Matthew Sottile is a computer scientist at Lawrence Livermore National Laboratory’s Center for Applied Scientific Computing (CASC). He received his Ph.D. in Computer Engineering from the University of New Mexico, an M.S. in Computer Science and a B.S. in Mathematics and Computer Science from the University of Oregon. His technical expertise spans high-performance computing (HPC), programming languages and compilers, performance analysis, and parallel computing models for scientific applications.

At LLNL he works on several projects in formal verification for scientific and engineering applications, and compiler-based tools for program analysis and transformation. Prior to joining CASC he spent 10 years in industry working in program synthesis (Noddle LLC), data science (Sailfan Research), and R&D for correctness in scientific and high-performance computing (Galois, Inc.). From 2001 to 2008, he was a Technical Staff Member at Los Alamos National Laboratory (LANL) in both the Continuum Dynamics and Advanced Computing Laboratory groups. He helped lead the Data Driven Modeling and Analysis team performing research related to image and data analysis for multi-physics simulation validation.  As part of the Cluster Research team, he performed research related to HPC system software and performance analysis.  

He has also worked in academic settings teaching computer science (U. Oregon) and mathematics (Washington State), and has co-advised three Ph.D. students and two M.S. students. His work has earned multiple awards (including an R&D 100 Award and DOE/NNSA honors) for contributions to high performance computing and national security science.

His full up-to-date publication list can be found via ORCiD at : https://orcid.org/0000-0001-7436-5246