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 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.
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.
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.
ABC, Gaussian process emulation, parameterising dynamical Systems, a #julialang package, work with @FeiHeUK and a bunch of very talented students: what’s not to like?
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa078/5727756
It's two months ahead. We're looking forward to your submission.
* Paper Abstracts Due: Friday, April 3, 2020
* Paper Submissions Due: Friday, April 10, 2020
https://escience2020.cmc.osaka-u.ac.jp/paper-submission/
Our latest work with Stefan Hellander on stochastic chemical kinetics now available online: https://aip.scitation.org/doi/10.1063/1.5095075 Quite substantial speedups of high-resolution spatial simulations if I might say so myself 🙂 #hellanderlab #stochss
