Conveners
Keynote: Erwin Laure
- Philipp Gschwandtner
Keynote: Sam Hatfield
- Aiko Voigt (University of Vienna)
Keynote: Dan Alistarh
- Alois Schlögl (Austria)
Keynote: Jan Šuntajs
- Davor Sluga
Keynote: Alessandro Laio
- Franci Merzel (Theory Department, National Institute of Chemistry)
Erwin Laure is the Director of the Max Planck Computing and Data Facility (MPCDF) of the MPG in Garching, Germany and Honorary Professor at the Technical University of Munich. Before joining MPG he was Professor for High Performance Computing at KTH Stockholm and Director of the PDC Center for High Performance Computing there. He holds a PhD from the University of Vienna and has more than 25...
For many decades now, the European Centre for Medium-Range Weather Forecasts (ECMWF) has spearheaded developments in global numerical weather prediction. The continued growth in forecast skill over the past few decades is due in large part to increases in the grid resolution of ECMWF’s Earth-systemnmodel, the Integrated Forecasting System (IFS). This increase has gone hand-in-hand with...
A key barrier to the wide deployment of highly-accurate machine learning models, whether for language or vision, is their high computational and memory overhead. Although we possess the mathematical tools for highly-accurate compression of such models, these elegant techniques require second-order information about the model’s loss function, which is hard to even approximate efficiently at the...
This talk highlights the importance of high-performance computing (HPC) in addressing some of the fundamental challenges in nonequilibrium quantum physics, particularly in understanding ergodicity-breaking transitions (EBTs) in isolated interacting quantum systems. These transitions delineate ergodic systems (also referred to as quantum chaotic), which equilibrate over time, from nonergodic...
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Finding a small set of features that still retains sufficient information about the dataset is important for the successful...
