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.
Since we could not accommodate everyone that wanted to attend the free @scaleout federated learning workshop last week, @morganekmefjord will repeat it again on the 20th May. #fedml
The workshop on federated learning with Scaleout was really popular and filled up quickly. Here's a new opportunity if you didn't make the last time!
https://www.eventbrite.se/e/scaleout-federated-learning-workshop-tickets-153831802061 https://www.eventbrite.se/e/scaleout-federated-learning-workshop-tickets-153831802061
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: https://uu.se/en/about-uu/join-us/details/?positionId=393155
