Integrative Scalable Computing Laboratory

ISCL is a research group at the Department of Information Technology at Uppsala University. PIs Andreas Hellander and Salman Toor.

Stochastic simulation

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.

Artificial intelligence

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.

Distributed computing

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.

Featured projects

Federated Machine Learning

Federated Machine Learning

Artificial intelligence is rapidly transforming our society. Machine learning models will be components in nearly every digital system we use. For this reason, there is an urgent need for methods and software that allows for development of state-of-the...


The group participates in the eSSENCE strategic initiative on eScience, focusing on eScience tools and technologies.

Scaleout Systems

Scaleout Systems is a spin-off from the group, focusing on privacy-preserving AI and cloud-native machine learning.

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FEDn 0.2.2 is out: 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

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.

"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.

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