Deutsch Intern
Astronomy

Data Lab

DataSphere@JMUW

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

Title
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
20.02.2020

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
27.02.2020

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)

03.12.2020

 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)