Rick Stevens is the Associate Laboratory Director of the Computing, Environment and Life Sciences Directorate at Argonne National Laboratory, and a Professor of Computer Science at the University of Chicago, with significant responsibility in delivering on the U.S. national initiative for Exascale computing and developing the DOE initiative in Artificial Intelligence (AI) for Science.
At Argonne, he is leading the Laboratory’s AI for Science initiative and currently focusing on high-performance computing systems which includes leading a significant collaboration with Intel and Cray to launch Argonne’s first exascale computer, Aurora 21, which will pursue some of the farthest-reaching science and engineering breakthroughs ever achieved with supercomputing, as well as a partnership with Cerebras Systems to bring hardware on site to advance the massive deep learning experiments being pursued at Argonne for basic and applied science and medicine with supercompute-scale AI.
Prof. Stevens is a member of the American Association for the Advancement of Science and has received many national honors for his research, including an R&D 100 award.
Focus Areas: Systems, Bioinformatics, High Performance Computing
Stevens’ research spans the computational and computer sciences from high-performance computing, to the building of innovative tools and techniques for biological science and infectious disease research as well approaches to advance deep learning to accelerate cancer research. He also specializes in high-performance computing, collaborative visualization technology, and grid computing. Currently, he is the PI of the Bacterial / Viral Bioinformatics Resource Center (BV-BRC) which is developing comparative analysis tools for infectious disease research and serves a large user community; the Exascale Deep Learning and Simulation Enabled Precision Medicine for Cancer project through the Exascale Computing Project (ECP), which focuses on building a scalable deep neural network application called the CANcer Distributed Learning Environment (CANDLE); the Predictive Modeling for Pre-Clinical Screening (Pilot 1) of the DOE-NCI Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) project; and the Co-design of Advanced Artificial Intelligence (AI) Systems project focused on predicting behavior of complex systems using multimodal datasets.