| Jitsev, Jenia j.jitsev@fz-juelich.de
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JSC Deep Learning Research Lab (CST-DL) |
Unsupervised Learning, Reinforcement Learning, Recurrent Hierarchical Winner-Take-All Networks, Generative Models, Biological Neural Networks, Open-end learning / Learning to learn |
Images, Time Series, Virtual Environments (e.g, OpenGym) |
Large scale scientific data sets (material science, high throughput genomics/proteomics, biotechnology, etc) |
| Wenzel, Susanne s.wenzel@fz-juelich.de
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INM-1 Big Data Analytics Group (BDA) |
Markov Marked Point Processes, rjMCMC |
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| Dickscheid, Timo t.dickscheid@fz-juelich.de
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INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets, Image Segmentation, Image Feature detection, Markov Random Fields, Clustering, Analogies to the human brain |
Microscopic resolution 2D and 3D images |
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| Kraus, Jiri jkraus@nvidia.com
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NVIDIA |
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Want to learn the ML/DL needs of scientist at FZJ |
| Zimmermann, Olav olav.zimmermann@fz-juelich.de
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JSC Simlab Biology |
Supervised learning (SVM and its variants), Dimension Reduction (Isomap et al, clustering), Metaheuristics, basics of sequence learning methods (CRF, LSTM), bioinformatics |
biological sequence data, molecular structure data, experimental data (2-dim to n-dim or graphs), unstructured data |
ML for hypertoroidal output spaces in depth understanding of seq2seq methods, representation of world knowledge for learning methods that work with non-differentiable loss |
| Herten, Andreas a.herten@fz-juelich.de
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JSC NVIDIA Application Lab |
Software installation |
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Overview/support of ML/DL software in Jülich |
| Göbbert, Jens Henrik j.goebbert@fz-juelich.de
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JSC Cross-Sectional-Team Visualization |
Basic DL with Keras |
turbulent flows (3D) |
Collaborations/support for enabling DL with on HPC with Jupyter |
| Hermanns, Marc-André m.a.hermanns@fz-juelich.de
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JSC Cross-Sectional-Team Parallel Performance |
none yet (testing with Keras atm.) |
Performance Data (profile and trace) |
Identify or estimate performance phenomena |
| Wagner, Christian c.wagner@fz-juelich.de
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PGI-3 Molecular manipulation lab |
general overview over ML principles, PCA, background on methods to combine ML and comptational chemistry, reinforcement learning with NNs |
(hysteretic) scalars along (x,y,z), trajectories, computational, chemistry data, scalar time series |
easy access to ML expertise, possibility to discuss (and potentially solve) individual ML problems / tasks at detailed level, potentially in the frame of a collaboration |
| Hagemeier, Björn b.hagemeier@fz-juelich.de
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JSC Project Helmholtz Analytics Framework |
Basic ML methods |
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| Stadtler, Scarlet s.stadtler@fz-juelich.de
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JSC Federated Systems and Data Division |
Absolute Beginner (At JSC courses in ML and DL) |
Meteorological four dimensional Data (space and time) |
meet ML experts, possibility to discuss individual DL problems |
| Goergen, Klaus k.goergen@fz-juelich.de
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IBG-3 Integrated Modelling |
various supervised and unsupervised methods, PCA, CCA, SOMs; not used recently though |
Regional climate model outputs, meteorological observations |
big data (200-300TB) capable data analytics frameworks |
| Krajsek, Kai k.krajsek@fz-juelich.de
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JSC HPC in Neuroscience |
Supervised Learning Unsupervised Learning Meta-Learning, Inverse Modelling with ML, Deep Learning in Computer Vision, Probabilistic Inference, Gaussian Processes |
Meteorological model output (4D space-time volumes), Diffusion MRI data |
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| Pleiter, Dirk d.pleiter@fz-juelich.de
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JSC Technology Department |
Architectures optimized for DL, requirements analysis |
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| Arasan, Durai d.arasan@fz-juelich.de
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INM-1 Connectivity |
Support Vector Machines, Random Forests, Neural Networks |
3D MRI data |
Developing ML algorithms for neuroimaging data, Learning Deep Learning, feature engineering and optimization |
| Huysegoms, Marcel m.huysegoms@fz-juelich.de
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INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets, Markov Random Fields, Clustering |
Microscopic-resolution 2D and 3D cyto images |
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| Schultz, Martin m.schultz@fz-juelich.de
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JSC-FSD |
Deep Learning Timeseries and video-sequence analysis (IntelliAQ project) |
Observational time series, numerical weather model data (gridded fields), geodata (gridded fields and point features |
discussion forum to exchange experiences with specific architectures and methods, and to share ideas how to improve result |
| Schiffer, Christian c.schiffer@fz-juelich.de
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INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets |
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| Image Segmentation, Distributed DL on HPC with TensorFlow, Unsupervised Domain Adaptation |
Microscopic resolution 2D images |
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| Campos, Lucas l.campos@fz-juelich.de
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INM-1 Big Data Analytics Group (BDA) |
None; JSC courses on Machine Learning/Deep Learning. Weekend fun with SVM and Neural networks way back when |
3D MRI data |
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