I lead a research group at the intersection of artificial intelligence and fundamental physics, pioneering new approaches to scientific discovery through machine learning. My work bridges experimental particle physics at CERN's ATLAS detector, theoretical physics, and astrophysics, with a focus on developing transformative AI methodologies for understanding dark matter and beyond-standard-model physics.
At the core of my current research is the development of Physical Data Encoders , Large Physics Models and Large Physics Benchmarks, a paradigm shift that brings the successes of foundation models from AI to fundamental physics. This vision combines decades of experience in particle physics data analysis with cutting-edge machine learning to create scalable, generalizable AI systems for physics discovery.
Beyond technical innovation, I've built and lead major international communities advancing AI in physics. As a founder of the DarkMachines Initiative and EuCAIF (European Coalition for AI in Fundamental Physics), I've created collaborative networks spanning over 100 researchers across Europe and beyond, establishing new frameworks for community-driven research at the frontier of AI and fundamental physics.
My research portfolio spans ATLAS collaboration leadership roles, pioneering work in generative AI for physics simulation and model-independent anomaly detection methods, and strategic contributions to European research policy through ECFA and the European Strategy for Particle Physics.
Established Europe's premier AI initiative for fundamental physics, officially recognized by ECFA, APPEC, and NuPECC. Named by the German Ministry (BMBF) in 2025 as a priority network for European AI strategy, building a community of over 60 senior fellows.
Explore EuCAIFPioneering the conceptual framework for applying large-scale AI paradigms to fundamental physics. Introducing Large Physics Models as physics analogues to Large Language Models and developing standardized benchmarks for evaluation across collider and astrophysical data analysis.
Read PaperFounded the first global research initiative applying machine learning to dark matter discovery, building an international community of over 100 researchers. Delivered 6 community publications including the anomaly detection challenge that became a standard benchmark in the field.
Visit DarkMachinesCo-led first published supersymmetry searches at the LHC with over 1000 combined citations. Featured in Nature news coverage and excluded large regions of dark matter parameter space, establishing foundational methodologies for beyond-standard-model searches.
View ResultsPioneered general, data-driven anomaly detection in collider physics over a decade before modern AI techniques. Delivered the broadest systematic searches at HERA and LHC spanning over 700 channels and 10,000 signal regions.
Learn MoreFirst publication using generative AI to simulate collider events, introducing the concept of an information buffer for VAEs. This work laid the foundation for fast simulation methods now widely adopted in particle physics.
Read PaperBuilt substantial independent research profile (H-index >20 outside collaborations) pioneering AI across theoretical physics, astrophysics, and scientific epistemology. Developed autosourceID framework and deep learning for gamma-ray astrophysics.
Full publication list and citation metrics available on:
INSPIRE-HEP (All publications) INSPIRE-HEP (excluding the ATLAS collaboration) Google ScholarOur research group consists of PhD students, postdocs, and collaborators working at the intersection of AI and fundamental physics.
Research focus: Classification and Anomaly detection for 4 top events
Research focus: Developing transformer architectures for tracking and muon reconstruction
Research focus: End to end di-higgs classification, reconstruction free higgsformers, Large Physics Benchmark
Research focus: ML based Optimization of di-higgs analysis
FNWI (Huijgensgebouw), Radboud University
Nijmegen, Netherlands
National Institute for Subatomic Physics
Amsterdam, Netherlands