Research Areas
Research of the high productivity data processing research group at Forschungszentrum Juelich – Juelich Supercomputing Centre and University of Iceland focuses on application-driven parallel and scalable machine learning methods including related areas such as feature engineering, statistical data mining, and innovative deep learning techniques.
In our projects we are cooperating with academic research groups worldside as well as with selected industry companies including the support of start-ups. Additional funding sources are the European Union or the German Federal Ministry of Education and Research.
If you are interested in one of these research topics please contact me by email for further information or check out my publications page on which you can find papers, software, and additional material for download.
Current Research Focus
- Investigate new deep learning network topologies that take advantage of innovative parallel computing technologies
- Improve hybrid modeling capabilities in order to better intertwine machine learning modeling with physical modeling
- Addressing scientific and engineering application challenges using High Performance Computing and High Throughput Computing
Publicly Funded Projects
- DEEP-EST: Dynamical Exascale Entry Platform – Extreme Scale Technologies (EU Project)
- SMITH: Smart Medical Information Technology for Healthcare (BMBF Project)
- ON4OFF: Connecting Online Shopping with Offline Shopping using Machine & Deep Learning (EU/NRW EFRE Project)
Enjoying our yearly research group dinner 'Iceland Section' to celebrate our productive collaboration of @uni_iceland @uisens @Haskoli_Islands & @fzj_jsc @fz_juelich & E.Erlingsson @ernire passed mid-term in modular supercomputing driven by @DEEPprojects – https://t.co/EuVbS7nPhR pic.twitter.com/urzHk65luR
— Morris Riedel (@MorrisRiedel) February 10, 2019