Part of the TPC Seminar Series
Speaker: Rick Stevens, Associate Laboratory Director, Argonne National Laboratory, Computing, Environment and Life Sciences Directorate and The University of Chicago
Date: Tuesday November 28, 2023
Time: 7:00 A.M. to 8:15 A.M. (Central Time)
Location: Virtual
Abstract:
The inaugural talk will be from Prof. Rick Stevens (Argonne National Laboratory and The University of Chicago), who will discuss Argonne’s “AuroraGPT” project, one of several such large-scale AI models for Science that are being undertaken by groups involved in TPC.
Biography:
Rick Stevens is a Professor of Computer Science at the University of Chicago as well as the Associate Laboratory Director of the Computing, Environment and Life Sciences (CELS) Directorate and Argonne Distinguished Fellow at Argonne National Laboratory. In these, and in numerous other roles, he 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. Recently, he has focused on developing AI methods for a variety of scientific and biomedical problems, and also has significant responsibility in delivering on the U.S. national initiative for Exascale computing and the Argonne AI for Science initiative.
Currently, Stevens 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) and recently earned a 2023 R&D100 Award; 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 that are 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 of understanding the impact of low doses of radiation on biological systems, including humans.
Stevens is a Fellow of the American Association for the Advancement of Science and a Fellow of the Association of Computer Machinery (ACM).