People
Current lab
- Sven Krippendorf — Principal Investigator
- Yi Gu — PhD student
- Zhimei Liu — PhD student
- Daniel Stoerk — Master’s student
Ongoing external PhD supervision
- Kai Lehman — machine learning for cosmological inference, jointly supervised with Jochen Weller
- Silas Zelmer — machine learning for eROSITA clusters and cosmology, jointly supervised with Esra Bulbul
Research collaborators
Our research is strongly collaborative. The names below highlight sustained research programmes rather than providing a complete list of co-authors.
Senior collaborators
- Esra Bulbul — Max Planck Institute for Extraterrestrial Physics (MPE)
- Michele Cicoli — University of Bologna
- Christian Holm — University of Stuttgart
- Anshuman Maharana — Harish-Chandra Research Institute (HRI)
- Gary Shiu — University of Wisconsin–Madison
- Michael Spannowsky — Karlsruhe Institute of Technology (KIT)
- Joseph Tooby-Smith — University of Bath
- Jochen Weller — LMU Munich
Postdoctoral and early-career collaborators
- Nina Elmer — postdoctoral research collaborator
- Andreas Schachner — research collaborator and former postdoctoral collaborator at LMU Munich
- Samuel Tovey — research collaborator and former visiting PhD student
- Pellegrino Piantadosi — research collaborator and former visiting PhD student
- Konstantin Nikolaou — research collaborator and former visiting PhD student
- Yong Sheng Koay — project collaborator and former visiting student
Sven is a Fellow of the European Coalition for AI in Fundamental Physics (EuCAIF), through which we contribute to collaborative efforts at the interface of AI and fundamental physics.
Cambridge–Infosys AI Centre
Sven leads the Mathematical AI theme at the Cambridge–Infosys AI Centre. More broadly, we provide industry-facing teaching through online courses, in-person sessions and focused workshops. Some of these activities are connected with the Centre, while others are direct industry engagements. They reflect current developments in machine learning and connect them with methods and questions relevant to industry.
Previous supervision
PhD students
- Marc Syvaeri — symmetries and neural networks, 2021
- Philip Betzler — dualities and machine learning, 2020
Master’s projects
- Yi Gu — automated search for scattering amplitudes as executable programs, before continuing as a PhD student
- Nicolas Baron Perez — machine learning for galaxy-cluster scaling relations, jointly supervised with Esra Bulbul, 2022
- Abhishek Dubey — reinforcement learning and structure in the string landscape, 2022
- Julian Ebelt — machine learning in the string landscape, 2024
- Mathis Gerdes — learning Ricci-flat Calabi–Yau metrics, 2020
- Valentin Kiendl — large language models and dynamical systems, 2024
- Sebastian Mayer — sampling string-theory solutions with invertible neural networks, 2022
- Rene Kroepsch — reinforcement learning of flux vacua, 2021
- Lukas Ranftl — machine-learning estimates of galaxy-cluster masses from optical images, jointly supervised with Jochen Weller, 2022
- Simon Schallmoser — machine-learning searches for axion-like particles in X-ray data, jointly supervised with Jochen Weller, 2021
- Felix Schmid — period vectors for Calabi–Yau manifolds, 2021
- William Shellard — machine-learning searches for axion-like particles, 2024
- Binh Tah — holomorphic and holographic generative neural networks, 2022
- Yuyang Wang — gravitational waves from phase transitions in the early universe, 2022
Bachelor’s projects
- Hannah Dengler — symbolic regression for the hydrogen atom, 2024
- Matthias Heim — X-ray galaxy-cluster masses with deep neural networks, 2023
- Christoph Kohl — robustness of Hamiltonian neural networks, jointly supervised with Volker Tresp, 2020
- Kai Gipp — Lorentz-invariant Lagrangians and extra-dimensional physics, 2019
- Paul Manz — aspects of supersymmetry, 2014
If you are interested in joining us to work at the intersection of physics and machine learning, please see Opportunities.