High Performance Cross-Matching and Computational Optics for Telescopes

Matthias A. Lee, Johns Hopkins University

We have arrived in an era where we face a deluge of data streaming in from countless sources and across virtually all disciplines; This holds especially true for data intensive sciences such as astronomy where upcoming surveys such as the LSST are expected to collect tens of terabytes per night, upwards of 100 Petabytes in 10 years. The challenge is keeping up with these data rates and extracting meaningful information from them. We present a number of methods for combining and distilling vast astronomy datasets using GPUs. In particular we focus on cross-matching catalogs containing close to 0.5 Billion sources, optimally combining multi-epoch imagery and computationally extracting color from monochrome telescope images.

Speaker Biography

Matthias A. Lee received his Bachelor of Science in Computer Science from Wentworth Institute of Technology (Boston, MA) in 2011 and completed his Masters of Engineering in Computer Science from Johns Hopkins University (Baltimore, MD) in 2014. He has spent the past 7 years as a Performance Engineering Co-Op at IBM Rational and IBM Cloudant, developing software performance testing and analysis frameworks. He is also the Technical Lead for the Corrie Health Platform which is aims to reduce hospital readmissions and improve patient outcomes. His research has focused on GPU-acceleration, Image Processing, NoSQL databases, low-power computing and performance testing. After graduation, Matthias will join Appian as the Lead Performance Engineer.