View on GitHub

The Deep Learning for Science Workshop

Please sign up DLS email list.

Program (June 20th, 9AM–6PM, Location: Gold 3 after the 11AM break in Frankfurt Marriott Hotel)

Time Title Speaker
9:00–9:10 Opening Valeriu Codreanu, SURFSara
9:10–9:40 Deep learning workflows using CANDLE Tom Brettin, Argonne National Laboratory
9:40–10:10 What is Unique in Individual Gait Patterns? Understanding and Interpreting - Deep Learning in Gait Analysis Fabian Horst, University of Mainz
10:10–10:40 Collider event generation with deep generative models Sydney Otten, Radboud University Nijmegen
10:40–11:10 Deep Learning/AI Accelerated Advances in Fusion Energy Science for Disruption Predictions with Implications for Plasma Control Bill Tang, Princeton University
11:00–11:30 Coffee Break  
11:30–12:00 Understanding the Earth system with machine learning Markus Reichstein, Max Planck Institute for Biogeochemistry
12:00–12:30 Accelerating the simulations of nonlinear dynamical systems in astrophysics with deep learning Maxwell Cai, Leiden Observatory
12:30–14:00 Lunch Break  
14:00–15:00 Keynote, Deep Learning application for High Energy Physics: examples from the LHC Sofia Vallecorsa, CERN
15:00–15:30 Generative Modeling of Protein Folding Transitions with Recurrent Auto-encoders Fangfang Xia, Argonne National Laboratory
15:30–16:00 Machine-learned turbulence in next-generation weather models Chiel van Heerwaarden, Wageningen University
16:00–16:30 Coffee Break  
16:30–17:30 Panel Discussion: Challenges in Applying Deep Learning to Scientific Research Panelists: Fangfang Xia, Chiel van Heerwaarden, Sofia Vallecorsa, Markus Reichstein

The Deep Learning for Science Workshop

The Deep Learning for Science workshop is with ISC’19 on June 20th, 2019 in Frankfurt, Germany. It is the second workshop in the Deep Learning on Supercomputers series. The workshop provides a forum for practitioners working on any and all aspects of DL for scientific research in the High Performance Computing (HPC) context to present their latest research results and development, deployment, and application experiences. The general theme of this workshop series is the intersection of DL and HPC, while the theme of this particular workshop is centered around the applications of deep learning methods in scientific research: novel uses of deep learning methods, e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (GAN), and reinforcement learning (RL), for both natural and social science research, and innovative applications of deep learning in traditional numerical simulation. Its scope encompasses application development in scientific scenarios using HPC platforms; DL methods applied to numerical simulation; fundamental algorithms, enhanced procedures, and software development methods to enable scalable training and inference; hardware changes with impact on future supercomputer design; and machine deployment, performance evaluation, and reproducibility practices for DL applications with an emphasis on scientific usage.

Topics include but are not limited to:

As part of the reproducibility initiative, the workshop requires authors to provide information such as the algorithms, software releases, datasets, and hardware configurations used. For performance evaluation studies, we will encourage authors to use well-known benchmarks or applications with open accessible datasets: for example, MLPerf and ResNet-50 with the ImageNet-1K dataset.

Import Dates

Paper Submission

Authors are invited to submit an extended abstract with 1-4 pages in single column text with LNCS style. All submissions should be in LNCS format and submitted using easychair.

Organizing Committee

Previous Workshop

1st Deep Learning on Supercomputers Workshop in SC’18 at Dallas, USA