19–30 Aug 2019
KTH Main Campus
Europe/Stockholm timezone

Speakers

David Broman is an Associate Professor at the KTH Royal Institute of Technology in Sweden, where he is leading the Model-based Computing Systems (MCS) research group. He is teaching computer architecture and his research focuses on programming and modeling language theory, compilers, real-time systems, and machine learning. 

Christoph Kessler is a professor for Computer Science at Linköping University, Sweden, where he leads the Programming Environment Laboratory's research group on compiler technology and parallel computing. He received a PhD degree in Computer Science in 1994 from the University of Saarbrücken, Germany, and a Habilitation degree in 2001 from the University of Trier, Germany. In 2001 he joined Linköping university, Sweden, as associate professor at the computer science department (IDA). In 2007 he was appointed full professor at Linköping university. His research interests include parallel programming, compiler technology, code generation, program optimization, and software composition. For publications and further information see his web page at http://www.ida.liu.se/~chrke. 

Stefano Markidis is Associate Professor at the CST department at KTH. He received a MS degree from Politecnico di Torino and a PhD degree from University of Illinois at Urbana- Champaign. His research interests include the investigation of novel programming models for HPC, and innovative algorithms for parallel computing. 

Niclas Jansson is a postdoc researcher at KTH Royal Institute of Technology. He received his M.S. in computer science in 2008 and a PhD in numerical analysis 2013 from KTH Royal Institute of Technology. Between 2013 and 2016, Niclas was a postdoc researcher at RIKEN Advanced Institute for Computational Science, where he was part of the application development team of the Japanese exascale program, Flagship 2020, focusing on developing extreme scale multiphysics solvers for the K computer, and currently holds a visiting scientist position at RIKEN. He has extensive experience in extreme scale computing as lead developer of RIKEN's multiphysics framework CUBE and the HPC branch of FEniCS.  

Erwin Laure is the director of PDC- HPC, professor for High Performance Computing, and head of the department for Computational Science and Technologies (CST) at KTH. His research interests include programming environments, languages, compilers and runtime systems for parallel and distributed computing. 

Joachim Hein received his PhD in Physics in 1996 from the Universität Hamburg in Germany. He held post- doctoral positions in Theoretical Particle Physics at The University of Glasgow, Cornell University and The University of Edinburgh. From 2002 until 2013 he worked as an applications expert with a focus on parallel high performance computing at EPCC at the University of Edinburgh, finishing with the position of Computing Architect. Since 2010 he is working as a researcher in the Centre for Mathematical Sciences at Lund University and as an applications expert, again with a focus on parallel computing at LUNARC, the Centre for Scientific and Technical Computing at Lund University.

Thor Wikfeldt is an Application Expert in Molecular Dynamics at PDC, KTH. He obtained his PhD in chemical physics in 2011 from Stockholm University and worked as a postdoctoral researcher at UCL, London, and the University of Iceland between 2011- 2015. At PDC, Thor provides advanced user support in the areas of molecular dynamics and computational chemistry, and he also works for the CodeRefinery project where he teaches better scientific software development practices to students and researchers.

Pawel Herman is an Associate Prof. at EECS school at KTH and the research focus in his group is on computational neuroscience, brain-like computing and machine/deep learning. He is also actively involved in teaching mainly in the areas of probabilistic machine learning and neural networks. Pawel obtained his doctoral degree in computer science from the University of Ulster, UK, where he worked on machine learning approaches to uncertainty handling in electroencephalography (EEG)-based brain-computer interfaces (BCIs). He continued this line of research and extended towards cognitive neuroimaging during his first postdoctoral experience at Donders Centre at Radboud University in Nijmegen, the Netherlands.