IT Matters: Driving Research and Discovery
Kevin Boyd, associate vice president and CIO, welcomed Rob Gardner and Robert Grossman for an engaging conversation on research and high performance computing. The guests shared their perspectives on the importance and role of institutions like UChicago in supporting and driving research and discovery.
About the Speakers
Rob Gardner is a Research Professor in the Enrico Fermi Institute at the University of Chicago working on computing and analytics for big science. He is Principal Investigator of the NSF-funded SLATE project which federates Kubernetes edge clusters in the national cyberinfrastructure. He co-leads the US ATLAS Computing Facility, a collaboration of 10 universities and two U.S. Department of Energy laboratories providing computational and data services to the ATLAS collaboration at the CERN Large Hadron Collider (LHC) in Geneva, Switzerland. He leads the Scalable Systems Laboratory of the newly created NSF Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) with focus on novel data organizational methods, data delivery services and analysis platforms for the high luminosity upgrade of the LHC in 2026. His lab also builds advanced cyberinfrastructure supporting forefront scientific collaborations including the XENON, South Pole Telescope, VERITAS, LIGO and IceCube experiments.
Robert Grossman is a faculty member in the section of Genetic Medicine, as well as the chief research informatics officer for the Division of the Biological Sciences. He is also a senior fellow in the Computation Institute and the Institute for Genomics and Systems Biology. His research group focuses on bioinformatics, data mining, cloud computing, data intensive computing, and related areas. He is also the founder of Open Data Group, which has provided strategic consulting and outsourced services in analytics since 2002, specializing in building predictive models over big data. His current research is focused on bioinformatics, especially developing systems, applications, and algorithms so that large data sets of genomics data can be integrated with phenotype information extracted from electronic medical records and analyzed to deepen our understanding of diseases.