Hyperparameter Optimization for Deep Learning Models Using High Performance Computing

by Eric Wulff (CERN)

Wednesday 23 Jul 2025, 13:30 → 15:00 Europe/Zurich

31/3-004 - IT Amphitheatre (CERN)

Abstract

In the past decade, Machine Learning (ML), and in particular Deep Learning (DL), has outperformed traditional rule-based algorithms on a wide variety of tasks, such as for instance image recognition, object detection and natural language processing. In CoE RAISE, we have additionally seen that ML can unlock new potential in fields such as high energy physics (HEP), remote sensing, seismic imaging, additive manufacturing, and acoustics. Training DL models, however, is no trivial task, especially if the model is large and have many tunable hyperparameters. To tackle this challenge, Hyperparameter Optimization (HPO) can be used to systematically explore the search space of possible hyperparameter configurations and, paired with the computing power of modern High Performance Computing (HPC) systems, it can drastically speed up the process of improving DL models. The aim of this talk is to give an introduction to HPO and the major challenges data scientists face when tuning their models, as well as to give some examples from a HEP use-case where large-scale HPO on HPC systems was successfully applied.

Bio

Eric Wulff is a data scientist and machine learning engineer in the Frontier Technologies and Initiatives group in the CERN IT department. He specializes in developing and applying AI models to address complex scientific and technical challenges, leveraging large-scale high-performance computing (HPC) systems for distributed training and hyperparameter optimization. As part of CERN openlab’s management team, he provides strategic guidance on the convergence of AI and HPC in openlab R&D projects.
Eric holds an MSc in Engineering Physics from Lund University. Before joining CERN, he worked as a machine learning engineer, focusing on real-time object detection and video analytics using deep learning on edge processors.

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Eric_Wulff_OPL_Lecture_2025.pdf

Recording