ZIH-Kolloquium - Towards scalable Machine Learning

Towards scalable Machine Learning

This talk will discuss the challenges and recent progress towards the application of machine learning methods to very large and difficult learning problems. We will analyse, why building a scalable high performance learning system is such a difficult task and which theoretical and practical problems need to be solved a every level of a Machnie Learning System - from accelerator hardware designs, over HPC system IO and communication protocols up to software middle layers, mathematical foundations and distributed learning algorithms.

Janis Keuper leads the "Large Scale Machnine Learning" group at the Fraunhofer Competence Center for High Performance Computing and is a Principal Investigator at the Fraunhofer Center Machine Learning. His current research is focus on scalable machine learning systems, especially Deep Learning. Before joining ITWM in 2012, he was a Group Leader at the Intel Visual Computing Institute (Saarbrücken, Germany). Janis received his Masters and PhD degrees in Computer Science form the Albert-Ludwigs University in Freiburg and did his PostDoc training in the group of Prof. Bernd Jähne at the University of Heidelberg. Janis is the chair of the Deep Learning trac at the ISC Supercomputing 2018 conference and member of the organizing committee of the "Machine Learning in HPC" Workshop at the ACM Supercomputing 18 conference. Publications: https://scholar.google.de/citations?hl=de&user=BUkDvU0AAAAJ&view_op=list_works