

 Name (Last, First), mail  FZJ Institute/Group  Your ML expertise  Your type of data  Your needs 



:::::



 Jitsev, Jenia j.jitsev@fzjuelich.de  JSC Deep Learning Research Lab (CSTDL)  Unsupervised Learning, Reinforcement Learning, Recurrent Hierarchical WinnerTakeAll Networks, Generative Models, Biological Neural Networks, Openend 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@fzjuelich.de  INM1 Big Data Analytics Group (BDA)  Markov Marked Point Processes, rjMCMC   



Dickscheid, Timo t.dickscheid@fzjuelich.de  INM1 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   



 Kraus, Jiri jkraus@nvidia.com  NVIDIA    Want to learn the ML/DL needs of scientist at FZJ 



Zimmermann, Olav olav.zimmermann@fzjuelich.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 (2dim to ndim 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 nondifferentiable loss 



Herten, Andreas a.herten@fzjuelich.de  JSC NVIDIA Application Lab  Software installation   Overview/support of ML/DL software in Jülich 



 Axer, Markus m.axer@fzjuelich.de  INM1 Fiber Architecture Group  none  Microscopic resolution 2D and 3D images, scalar and vector valued data 



 Comito, Claudia c.comito@fzjuelich.de  JSC  Helmholtz Analytics Framework (HAF)  Currently humanlearning   Probabilistic approach 



 Dickscheid, Timo t.dickscheid@fzjuelich.de  INM1 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   



 Gohlke, Holger h.gohlke@fzjuelich.de  JSC / ICS6 / 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@fzjuelich.de  JSC CrossSectionalTeam Visualization  Basic DL with Keras  turbulent flows (3D)  Collaborations/support for enabling DL with on HPC with Jupyter 



 Grießbach, Sabine s.griessbach@fzjuelich.de  JSC SimLab Climate Science  none  satellite data, infrared spectra, meteorological data  unsupervied ML 



Hermanns, MarcAndré m.a.hermanns@fzjuelich.de  JSC CrossSectionalTeam Parallel Performance  none yet (testing with Keras atm.)  Performance Data (profile and trace)  Identify or estimate performance phenomena 



 Wagner, Christian c.wagner@fzjuelich.de  PGI3 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 



 Herten, Andreas a.herten@fzjuelich.de  JSC NVIDIA Application Lab  Software installation   Overview/support of ML/DL software in Jülich 



 Jitsev, Jenia j.jitsev@fzjuelich.de  JSC Deep Learning Research Lab (CSTDL)  Unsupervised Learning, Reinforcement Learning, Recurrent Hierarchical WinnerTakeAll Networks, Generative Models, Biological Neural Networks, Openend 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) 



 Krajsek, Kai k.krajsek@fzjuelich.de  JSC HPC in Neuroscience  Supervised Learning Unsupervised Learning MetaLearning, Inverse Modelling with ML, Deep Learning in Computer Vision, Probabilistic Inference, Gaussian Processes  Meteorological model output (4D spacetime volumes), Diffusion MRI data 



 Kraus, Jiri jkraus@nvidia.com  NVIDIA    Want to learn the ML/DL needs of scientist at FZJ 



 Hagemeier, Björn b.hagemeier@fzjuelich.de  JSC Project Helmholtz Analytics Framework  Basic ML methods    



 Stadtler, Scarlet s.stadtler@fzjuelich.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 



 Goergen, Klaus k.goergen@fzjuelich.de  IBG3 Integrated Modelling  various supervised and unsupervised methods, PCA, CCA, SOMs; not used recently though  Regional climate model outputs, meteorological observations  big data (200300TB) capable data analytics frameworks 



 Krajsek, Kai k.krajsek@fzjuelich.de  JSC HPC in Neuroscience  Supervised Learning Unsupervised Learning MetaLearning, Inverse Modelling with ML, Deep Learning in Computer Vision, Probabilistic Inference, Gaussian Processes  Meteorological model output (4D spacetime volumes), Diffusion MRI data 



