Hello, I'm Dillon.
Cosmologist. Astrophysicist. Python Enthusiast.
Hello, that's me!
I'm a 28 year old PhD student at University of Pennsylvania in Astrophysics and Cosmology and a 2019 NASA Hubble Einstein Fellow. I'm driven to understand the dynamics of the cosmos and the nature of Dark Energy.
I work with extremely large datasets on a regular basis and imploy state of the art machine learning, statistical tools, and image analysis to make statements about the universe.
I am also passionate about analysis, vizualization, and manipulation of large datasets across other disciplines.
Dark Energy Properties
I've led the recent analysis of the Dark Energy Survey Supernova Program's first 3 years of data in the measurement of cosmological parameters including the equation of state for dark energy. Our results are in agreement with a cosmological constant and make a significant leap forward in suprnova analysis and data quality. This has involved developement of a "forward modeling" photometric measurement pipeline and the implementation of the final cosmologial parameter analysis. Looking ahead we will perform the single largest analaysis of photometrically classified cosmologically useful supernovae for a state of the art dark energy measurement
Gravitational Wave Cosmology
I work as part of the Dark Energy Survey Gravitational Wave (DESGW) followup team which optimizes use of the Dark Energy Camera for detection of GW Sources. Once found (or with probability of detection) we can use the distance and redshift to obtain an estimate for the expansion rate of the universe today.
Large Datasets, Data Analysis/ Visualization
I work on a daily basis with the Dark Energy Survey database of over 4 petabytes of images. Data reduction, manipulation, transfer, and precision anlysis using state of the art statistical tools. Develop custom algorithims for simulating and making measurements of the astrophysical data.Extensive experience with high performance computing on clusters. Parallelization on both CPU and GPU clusters. Additionally worked in efficiency optimization and large file data transfer techniques.
Machine learning and Neural Nets
I employ a number of machine learning algorithms in my daily work, specifically neural networks to perform object classification and detection. I have extended deep learning neural networks on datasets outside of astrophysics.
Itinerary & Past Talks
Colloquium @ Service de Physique Théorique
Université Libre de Bruxelles