Mission
We study and advance the foundations of data-driven science and engineering.
We power scientific discovery, driven by AI, Optimization and Statistics.
We are a group hosted at the Division of Scientific Computing, Department of Information Technology, Science for Life Laboratory (SciLifeLab), UU.
We work on the fundamentals of machine learning and AI, with a strong focus on solving hard problems in (life) sciences.
Recent News
– Sep, 2025: Our paper exploring asymmetric uncertainty structures in pre-trained VLMs accepted at NeurIPS 2025 – arXiv link
– Aug, 2025: PostDoc position available in Machine Learning Theory – advertisement link.
– Jul, 2025: Mayank Nautiyal presents our work on variational autoencoders for simulation-based inference at IEEE IJCNN 2025 – arXiv link.
– May, 2025: We will participate in the NEST project ‘Time-Resolved Imaging and Multi-Channel Evaluation of Cellular Dynamics’.
– July 23-25, 2024: Aleksandr Karakulev will present our take on parameter-free robust learning via variational inference at ICML 2024 – arXiv link.
Spotlight: Jan 2024
Recent student projects:
– Bayesian Sequential Model Optimization for Drug Combination Repurposing (Dhanushki Mapitigama, Mina Badri, Ema Duljkovic)
– Bayesian Optimization for Characterising Quantum Entanglement (Stefanos Tsampanakis, Ramin Modaresi, Niklas Kostrzewa)
– Deep Learning for Ill-Posed Inverse Problems in Photonics (Johan Rensfeldt, Fredrik Gillgren, Gustav Fredrikson)
Research Areas
Our research activities broadly span theory and applications within machine learning, optimization, inverse problems, statistical sampling (Publications).
