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 core theme in the group is the use of machine learning to make scientific computing software and infrastructure more efficient, interactive and scalable. We also do disciplinary research in specific areas of ML, such as likelihood-free inference and privacy-preserving federated machine learning.
Our research in distributed computing and data engineering sciences ranges from development of new ways to manage large and fast data to design and development of massively parallel, interactive, cloud native applications operating in cloud, fog 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.
FEDn 0.2.2 is out: https://github.com/scaleoutsystems/fedn/releases/tag/v0.2.2 Mostly docs updates, and we have improved how we handle sample data for the getting-started examples. #fedml
Vår rektor slår världsrekord i solcellers prestanda. Coolt. 🌞 #solenergi #solceller #hållbarhet #klimat #energiforskning @uppsalauni https://www.uu.se/nyheter/artikel/?id=16738&typ=artikel
ABC in Svalabard starts on 12 April. It's free and open to anyone. https://twitter.com/dennisprangle/status/1380137499968868353
Detail of next week's ABC workshop (which was to have been in Svalbard but is now available online) https://sites.google.com/view/abcinsvalbard-grenoble-mirror/ (via Xi'an https://xianblog.wordpress.com/2021/04/08/abc-in-everywhere-programme/)
I'll be announcing a (4-year, paid) PhD student position in Scientific Computing for extending our existing work for deep learning modelling of genomes into applications for agricultural populations.
https://www.biorxiv.org/content/10.1101/2020.09.30.320994v1
"In order to unleash the full potential of the National Library's collections, the data must be made available to more actors. The collaboration with Scaleout Systems and AI Sweden opens up for such opportunities", says Love Börjeson, director of KB-labb.
https://www.ai.se/en/node/81535/pilot-study-federated-language-models-swedish
