Research Scientist in Artifical Intelligence for Particle Accelerators
About Me #
I am a research scientist with a background in Computer Science and a passion for AI, currently completing my PhD at Deutsches Elektronen-Synchrotron DESY in Hamburg, Germany. My research focuses on the development of machine learning-based algorithms towards the goal of autonomous particle accelerator operations with a strong focus on control and tuning using reinforcement learning and differentiable simulations. My other research interests include surrogate modelling and virtual diagnostics using a variety of supervised learning methods.
Download my CVPublications #
Ryan Roussel, Grégoire Charleux, Auralee Edelen, Annika Eichler, Juan Pablo Gonzalez-Aguilera, Axel Huebl, Jan Kaiser, Remi Lehe, Andrea Santamaria Garcia and Chenran Xu. Advancements in Backwards Differentiable Beam Dynamics Simulations for Accelerator Design, Model Calibration, and Machine Learning. In 32nd Linear Accelerator Conference (LINAC2024), 2024.
Simon Hirlaender, Sabrina Pochaba, Lamminger Lukas, Andrea Santamaria Garcia, Chenran Xu, Jan Kaiser, Annika Eichler and Verena Kain. Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN’s AWAKE Project. In Combining, Modelling and Analyzing Imprecision, Randomness and Dependence. SMPS 2024. Advances in Intelligent Systems and Computing, 2024.
Ryan Roussel, Auralee L. Edelen, Tobias Boltz, Dylan Kennedy, Zhe Zhang, Fuhao Ji, Xiaobiao Huang, Daniel Ratner, Andrea Santamaria Garcia, Chenran Xu, Jan Kaiser, Angel Ferran Pousa, Annika Eichler, Jannis O. Lübsen, Natalie M. Isenberg, Yuan Gao, Nikita Kuklev, Jose Martinez, Brahim Mustapha, Verena Kain, Christopher Mayes, Weijian Lin, Simone Maria Liuzzo, Jason St. John, Matthew J. V. Streeter, Remi Lehe and Willie Neiswanger. Bayesian Optimization Algorithms for Accelerator Physics. In Physical Review Accelerators and Beams, 2024.
Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia, Oliver Stein, Erik Bründermann, Willi Kuropka, Hannes Dinter, Frank Mayet, Thomas Vinatier, Florian Burkart and Holger Schlarb. Reinforcement Learning-trained Optimisers and Bayesian Optimisation for Online Particle Accelerator Tuning. In Scientific Reports, 2024.
Jan Kaiser, Chenran Xu, Annika Eichler and Andrea Santamaria Garcia. Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations. In Physical Review Accelerators and Beams, 2024.
Andrea Santamaria Garcia, Luca Scomparin, Chenran Xu, Simon Hirlaender, Sabrina Pochaba, Annika Eichler and Jan Kaiser. The Reinforcement Learning for Autonomous Accelerators Collaboration. In 15th International Particle Accelerator Conference (IPAC), 2024.
Simon Hirlaender, Lukas Lamminger, Sabrina Pochaba, Jan Kaiser, Chenran Xu, Andrea Santamaria Garcia, Luca Scomparin and Verena Kain. Towards Few-Shot Reinforcement Learning in Particle Accelerator Control. In 15th International Particle Accelerator Conference (IPAC), 2024.
Antonin Sulc, Gregor Hartmann, Jenefer Maldonado, Verena Kain, Florian Rehm, Annika Eichler, Jan Kaiser, Tim Wilksen, Frank Mayet, Raimund Kammering, Henrik Tuennermann, Jason St. John, Hayden Hoschouer, Kyle J. Hazelwood, Thorsten Hellert, Daniel Ratner, Wan-Lin Hu and Alex Bien. Towards Unlocking Insights from Logbooks Using AI. In 15th International Particle Accelerator Conference (IPAC), 2024.
Jan Kaiser, Annika Eichler and Anne Lauscher. Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language. In arXiv, 2024.
Sergey Tomin, Jan Kaiser, Nils Maris Lockmann, Torsten Wohlenberg and Igor Zagorodnov. Undulator Linear Taper Control at the European X-Ray Free-Electron Laser Facility. In Physical Review Accelerators and Beams, 2024.
