| Arasan, Durai d.arasan@fz-juelich.de
|
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 |
| Axer, Markus m.axer@fz-juelich.de
|
INM-1 Fiber Architecture Group |
none |
Microscopic resolution 2D and 3D images, scalar and vector valued data |
|
| Campos, Lucas l.campos@fz-juelich.de
|
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 |
|
| Cavallaro, Gabriele g.cavallaro@fz-juelich.de
|
JSC High Productivity Data Processing Group |
Supervised learning, Unsupervised learning, Feature engineering |
Remote sensing data (optical and SAR) |
|
| Comito, Claudia c.comito@fz-juelich.de
|
JSC - Helmholtz Analytics Framework (HAF) |
Currently human-learning |
|
Probabilistic approach |
| Dammers, Jürgen j.dammers@fz-juelich.de
|
INM-4 MEG Group |
A bit of supervised Deep Learning techniques (CNN and DNN) |
4D MEG data |
Developing unsupervised deep learning strategies for the analysis of large sets of 4D neuroimaging data |
| 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 |
|
| Glock, Philipp p.glock@fz-juelich.de
|
INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets, Image Segmentation, pytorch, SVM |
Microscopic-resolution 2D and 3D cyto images |
|
| Gohlke, Holger h.gohlke@fz-juelich.de
|
JSC / ICS-6 / NIC Research Group |
Deep learning / Supervised/unsupervised learning |
Molecular structure data |
meet ML experts, possibility to discuss individual DL problem |
| 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 |
| Goergen, Klaus k.goergen@fz-juelich.de
|
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 |
| Grießbach, Sabine s.griessbach@fz-juelich.de
|
JSC SimLab Climate Science |
none |
satellite data, infrared spectra, meteorological data |
unsupervied ML |
| Haas, Sarah sa.haas@fz-juelich.de
|
INM-1 Big Data Analytics Group (BDA |
Deep Learning with ConvNets, Image Feature Detection |
Microscopic-resolution 2D and 3D cyto images |
|
| Hagemeier, Björn b.hagemeier@fz-juelich.de
|
JSC Project Helmholtz Analytics Framework |
Basic ML methods |
|
|
| 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 |
| Herten, Andreas a.herten@fz-juelich.de
|
JSC NVIDIA Application Lab |
Software installation |
|
Overview/support of ML/DL software in Jülich |
| Huysegoms, Marcel m.huysegoms@fz-juelich.de
|
INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets, Markov Random Fields, Clustering |
Microscopic-resolution 2D and 3D cyto images |
|
| 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) |
| Khalid, Fahad f.khalid@fz-juelich.de
|
JSC SimLab Neuroscience |
1) Experience in using CNNs for image segmentation. 2) Overview of literature on distributed training and inference for deep networks. 3) Setting up and testing DL frameworks on HPC infrastructure. |
1) Very high resolution 2D images captured using the Polarized Light Imaging technology 2) Low resolution volumetric image data from Magnetic Resonance Tomography |
Hardware infrastructure for training and inference, which is readily available at the JSC |
| Krajsek, Kai k.krajsek@fz-juelich.de
|
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 |
|
| Kraus, Jiri jkraus@nvidia.com
|
NVIDIA |
|
|
Want to learn the ML/DL needs of scientist at FZJ |
| Nöh, Katharina k.noeh@fz-juelich.de
|
IBG-1: Biotechnology, Modeling and Simulation Group |
Image segmentation with DL (U-Nets) |
Large-scale microscopic images from time-lapse microscopy (2D, x-TB range) |
Meet DL/ML experts, discuss DL solutions for specific applications |
| Pleiter, Dirk d.pleiter@fz-juelich.de
|
JSC Technology Department |
Architectures optimized for DL, requirements analysis |
|
|
| Schiffer, Christian c.schiffer@fz-juelich.de
|
INM-1 Big Data Analytics Group (BDA) |
Deep Learning with ConvNets, Image Segmentation, Distributed DL on HPC with TensorFlow, Unsupervised Domain Adaptation |
Microscopic resolution 2D images |
|
| Schultz, Martin m.schultz@fz-juelich.de
|
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 |
| Stadtler, Scarlet s.stadtler@fz-juelich.de
|
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 |
| Schlottke-Lakemper, Michael m.schlottke-lakemper@fz-juelich.de
|
JSC/JARA SimLab Fluids & Solids |
Beginner |
Continuum mechanics (fluids, solids) |
Meet like-minded researches & ML experts to exchange ideas, learn who to ask in case of specific problems/questions |
| Schober, Martin m.schober@fz-juelich.de
|
INM-1 Fiber Architecture Group |
Beginner |
Microscopic resolution 2D and 3D images, scalar and vector valued data |
|
| Ungermann, Jörn j.ungermann@fz-juelich.de
|
IEK-7 |
Inverse Modelling, Linear Regression |
2-D and 3-D interferograms |
|
| 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 |
| Wenzel, Susanne s.wenzel@fz-juelich.de
|
INM-1 Big Data Analytics Group (BDA) |
Markov Marked Point Processes, rjMCMC |
|
|
| 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 |
