Integrative Scalable Computing Laboratory

Led by Andreas Hellander and Salman Toor

Modeling & 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.

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Artificial intelligence

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.

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Cloud computing & data engineering

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.

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Featured projects

Federated Machine Learning

Federated Machine Learning

Artificial intelligence is rapidly transforming our society. Machine learning models will be in every digital system we use, and it is imperative that we protect the integrity of data owners . In this project we work on training schemes, scalable implementations,...

ESSENCE

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

The Federated and Distributed Machine Learning Conference start today!

Key Topics Include:
- Federated learning infrastructure design
- Rational collaboration and incentive design
- Challenges in maintaining privacy and security

https://www.federatedlearningconference.com/

@FedLearning

More fantastic news for Johannes-Wenner-Gren Fellow 2020! Next stop @OxUniMaths
and @ruth_baker lab! https://www.swgc.org/wennergren-fellows.aspx

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

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