Jan Kaiser, Chenran Xu, Annika Eichler and Andrea Santamaria Garica. Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations. In arXiv, 2024.
Ryan Roussel, Auralee L. Edelen, Tobias Boltz, Dylan Kennedy, Zhe Zhang, Fuhao Ji, Xiaobiao Huang, Daniel Ratner, Andrea Santamaria Garcia, Chenran Xu, Jan Kaiser, Angel Ferran Pousa, Annika Eichler, Jannis O. Lübsen, Natalie M. Isenberg, Yuan Gao, Nikita Kuklev, Jose Martinez, Brahim Mustapha, Verena Kain, Weijian Lin, Simone Maria Liuzzo, Jason St. John, Matthew J. V. Streeter, Remi Lehe and Willie Neiswanger. Bayesian Optimization Algorithms for Accelerator Physics. In arXiv, 2023.
Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia, Oliver Stein, Erik Bründermann, Willi Kuropka, Hannes Dinter, Frank Mayet, Thomas Vinatier, Florian Burkart and Holger Schlarb. Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning. In arXiv, 2023.
Chenran Xu, Jan Kaiser, Erik Bründermann, Annika Eichler, Anke-Susanne Müller and Andrea Santamaria Garcia. Beam Trajectory Control with Lattice-agnostic Reinforcement Learning. In 14th International Particle Accelerator Conference (IPAC), 2023.
Jan Kaiser, Annika Eichler, Sergey Tomin and Zihan Zhu. Machine Learning for Combined Scalar and Spectral Longitudinal Phase Space Reconstruction. In 14th International Particle Accelerator Conference (IPAC), 2023.
Zihan Zhu, Sergey Tomin and Jan Kaiser. Application of Machine Learning in Longitudinal Phase Space Prediction at the European XFEL. In FEL2022, 2022.
Jan Kaiser, Oliver Stein and Annika Eichler. Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training. In 39th International Conference on Machine Learning (ICML), 2022.
Oliver Stein, Jan Kaiser, Annika Eichler and Ilya Agapov. Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications. In 13th International Particle Accelerator Conference (IPAC), 2022.
Annika Eichler, Florian Burkart, Jan Kaiser, Willi Kuropka, Oliver Stein, Chenran Xu, Erik Bründermann and Andrea Santamaria Garcia. First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT. In 12th International Particle Accelerator Conference (IPAC), 2021.
Fin Hendrik Bahnsen, Jan Kaiser and Görschwin Fey. Designing Recurrent Neural Networks for Monitoring Embedded Devices. In IEEE European Test Symposium (ETS), 2021.
Jan Kaiser, Kai Bavendiek and Sibylle Schupp. Do We Need Real Data? - Testing and Training Algorithms with Artificial Geolocation Data. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, 2019.
Featured talks #
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations. In Fourth MODE Workshop on Differentiable Programming for Experiment Design, Valencia, Spain, 2024.
Cheetah – A High-speed Differentiable Beam Dynamics Simulation for Machine Learning Applications. In 4th ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators, Gyeongju, South Korea, 2024.
Applying Reinforcement Learning to Particle Accelerators: An Introduction. In 4th ICFA Beam Dynamics Mini-Workshop on Machine Learning Applications for Particle Accelerators, Gyeongju, South Korea, 2024.
Large Language Models for Particle Accelerator Tuning. In 1st Large Language Models in Physics Symposium (LIPS), Hamburg, Germany, 2024.
Reinforcement Learning Tutorial: Application to an Accelerator Problem. In 1st Collaboration Workshop on Reinforcement Learning for Autonomous Accelerators (RL4AA'23), Karlsruhe, Germany, 2023.
Reinforcement Learning for the Optimization of Particle Accelerators. In Machine Learning in Engineering Summer School, Hamburg, Germany, 2022.
Reinforcement learning for Particle Accelerators. In MT ARD ST3 pre-meeting Machine Learning Workshop, Berlin, Germany, 2022.