We often use stochastic descriptions to model complex systems. Many of our projects involve kinetic Monte Carlo, agent-based models and multiscale modeling. A reoccurring theme is how to leverage distributed e-infrastructure for simulations and how to use machine learning to construct approximations.
A theme in the group is the use of artificial intelligence and machine learning to make software and infrastructure intelligent, interactive and scalable. We also do disciplinary research into specific areas of ML, such as likelihood-free inference and federated ML.
Our research on e-Infrastructure and data engineering ranges from development of new ways to manage large and fast data to massively parallel, interactive and cloud native applications, to management and scalability of cloud and edge infrastructure.
The group participates in the eSSENCE strategic initiative on eScience, focusing on eScience tools and technologies.
Scaleout Systems is a spin-off from the group, focusing on privacy-preserving AI and cloud-native machine learning.
A brief overview of federated learning with @A_Hellander
https://www.youtube.com/watch?v=jbLHRtGWPL8
An excellent paper be @Hakanwie et al. Check it out! https://ieeexplore.ieee.org/document/9103229
Early prediction of drug uptake by monitoring cell dynamics with deep learning, see our pre-publication at https://www.biorxiv.org/content/10.1101/2020.04.06.027672v1
