Computer Scientist at Oak Ridge National Laboratory

“Data management strategies for large scale workflows coupling simulations to AI“

Thursday 19 February 2026

Abstract

Computer Scientist (co-lead of the Data Understanding thrust in SciDAC RAPIDS and lead of the Self-improving AI models thrust in The Transformational AI Models Consortium (ModCon))

The next generation of HPC application is represented by hybrid approaches that weave together traditional simulations and modern AI. However, a critical bottleneck in integrating HPC with AI is the “lack of awareness” between workflow components. The outputs of HPC applications are often analyzed only sparingly before archival, effectively becoming inaccessible for future training codes due to the manual, time-consuming processes of finding, and processing datasets for each analysis purpose, frequently outweighing the cost of re-running simulations. This fragmentation results in complex, brittle workflows where data management is treated as an afterthought. In this presentation, we propose a unified framework for managing the complex lifecycle of data in hybrid AI-HPC systems. We will address the limitations of current domain-specific solutions by introducing abstractions that map the relationships between raw simulation outputs, processed training sets, and surrogate model inference. By creating a system where data provenance and transformation history are persistent, we enable workflows that “learn” from previous executions. Attendees will learn how to design workflows that minimize redundant processing, facilitate cross-domain optimization transfer, and ensure that the massive datasets required for AI training remain accessible, structured, and reusable.

Bio

Dr. Ana Gainaru is a Research Scientist at Oak Ridge National Laboratory’s Computer Science and Mathematics Division. Her research addresses performance optimization, data management, and the advancement of AI-driven workflows for large-scale scientific applications. She currently leads research thrusts for the RAPIDS SciDAC project and the Transformational AI Models Consortium in the Genesis project. Additionally, she serves on the leadership team for the UT-ORII initiative, where she sits on the ORNL hiring board and mentors research focused on the development of surrogate models for fusion.

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