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.
Selected for the enterprise software category and among the 500+ top AI startups in Europe!
https://www.ai-startups-europe.eu/
#EuropeanAILandscape #AI #artificialintelligence #startups #AIstartups #machinelearning https://www.ai-startups-europe.eu/
Would you like to pursue a PhD on an interdisciplinary project?
We have PhD positions available @univofstandrews on the computational modelling of cell mechanics, tissue dynamics, and cell differentiation during embryonic development.
https://www.findaphd.com/phds/?Keywords=kursawe+eastbio
Get started with production grade framework agnostic #fedml. Then help us build it as open source. Also a good research framework if you want to experiment with real distributed deployments. Reach out to me or @morganekmefjord if you want to contribute.
The easiest way to start with FEDn is to use the provided docker-compose templates to launch a local sandbox & simulated environment.
Clone the repository and follow these steps: https://github.com/scaleoutsystems/fedn#getting-started
#federatedlearning #machinelearning #ai
The winner of the Adipocyte Cell Imaging Challenge is the HASTE team! 🎉 Eight teams presented different solutions to the challenge but @AstraZeneca chose the HASTE team for their way of managing the problem within cell biology. Congratulations!
To make a robust and horizontally scalable #fedml implementation that copes well with system heterogeneity, we draw on proven architectural patterns. It should come as no surprise if you see parallels to MapReduce (the programming model).
FEDn is an open-source, modular framework for Federated Machine Learning (FedML), developed and maintained by Scaleout Systems.
Learn more about the FEDn architecture in this video: https://youtu.be/TJnkN4JyrRM
#federatedlearning #ml #AI #machinelearning https://youtu.be/TJnkN4JyrRM
