Dr. Julian Arnold
Short Curriculum Vitæ
| 2025 - Present | PostDoc in Theoretical Physics Quantum Theory Bruder Group Department of Physics, University of Basel, Basel, Switzerland |
| 2021 - 2025 | PhD in Theoretical Physics Thesis: "Machine learning phase transitions: A probabilistic perspective" Supervisor: Prof. Christoph Bruder Department of Physics, University of Basel, Basel, Switzerland |
| 2020 - 2021 | Master Thesis: "Entropy production in ticking clocks" Supervisors: Dr. Mischa Prebin Woods, Prof. Renato Renner, and Prof. Christoph Bruder Institute for Theoretical Physics, ETH Zurich |
| 2019 - 2021 | Master of Science in Nanosciences University of Basel, Basel, Switzerland |
| 2016 - 2019 | Bachelor of Science in Nanosciences University of Basel, Basel, Switzerland |
Research interests
- Machine learning
- Quantum information theory
- Stochastic processes
Publications
- Neural Total Variation Distance Estimator for Changepoint Detection in News Data
C. Zsolnai, N. Lörch, and J. Arnold
arXiv:2506.18764
- Machine learning the Ising transition: A comparison between discriminative and generative approaches
D. Zhang, F. Schäfer, and J. Arnold
J. Phys. Commun. 9 055007 (2025); arXiv:2411.19370
- Phase Transitions in the Output Distributions of Large Language Models
J. Arnold, F. Holtorf, F. Schäfer, and N. Lörch
arXiv:2405.17088
- Machine learning phase transitions: Connections to the Fisher information
J. Arnold, N. Lörch, F. Holtorf, and F. Schäfer
arXiv:2311.10710
- Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion
J. Arnold, F. Schäfer, and N. Lörch
NeurIPS 2023 Machine Learning and the Physical Sciences Workshop; arXiv:2311.09128
- Mapping out phase diagrams with generative classifiers
J. Arnold, F. Schäfer, A. Edelman, and C. Bruder
Phys. Rev. Lett. 132, 207301 (2024); arXiv:2306.14894
Featured in press release by University of Basel and MIT
- Performance Bounds for Quantum Feedback Control
F. Holtorf, F. Schäfer, J. Arnold, C. Rackauckas, and A. Edelman
IEEE Trans. Autom. Control 69, 8057 (2024); arXiv:2304.03366
- Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions
J.C.S.V. Veliz, J. Arnold, R.J. Bemish, and M. Meuwly
J. Phys. Chem. A 126, 7971 (2022); arXiv:2209.0037
- Modern applications of machine learning in quantum sciences
A. Dawid, J. Arnold et al.
Available as a book at Cambridge University Press (ISBN: 9781009504935); arXiv:2204.04198
- Replacing neural networks by optimal analytical predictors for the detection of phase transitions
J. Arnold and F. Schäfer
Phys. Rev. X 12, 031044 (2022); arXiv:2203.06084
Featured in press release by University of Basel.
- Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
J. Arnold, J.C.S.V. Veliz, D. Koner, N. Singh, R.J. Bemish, and M. Meuwly
J. Chem. Phys. 156, 034301 (2022); arXiv:2111.03563
- Interpretable and unsupervised phase classification
J. Arnold, F. Schäfer, M. Žonda, and A.U.J. Lode
Phys. Rev. Res. 3, 033052 (2021); arXiv:2010.04730
- Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions
J. Arnold, D. Koner, S. Käser, N. Singh, R.J. Bemish, and M. Meuwly
J. Phys. Chem. A 124, 7177 (2020); arxiv:2005.1446