paul brunzema
[classic]phd student, institute for data science in mechanical engineering (dsme), rwth aachen university
I aim to create agents and algorithms that efficiently and optimally act in uncertain and potentially changing environments.▌
I am a PhD student at the Institute for Data Science in Mechanical Engineering (DSME) at RWTH Aachen University, supervised by Sebastian Trimpe, since March 2022. Since September 2023, I am also an associate doctoral researcher in the UnRAVel Research Training Group, funded by the DFG.
In summer 2026, I was a research scientist intern at Meta in New York, NY, developing the concept of agentic Bayesian optimization. In summer 2025, I joined the EPIC group at Toyota Research Institute (TRI) in Los Altos, CA as a research intern, working on vision-conditioned autonomous racing in changing conditions.
My research focuses on uncertainty quantification (Gaussian processes, Bayesian neural networks) and sequential decision-making (Bayesian optimization, control, reinforcement learning).
-
2026
Beyond Single-Step Likelihood: Gibbs Variational Last Layers for Long-Horizon Dynamics Learning
Paul Brunzema, Henrik Hose, Sebastian Trimpe
Advances in Neural Information Processing Systems (NeurIPS)
@inproceedings{brunzema2026gibbs, title = {Beyond Single-Step Likelihood: {Gibbs} Variational Last Layers for Long-Horizon Dynamics Learning}, author = {Brunzema, Paul and Hose, Henrik and Trimpe, Sebastian}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, year = {2026} } -
2026
Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis C. Tiao, Nhat Le, Kevin De Angeli, Yao Xuan, Djordje Gligorijevic
arXiv preprint arXiv:2608.00316 · Currently under review
@article{brunzema2026agentic, title = {Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch}, author = {Brunzema, Paul and Tiao, Louis C. and Le, Nhat and De Angeli, Kevin and Xuan, Yao and Gligorijevic, Djordje}, journal = {arXiv preprint arXiv:2608.00316}, year = {2026} } -
2026
Vision-Conditioned Variational Bayesian Last Layer Dynamics Models
Paul Brunzema, Thomas Lew, Ray Zhang, Takeru Shirasawa, John Subosits, Marcus Greiff
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
@article{brunzema2026vision, title = {Vision-Conditioned Variational {Bayesian} Last Layer Dynamics Models}, author = {Brunzema, Paul and Lew, Thomas and Zhang, Ray and Shirasawa, Takeru and Subosits, John and Greiff, Marcus}, journal = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2026} } -
2025
BayeSQP: Bayesian Optimization through Sequential Quadratic Programming
Paul Brunzema, Sebastian Trimpe
Advances in Neural Information Processing Systems (NeurIPS)
@inproceedings{brunzema2025bayesqp, title = {{BayeSQP}: Bayesian Optimization through Sequential Quadratic Programming}, author = {Brunzema, Paul and Trimpe, Sebastian}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, year = {2025} } -
2025
Bayesian Optimization via Continual Variational Last Layer Training
Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe, Jasper Snoek, James Harrison
International Conference on Learning Representations (ICLR)
@inproceedings{brunzema2024bayesian, title = {Bayesian Optimization via Continual Variational Last Layer Training}, author = {Brunzema, Paul and Jordahn, Mikkel and Willes, John and Trimpe, Sebastian and Snoek, Jasper and Harrison, James}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2025} } -
2025
Event-Triggered Time-Varying Bayesian Optimization
Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe
Transactions on Machine Learning Research (TMLR)
@article{brunzema2025event, title = {Event-Triggered Time-Varying {Bayesian} Optimization}, author = {Brunzema, Paul and {von Rohr}, Alexander and Solowjow, Friedrich and Trimpe, Sebastian}, journal = {Transactions on Machine Learning Research (TMLR)}, year = {2025} }