| Jitsev, Jenia |
j.jitsev@fz-juelich.de |
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) |
none |
| Wenzel, Susanne |
s.wenzel@fz-juelich.de |
INM-1 Big Data Analytics Group (BDA) |
Markov Marked Point Processes, rjMCMC |
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14.02.2020 |
| Dickscheid, Timo |
t.dickscheid@fz-juelich.de |
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 |
NVIDIA |
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Want to learn the ML/DL needs of scientist at FZJ |
none |
| Zimmermann, Olav |
olav.zimmermann@fz-juelich.de |
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 |
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| Herten, Andreas |
a.herten@fz-juelich.de |
JSC NVIDIA Application Lab |
Software installation |
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Overview/support of ML/DL software in Jülich |
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| Göbbert, Jens Henrik |
j.goebbert@fz-juelich.de |
JSC Cross-Sectional-Team Visualization |
Basic DL with Keras |
turbulent flows (3D) |
Collaborations/support for enabling DL with on HPC with Jupyter |
none |
| Hermanns, Marc-André |
m.a.hermanns@fz-juelich.de |
JSC Cross-Sectional-Team Parallel Performance |
none yet (testing with Keras atm.) |
Performance Data (profile and trace) |
Identify or estimate performance phenomena |
none |
| Wagner, Christian |
c.wagner@fz-juelich.de |
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 |
none |
| Hagemeier, Björn |
b.hagemeier@fz-juelich.de |
JSC Project Helmholtz Analytics Framework |
Basic ML methods |
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