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We study and advance the foundations of data-driven science and engineering.

We power scientific discovery, driven by AI and statistical science.

We are a group hosted at the Division of Scientific Computing, Science for Life Laboratory (SciLifeLab). 

We work on the fundamentals of machine learning and AI, with a strong focus on solving hard problems in (life) sciences.

Funding agencies enabling our research:

Recent News

– Feb 15, 2024: We welcome applications for a PhD position in Scientific Machine Learning (keywords: variational inference, large-scale optimization, robust learning, Bayesian inference). Apply here by April 2, 2024!

– Feb 15, 2024: We welcome applications for a PhD position in Deep Learning for Drug Repurposing (keywords: Bayesian neural networks, large-scale optimization, active learning, drug discovery). Apply here by March 22, 2024! Please select Project 11 titled ‘Adaptive Deep Learning of Drug Combination Mechanics for Accelerated Repurposing‘.

– Feb 13, 2024: Prashant will deliver his Docentship lecture on ‘Global Optimisation of Computationally Expensive Objective Functions’ on 27/02 at 13:00 in Theatrum Visuale (Room 100155), Ångströmlaboratoriet

– Dec 01, 2023: Preprint out! Our take on parameter-free robust learning via variational inference – arXiv link

– Nov 07, 2023: Swedish Research Council (VR) starting grant awarded to Prashant Singh.

– Oct 23, 2023: We welcome Andrey Shternshis as a PostDoc in our lab.

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


statistical sampling, data gathering, active learning, design of experiments, sequential design, reinforcement learning


surrogate modeling, multi-fidelity modeling, deep learning, Bayesian models, time series modeling


Bayesian optimization, non-convex optimization, variational inference, surrogate-based single and multi-objective optimization

Inverse Problems

likelihood-free parameter estimation, approximate Bayesian computation, learning summary statistics, ill-posed problems