Rate My Professor Michael Spannowsky

Spannowsky's research centers on theoretical high-energy physics, specializing in particle physics phenomenology, quantum field theory, machine learning and quantum computing applications to collider physics, physics beyond the Standard Model, electroweak symmetry breaking, perturbative QCD, and jet physics. His contributions are evidenced by over 27,000 citations across more than 470 publications. Key works include 'Theory-informed neural networks for particle physics' (2026), 'Electroweak scalar effects beyond dimension-six in SMEFT' (2026), 'Real-time scattering processes with continuous-variable quantum computers' (2025, with S. Abel and S. Williams), 'Collective variables of neural networks: empirical time evolution and scaling laws' (2025), 'Quantum machine learning for particle physics using a variational quantum classifier' (2021, cited over 140 times), 'Completely quantum neural networks' (2022), and 'Anomaly detection in high-energy physics using a quantum autoencoder' (2022). These publications address critical challenges such as effective field theory interpretations of LHC data, quantum simulations of scattering processes, IRC-safe graph neural networks for new physics searches, and variational quantum algorithms for lattice gauge theories. Spannowsky has supervised multiple PhD students, contributing to advancements in quantum machine learning for anomaly detection and jet substructure analysis at the High-Luminosity LHC. In 2026, he was appointed University Professor of Theoretical Physics at the Karlsruhe Institute of Technology while retaining his affiliation with Durham.