... for a project entitled Scalable and Resilient Modeling for Federated-Learning-Based Complex Workflows.
Guanpeng Li is an Assistant Professor in the Department of Computer Science at the University of Iowa. His research interests are in the areas of HPC Fault Tolerance, Lossy Compression, Safety of Autonomous Driving Systems, Machine Learning Dependability, and Approximate Computing.
Overview
“Scientific research is getting more complex and will need next-generation workflows as we move forward with larger data sets and new tools spread across the U.S.,” said Ceren Susut, DOE Acting Associate Director of Science for Advanced Scientific Computing Research. “This program will explore how science can be conducted in this new environment – where tools and data are in multiple places but must be integrated in a high-performance fashion.” [Source: DOE press-release]
Li's project will be a collaboration with Xiaoyi Lu - University of California, Merced and Sheng Di - Argonne National Laboratory, aiming to improve our understanding of scalable, federated, privacy-preserving machine learning; in response to the reality that "the computational workflows associated with modern science are becoming increasingly complex, often processing an astounding amount of data generated by geographically distributed instruments. [...] The critical nature of these distributed workloads requires advancements to the science of resilience of distributed systems."
More information forthcoming and at this Merced announcement of this collaborative work.
This five-year project has been awarded $4,350,000 - including $850,000 for Li.
