Challenges

The practical problems that arise when going from simple models to big models include: Most simulation algorithms do not scale well to high dimensions. Simulation can becomes prohibitively expensive due to the multiscale nature of systems. Failure of traditional engineering methodology such as sensitivity analysis and optimization due to high dimensionality, non-linearities and stochasticity…. … […]

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, and applications of Federated Learning – a recent approach to training ML models while […]

“Ten simple rules” for establishing a national scale OpenStack cloud e-infrastructure for science

The SNIC Science Cloud (SSC) team has published a paper in the 2017 conference on IEEE eScience.  SNIC Science cloud has been an infrastructure project run by the Swedish National Infrastructure for Computing (SNIC) with the purpose to assess if and how SNIC should offer cloud infrastructure to the scientific community. The project is now coming […]

We welcome Ben Blamey to the group!

We are thrilled to welcome Dr. Ben Blamey as a postdoc in the group. Ben will work on our SSF-funded HASTE project, with intelligent systems for setting up and managing information hierarchies in very large volumes of image data from microscopy, read mroe about Ben and the project here: Ben Blamey joins the team to […]

Stefan Hellander joins the lab

  We are delighted to have Stefan Hellander join the lab!  Stefan obtained his Ph.D. in scientific computing from Uppsala University in 2013, with the thesis “Stochastic Simulation of Reaction-Diffusion Processes”, advised by Prof. Em. Per Lötstedt. He then went on to work as a postdoc in the lab of Prof. Linda Petzold at UCSB, […]