Dr. Reid is the head of genome informatics at the Regeneron Genetics Center where he leads a team developing and applying novel large-scale computational analysis tools, systems, and methods to produce and analyze large genomic data sets with the goal of making precision medicine a reality. His primary focus is on maximizing the impact that the integration of EHR-derived phenotypes and genomic data can have in providing biological insights to drive drug discovery and improve patient outcomes. Dr. Reid has worked in all aspects of large-scale genomic sequence data production and analysis, and using his background in computational physics, has been an evangelist for cloud computing in genomics and he thoughtful application of data science techniques to next-generation sequencing problems. Dr. Reid received his Ph.D. in physics from The University of Washington and his Bachelor’s degree from Harvey Mudd College. He lives in Stamford, Connecticut with his husband Jim and three cats Sabrina, Lyndon, and and Emile.
Sooner or later everyone is touched by disease. With this hard fact in mind, over the past 30 years, we at Regeneron have done everything we can to bring science to medicine and develop therapeutics to improve health outcomes for everyone. Towards this end, in 2014 we launched the “Regeneron Genetics Center” and embarked on one of the largest sequencing efforts in the world to bring the genomics revolution to drug development with the goal of making important new medicines informed by human genetics and validated using our best-in- class mouse genetics technologies. Because of the scale of sequencing needed to discover and validate the impact of rare functionally important genetic variants, we knew we would need one of the largest genomic data science efforts ever conceived to enable the RGC. Our early large-scale data strategy has already paid off with a new drug target for chronic liver disease*, but knowing that there are many more insights to be gained from our rapidly growing TB-scale data set of more than 80B data points, we turned to the Databricks Unified Analytics Platform to provide both scalability so that we can work in every disease area simultaneously, and enable users at all levels of expertise in the drug development teams to gain insights from our massive volume of diverse data. We know that having the data is just the first step, enabling drug development teams to answer questions with the data is how we are building the future of drug discovery.