DERI Seminar with Enrico Camporeale, Senior Lecturer in Digital Environments
When: Thursday, March 12, 2026, 11:00 AM - 12:00 PM
Where: Teams
Speaker: Dr Enrico Camporeale, senior lecturer in Digital Environments at QMUL
Title: Machine Learning for Space Weather and the challenge of rare and extreme events
Space weather refers to the conditions in near-Earth space driven by solar activity—solar flares, coronal mass ejections, and geomagnetic storms—that can disrupt satellites, radio communication, navigation systems, and power grids. In recent years, the field has been revolutionized by the rapid improvement in data-driven and machine learning based forecasting. Yet the events that matter most for operational decision-making are precisely those that occur least often. This creates a core scientific challenge: our models must anticipate the rare and the extreme, even though the historical record is dominated by quiet days.
Classical machine learning methods struggle in this setting. In regression tasks with strongly imbalanced target distributions, “more data” often means “more of the same,” offering limited benefit. The data that capture extremes are far more informative than abundant but redundant samples.
In this talk, I will give an overview on recent advances of machine learning in the broad field of space weather and space physics. Emphasis will be given on three complementary methods that address the imbalanced regression challenge. ACCRUE (Accurate and Reliable Uncertainty Estimate) is a model-agnostic post-hoc technique that converts deterministic models into probabilistic ones with calibrated and trustworthy uncertainties. PARIS (Pruning Algorithm via the Representer theorem for Imbalanced Scenarios) applies influence-aware sample attribution to identify and remove redundant or counterproductive data, improving performance specifically on rare events. ProBoost (Probabilistic Boosting) integrates the two ideas, forming an uncertainty-weighted ensemble in which each model contributes proportionally to its calibrated confidence.
Although motivated by space weather prediction, these approaches are broadly applicable to any domain where rare or extreme events—not the majority of the data—drive the real-world risk.
Bio: Enrico Camporeale is a senior lecturer in Digital Environments in the School of Physics and Chemical Sciences at Queen Mary University of London, and a DERI academic fellow. His research focuses on the use of machine learning in space physics and space weather and the synergy between physics-based simulations, machine learning, and uncertainty quantification. He is the founding editor in chief of the American Geophysical Union (AGU) Journal of Geophysical Research: Machine Learning and Computation, and the chair of the Machine Learning in Heliophysics conference (ML-Helio).
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Microsoft Teams meeting
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