Together with the University of Groningen, Hamburg Observatory and Quantum Universe are organizing a Spring School on new methods in data science & astronomy.
In recent years, the rate of innovation in scientific information processing is showing an impressive upsurge. Innovations are taking place in all corners, from software engineering, traditional processing methodology and (deep) machine learning. Under these conditions it is difficult to keep up with these developments. Especially challenging is the absorption of deep learning methods in fields that traditionally are focused on 'first principles programming' with a healthy skepticism towards models with large number of parameters, such as neural networks. Experiences with such models may also have been less than stellar, in the past. However, in many disciplines such as medical image diagnostics and genomics, impressive breakthroughs are currently achieved by using deep-learning methods. It would be surprising if this state of affairs would have no impact on astronomy.
In this spring school, we will address data science in astronomy from at least three perspectives: The scientific research questions, traditional methodologies and deep learning. In the format of the spring school, ample time will be dedicated to cross-disciplinary discussion. The programme consists of seven-eight speakers. Active participation of the PhD students will be encouraged through various presentation sessions, plenary discussion and a hands-on hackathon (Python) addressing examples from traditional and deep image processing.
Organizers: Leon Koopmans (Groningen), Marcus Brüggen (Hamburg), Lambert Schomaker (Groningen)
Please register before January 15th 2020 by sending an email to Marcus Brüggen (email@example.com).
( ) I wish to participate in the lectures Monday and Tuesday (9 & 10 March)
( ) I wish to participate in the dinner on Tuesday evening in the “Wasserschloss” (Hafencity)
( ) I wish to participate in the XFEL excursion on 11 March