Research Highlights
EuCAIF Leadership
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.
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Large Physics Models & Benchmarks
Pioneering 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.
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DarkMachines Community
Founded 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.
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First SUSY Search Results at LHC
Co-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. Co-convener of the ATLAS SUSY working group for missing transverse momentum searches, coordinating over 100 scientists during LHC's startup phase. Delivered the first official conference presentation of SUSY search results at Moriond 2011 (ATLAS experiment).
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Model-Independent Discovery Framework
Pioneered 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.
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Generative AI for Physics Events
First 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.
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Reconstruction-Free Encoders for LHC
Pioneering work in the field of transformer-based tracking (matching hits to tracks) for the LHC. Demonstrated that AI allows Higgs events to be selected based solely on raw data.
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Interdisciplinary AI Applications
Built 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.