 Pleiter, Dirk d.pleiter@fzjuelich.de  JSC Technology Department  Architectures optimized for DL, requirements analysis   



 Arasan, Durai d.arasan@fzjuelich.de  INM1 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@fzjuelich.de  INM1 Big Data Analytics Group (BDA)  Deep Learning with ConvNets, Markov Random Fields, Clustering  Microscopicresolution 2D and 3D cyto images  

...  ...  @@ 21,14 +22,13 @@ Dickscheid, Timo t.dickscheid@fzjuelich.de  INM1 Big Data Analytics Group (BD 


Campos, Lucas l.campos@fzjuelich.de  INM1 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  



 Dammers, Jürgen j.dammers@fzjuelich.de  INM4 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 



 Cavallaro, Gabriele g.cavallaro@fzjuelich.de  JSC High Productivity Data Processing Group  Supervised learning, Unsupervised learning, Feature engineering  Remote sensing data (optical and SAR)  



 Axer, Markus m.axer@fzjuelich.de  INM1 Fiber Architecture Group  none  Microscopic resolution 2D and 3D images, scalar and vector valued data 



 Khalid, Fahad f.khalid@fzjuelich.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 



 Glock, Philipp p.glock@fzjuelich.de  INM1 Big Data Analytics Group (BDA)  Deep Learning with ConvNets, Image Segmentation, pytorch, SVM  Microscopicresolution 2D and 3D cyto images  



 Comito, Claudia c.comito@fzjuelich.de  JSC  Helmholtz Analytics Framework (HAF)  Currently humanlearning   Probabilistic approach 



 Haas, Sarah sa.haas@fzjuelich.de  INM1 Big Data Analytics Group (BDA  Deep Learning with ConvNets, Image Feature Detection  Microscopicresolution 2D and 3D cyto images  



 Ungermann, Jörn j.ungermann@fzjuelich.de  IEK7  Inverse Modelling, Linear Regression  2D and 3D interferograms  



 Schober, Martin m.schober@fzjuelich.de  INM1 Fiber Architecture Group  Beginner  Microscopic resolution 2D and 3D images, scalar and vector valued data  



 Nöh, Katharina k.noeh@fzjuelich.de  IBG1: Biotechnology, Modeling and Simulation Group  Image segmentation with DL (UNets)  Largescale microscopic images from timelapse microscopy (2D, xTB range)  Meet DL/ML experts, discuss DL solutions for specific applications 



 SchlottkeLakemper, Michael m.schlottkelakemper@fzjuelich.de  JSC/JARA SimLab Fluids & Solids  Beginner  Continuum mechanics (fluids, solids)  Meet likeminded researches & ML experts to exchange ideas, learn who to ask in case of specific problems/questions 



 Grießbach, Sabine s.griessbach@fzjuelich.de  JSC SimLab Climate Science  none  satellite data, infrared spectra, meteorological data  unsupervied ML 



 Gohlke, Holger h.gohlke@fzjuelich.de  JSC / ICS6 / NIC Research Group  Deep learning / Supervised/unsupervised learning  Molecular structure data  meet ML experts, possibility to discuss individual DL problem 



 Nöh, Katharina k.noeh@fzjuelich.de  IBG1: Biotechnology, Modeling and Simulation Group  Image segmentation with DL (UNets)  Largescale microscopic images from timelapse microscopy (2D, xTB range)  Meet DL/ML experts, discuss DL solutions for specific applications  


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 Schober, Martin m.schober@fzjuelich.de  INM1 Fiber Architecture Group  Beginner  Microscopic resolution 2D and 3D images, scalar and vector valued data  



 Ungermann, Jörn j.ungermann@fzjuelich.de  IEK7  Inverse Modelling, Linear Regression  2D and 3D interferograms  



 Wagner, Christian c.wagner@fzjuelich.de  PGI3 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@fzjuelich.de  INM1 Big Data Analytics Group (BDA)  Markov Marked Point Processes, rjMCMC   



Zimmermann, Olav olav.zimmermann@fzjuelich.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 (2dim to ndim 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 nondifferentiable loss  