populations (particularly at very small sizes), collisional families, and asteroids with weird orbits or spin states. I love astronomical observing, looking at fresh data, and working on novel methods to squeeze all the science out of challenging data sets.
Rebecca Phillipson is a postdoctoral scholar working with Dr. Eric Bellm in the UW Department of Astronomy. Dr. Phillipson’s primary research interests are in the timing variability of accreting black holes and neutron stars using data from instruments onboard satellites such as the Rossi X-ray Timing Explorer, Swift, Kepler, and TESS in combination with ground-based optical observatories for spectroscopic follow-up. She is currently developing methods that accommodates unevenly sampled time series to study optical timing data from the Zwicky Transient Facility of unique compact binary systems. Dr. Phillipson employs methods from nonlinear dynamics and chaos theory for comparison to traditional Fourier-based techniques and Bayesian statistics as a means to create a new and complementary classification of transient and variable astrophysical systems. Her side projects include developing radiation-hydrodynamic simulations of the accretion disks around black holes and exploring interdisciplinary applications of novel time series analysis methods.
Born in Poland, graduated with MPhys Physics in 2012 (University of Oxford), obtained a PhD in Astronomy and Astrophysics in 2019 (University of Washington). Pursuing research in Astrophysics, specializing in the analysis of time series data, using the variability information to classify and characterize quasars and variable stars. Since 2020 working within the Active Optics System group as a commissioning postdoc for the Rubin Observatory (LSST).
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.
I currently work for Rubin Observatory as the Lead Community Scientist for the Community Engagement Team and as a Science Analyst for the Data Management team. My main research focus is supernovae, especially those of Type Ia.
I’m a Professor of Applied Physics at Olympic College, with a background in Astronautical Engineering and Space Physics, and as a Collaborator with the UW’s Rubin Observatory efforts since 2009.
Joachim Moeyens is a graduate student in the Department of Astronomy at the University of Washington. He is interested in big data and software driven solutions to problems in astronomy. During his undergraduate studies at the University of Washington, he was presented with the opportunity to work on a research project for the Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST). For his doctoral thesis, Joachim is working on algorithms that discover minor planets in astronomical surveys, in particular, on Rubin Observatory’s Solar System Processing pipelines, and on a novel algorithm named Tracklet-less Heliocentric Orbit Recovery (THOR).
I am a Postdoctoral Fellow at the DiRAC Institute and a UW Data Science Postdoctoral Fellow at the UW eScience Institute. I am a member of the Rubin Observatory Commissioning team as well as a member of two LSST Science Collaborations: the LSST Solar System Science Collaboration (LSST SSSC) and the LSST Dark Energy Science Collaboration (LSST DESC) where I am the co-convener of the Cosmological and Survey Simulations Working Group.
My scientific interests focus on creating new tools and methods to study large, complex datasets like the LSST through simulations, machine learning and high performance computing. These interests take me across a range of astronomical topics from detecting the faintest asteroids in our Solar System to measuring the distances to far-off galaxies.