Deutsch Intern

    Data Lab


    Co-working space with a GPU/CPU compute-cluster for researchers working on machine-learning problems

    The interdisciplinary datalab DataSpher@JMUW is a meeting point for researchers in the field of machine-learning in data-intensive sciences.  For access to the datalab, please contact the Chair of Astronomy by email. ML-researchers meet  in a joint seminar to discuss their projects.  Talks will be announced via an email list and this webpage.  A moodle-course on the WueCampus webpage and a workspace on slack (data-lab-astrowue) have been arranged.


    Date Speaker


    07.03.2019 Paul Ray Burd Radio frequency interference filtering with LSTM cells
    25.07.2019 Martin Blaimer

    Magnetic-Resonance-Imaging (MRI): Sequence optimization with Machine Learning


    Peter Dawood

    Neural Networks for Magnetic Resonance Imaging Reconstructions


    Rick Seifert

    DeepCLEM: automated Registration of correlative light- and electron microscopy Images with convolutional neural networks


    Jannik Stebani

    Enhancing quantitative Magnetic Resonance Fingerprinting via deep neural nets

      Andreas Berberich Generative Adversarial Networks for single-molecule localization microscopy
    Thursday 11-13 All Weekly datalab meeting for informatl discussions among machine-learning practicioners (ask Paul Burd for zoom meeting id and pwd)


    Karl Mannhiem opening words (2.15 p.m. - 2.30 p.m.)


    Thorsten Feichtner

    Evolutionary optimization of optical antennas (abstract) (2.30 p.m.-3.15 p.m.)


    Sebastian Reinhard

    An approach to combine compressed sensing and neuronal networks in single-molecule localization microscopy (abstract) (3.15 p.m. - 4.00 p.m.)

    18.03.2021 Elisabeth Fischer Integrating Keywords into BERT4Rec for Sequential Recommendation (abstract)
      Padraig Davidson Anomaly Detection in Beehives using Deep Recurrent Autoencoders (abstract)
    04.11.2021 Luca Kohlhepp ML driven RFI filtering (abstract)
      Sebastian Förtsch Applied Earth Observation (abstract)