Nicholas Luongo

Researcher at the intersection of particle physics and machine learning. I currently study data from the ATLAS experiment at the Large Hadron Collider looking for evidence of undiscovered particles and physical forces. I'm very interested in how machine learning can give us a deeper understanding of this data and aid in discoveries.

Research Areas

Physics

Particle physics as part of the ATLAS experiment at CERN

Data Science

Usage of data to reveal truths of the world around us

Machine Learning

Cutting-edge machine learning applied to complex physics problems

Anomaly Detection

Identify unusual signatures in a new paradigm of BSM search

High-Performance Computing

Scaling ML training on some of the largest supercomputers

Recent Publications

Enhancing Sensitivity for Di-Higgs Boson Searches Using Anomaly Detection and Supervised Machine Learning Techniques

Journal of High Energy Physics (In Review) • 2025 (Est.)

Salt: Multimodal Multitask Machine Learning for High Energy Physics

Journal of Open Source Software • August 2025

ADFilter – A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks

Information • March 2025

Search for resonant and non-resonant Higgs boson pair production in the b¯bτ +τ − decay channel using 13 TeV pp collision data from the ATLAS detector

Journal of High Energy Physics • July 2023

Deep Learning for Pion Identification and Energy Calibration with the ATLAS Detector

ATLAS Public Note • 2020