I've got a paper accepted at IJCNN 2026

Last year, I learned a good deal about our ubiquitous little friends, the neutrinos. So, naturally, this is a 📢 Publication Alert!

Our paper, “Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers,” coauthored with Gregory Lehaut, Antonin Vacheret, Frederic Jurie, and Jalal Fadili, from Greyc and LPC Caen, has been accepted at the International Joint Conference on Neural Networks (IJCNN). This year, it is part of the World Congress on Computational Intelligence (IEEE WCCI 2026), to be held in Maastricht, Netherlands, June 21-26, 2026. 🇳🇱

🔬 Key Insights:

• We introduce a data-driven framework that reformulates atmospheric neutrino oscillation parameter inference as a supervised regression task over structured oscillation maps. 📊
• We also introduce a neural network-based uncertainty quantification mechanism, Conformal DualAQD, that produces distribution-free prediction intervals with formal coverage guarantees. 🛡️
• Experiments on simulated oscillation maps under Earth-matter conditions demonstrate that the proposed method is comparable to an MCMC baseline in estimation accuracy, with substantial improvements in computational cost (around 240× fewer FLOPs and 33× faster in average processing time). ⚡

📖 Read the full paper here: https://arxiv.org/abs/2603.22342

💻 GitHub repo here: https://github.com/GiorgioMorales/NuOscParam