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.

Follow us on twitter

I have a one-year Lecturer position in my group at @uppsalauni and @scilifelab to develop undergraduate courses in #artificialintelligence and #labautomation for #drugdiscovery. Deadline to apply: May 17th. Read more and apply at:

The first ever workshop using our brand new cloud-native platform for setting up #fedn networks goes live in 1h 🙂 Users can use all the open source tools they rely on for "normal ML" to set up #FedML. We believe that this SaaS will lower the threshold for real use-cases.

Great work by Sheetal Readdy @AISweden and Mina Alibeigi @zenseact presented today at AI Sweden Partner Days. Probably the first federated model for Baltic Seabird classification, trained with @scaleoutsystem #fedml technology deployed in #edgelab. Well done! đź‘Ź

"Seven challenges in the multiscale modeling of multicellular tissues", my review/opinion piece with @DIYOzzy, is now published online: (Special thanks to anonymous reviewer 2 for suggesting 7 additional references, all featuring the same author)

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