Postdoctoral Researcher, Argonne National Lab (ANL). USA/Algeria.

“Stability in Motion: Performance Characterization, Resilience, and Trustworthiness in Contemporary Workflows“

Thursday 19 February 2026

Abstract

Contemporary scientific workflows operate at unprecedented scales of complexity and heterogeneity, and while they accelerate discovery, they challenge our ability to measure, reproduce, and trust them. In this talk, we present our work on three foundational directions that aim to bring stability to this constantly shifting landscape. First, we develop performance characterization and provenance methods that allow us to observe, understand, and ultimately improve distributed and hybrid workflows. Second, we incorporate resilience as a built-in property for experiment-driven systems where failures are costly and time-critical. Finally, we explore reliability in agentic workflows, where trustworthy autonomy depends on transparent provenance and verifiable decision making. Together, these directions outline a path toward scientific workflows that remain dependable even as they evolve, adapt, and scale.

Bio

Amal Gueroudji is a Postdoctoral Appointment at Argonne National Laboratory, specializing in high-performance computing (HPC) and distributed computing. She earned her Ph.D. from Université Grenoble Alpes and conducted research at Maison de la Simulation, a collaborative laboratory involving CEA, CNRS, Université Paris-Saclay, and Université Versailles Saint-Quentin. Her work focused on integrating bulk synchronous parallel simulations with distributed task-based in situ data analysis, specifically coupling MPI codes with Dask distributed. This approach aimed to enhance the efficiency of data analytics workflows in HPC environments.

Amal holds engineering and master’s degrees in computer systems from the Higher National School of Computer Science (ESI, formerly INI) in Algeria. Her final year project involved automating CPU/GPU communications within the GPU backend of the Tiramisu Compiler, in collaboration with the Computer Science and Artificial Intelligence Laboratory at MIT.

At Argonne, she continues to advance research in workflows and data services for scientific computing, contributing to the Radix-io team. Her expertise includes high-performance computing, distributed computing, in situ analytics, task-based programming, and programming models.

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