Rick Stevens

Rick Stevens is a professor at the University of Chicago and an Associate Laboratory Director at Argonne National Laboratory. He is internationally known for work in high-performance computing, collaboration and visualization technology, and for building computational tools and web infrastructures to support large-scale genome and metagenome analysis for basic science and infectious disease research. He is also recognized for his role in developing the national initiative for exascale computing, and for AI-based cancer research that is defining exascale computing requirements. Stevens teaches and supervises students in the areas of computer systems and computational biology.
Stevens is a principle investigator for the NIH/NIAID supported PATRIC Bioinformatics Resource Center, which is developing comparative analysis tools for infectious disease research and serves a large user community. He is also the PI of 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 code called the CANcer Distributed Learning Environment (CANDLE) to address three top challenges of the National Cancer Institute. Stevens plays a central role in the Exascale Computing Project, a collaborative effort of two DOE organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, in support of the nation’s exascale computing imperative. He is also one of the PIs for the DOE-NCI Joint Design of Advanced Computing Solutions for Cancer project, part of the Cancer Moonshot initiative. In this role, he leads a pilot project on pre-clinical screening aimed at building machine learning models for cancer drug response that will integrate data from cell line screens and patient derived xenograft models to improve the range of therapies available to patients. In addition, Stevens is a co-principal investigator on a multi-institutional effort to study the effects of traumatic brain injury (TBI). This study aims to bring machine learning and deep learning to bear on predicting future outcomes as well as classifying TBI to aid in diagnosis.
Over the past twenty years, he and his colleagues have developed the SEED, RAST, MG-RAST and ModelSEED genome analysis and bacterial modeling servers that have been used by tens of thousands of users to annotate and analyze more than 250,000 microbial genomes and metagenomic samples. He was a PI on the DOE Systems Biology Knowledgebase, creating a software and data platform designed to meet the grand challenge in systems biology of predicting and designing biological function.
At Argonne, Stevens leads the Computing, Environment and Life Sciences (CELS) Directorate that operates one of the top supercomputers in the world (a 10 Petaflops/s machine called MIRA). The directorate is home to several user facilities and joint institutes, including the Argonne Leadership Computing Facility, one of two DOE Leadership Computing Facilities in the nation dedicated to open science, to accelerate the pace of discovery, address complex national challenges, and deliver future exascale computing capabilities. Prior to that role, he led the Mathematics and Computer Science Division for ten years and the Physical Sciences Directorate.
Stevens and his group have won R+D100 awards for developing advanced collaboration technology (Access Grid). He has published over 200 papers and book chapters and holds several patents. He has testified on a Congressional Subcommittee on Energy hearing in response to a bill to improve the HPC research program of the Department of Energy. In late 2017, Stevens was recognized by the HPC community by winning the prestigious 2017 HPCwire Readers’ and Editors’ Choice Awards, for developing and using deep learning tools to accelerate cancer research. Also in 2017, he was a recipient of the DOE Secretary Appreciation Award, for his superlative contributions in predictive oncology that push computational requirements for exascale computing, and for his timely response to the 2014 Ebola outbreak.