Dr. Speyer investigates high performance computational approaches to analysis of large biological datasets. The advances from the last two decades in sequencing technology have engendered new research opportunities due to a vast improvement in the quality and quantity of available data. However, in order to process this data into actionable information, it is evident that advances in two critical areas must be made. First, to confront the unprecedented amounts of data, researchers must implement approaches that exploit advances in computational hardware. Massively parallel platforms such as graphical processing units (GPUs) lend themselves well to many biological data applications where the computation is both abundant and regular. Second, new theoretical and statistical approaches, such as network analysis, can be employed to characterize the nonlinear and fine structure properties of biological systems. Dr. Speyer's interest lies in the intersection of these two areas, where the rigor of computational implementation can be made highly efficient on state-of-the-art architectures enabling novel scientific discovery.
Dr. Speyer joined TGen in 2015. After receiving his B.S. in electrical engineering from MIT, Gil worked at Xilinx, Inc. in San Jose, CA. He earned his M.S. and Ph.D. in electrical engineering at Arizona State University (ASU), researching electron transport in molecular devices. Subsequently, he developed parallel codes in various domain areas as a research scientist at Arizona State Advanced Computing Center, taught supercomputing in the ASU computer science department, and developed GPU applications at the Mayo Clinic.