Simon Hirländer
Vice Director, IDA Lab · Head of Smart Analytics & Reinforcement Learning
Department of Artificial Intelligence and Human Interfaces · Paris Lodron University Salzburg
Theoretical Physicist and Decision Scientist bridging rigorous mathematical physics—including Hamiltonian mechanics, symplectic geometry, and potential theory—with modern Deep Reinforcement Learning. In 2018, I led the first Deep RL experiment on the CERN accelerator complex. Today, my research team develops safe, uncertainty-aware, and physics-informed AI systems for critical physical infrastructures and industrial processes.
Core Research Pillars
Integrating physical invariants and control guarantees into modern decision-making architectures.
Recent News & Highlights
Invited Talk at NeurIPS 2026 Workshop (Paris)
Invited talk on "Koopman Stabilised Symplectic World Models for Safe Offline-to-Online RL in Particle Accelerators" at the NeurIPS 2026 Workshop RL for Experimental Sciences: Bridging the Simulation-to-Reality Gap in Paris.
Podcast: S2 #17 | Reinforcement Learning in Business
Featured episode on DIH West's EASY cheesy DIGITAL exploring decision-making under uncertainty, agentic AI, and business transformation through Reinforcement Learning.
Four IPAC'26 Papers Accepted
Contributions on Koopman-stabilised world models, Causal GP-MPC, delayed-consequence RL beyond greedy optimization, and SIS18 injection optimization accepted for presentation.
KI-MINTFIT Doctoral Program Launches (€1.08M)
Launch of the cooperative doctoral program funded by the Austrian BMBWF in collaboration with PH Salzburg, advancing AI-driven STEM subject instruction and student models.
Real-World Impact & Results
Demonstrated breakthroughs across extreme, high-dimensional experimental environments.
Reduced beam losses at the GSI/FAIR SIS18 heavy-ion synchrotron by 67% using multi-objective Bayesian optimization and Gaussian Process MPC.
Conducted the pioneering autonomous beam alignment experiment on the CERN Super Proton Synchrotron (SPS) and AWAKE lines, establishing international baselines.
Trained Stable Koopman State-Space Models on 58M+ operational industrial time-series points with Danieli Automation, achieving high-accuracy torque and tension prediction.
Institutional Partners & Collaborators
Interested in Research Collaboration or Theses?
We regularly supervise motivated MSc and PhD students and collaborate with industrial partners and research labs on reinforcement learning and autonomous systems.