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Rick L. Stevens

Associate Laboratory Director and Argonne Distinguished Fellow

Rick Stevens is Associate Laboratory Director for Computing, Environment and Life Sciences.


Rick Stevens is Argonne’s Associate Laboratory Director for the Computing, Environment and Life Sciences (CELS) Directorate and an Argonne Distinguished Fellow. He is also a Professor of Computer Science at the University of Chicago. Stevens has been at Argonne since 1982.

In these and numerous other roles, Stevens is responsible for ongoing research in the computational and computer sciences from high performance computing architecture to the development of tools and methods for bioinformatics, cancer, infectious disease, and other challenges in science and engineering. He has significant responsibility in delivering on the national initiative for exascale computing and developing the U.S. Department of Energy’s (DOE) AI for Science national initiative. Recently, he has focused on developing artificial intelligence methods for a variety of scientific and biomedical challenges.

Stevens is a member of the American Association for the Advancement of Science and has received many national honors for his research, including being named a Fellow of the Association of Computer Machinery (ACM) for his continuing contributions to high performance computing.

Stevens is currently 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 Innovative Methodologies and New Data for Predictive Oncology Model Evaluation (IMPROVE) project which is building a comprehensive framework and exascale workflow to compare deep learning models aimed at solving critical problems; and the Exploration of the Potential for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Research (RadBio-AI) project to investigate the opportunity to understand the impact of low doses of radiation on biological systems, including humans.

Previously, Stevens has led and participated in a variety of other projects: constructing integrated databases of microbial pathogens (e.g., NMDPR); automating genome and metagenome annotation (e.g., RAST, MG-RAST) to develop a minimum genome derived from Bacillus subtilis; automating the construction of metabolic models for bacteria and archaea (e.g., ModelSEED); predicting drug targets for microbial pathogens and obtaining their protein structures; using experimental feedback for refinement of modelling and simulation analysis for biomedical research; and developing a massively parallel computational drug screening pipeline and novel algorithms for creating targeted virtual small molecule libraries that outperform existing databases. All of these projects have involved undergraduate and graduate students, many who are cross trained in computer science and biology and have gone on to varied careers in academia, industry, and the AI testbed at the Argonne Leadership Computing Facility, a DOE Office of Science user facility.