Parallel Sessions

Below you will find a list of all confirmed parallel sessions. No previous registration is needed, and attendance will be on a first-come first-served basis. Please note that the programme will be updated continuously, so keep an eye out for more information.


MONDAY, NOVEMBER 7, 13:45 – 16:00
PS1. Machine Learning in Production: MLOps is all you need?

Organised by Nicki Skafte Detlefsen (Technical University of Denmark) and Mikkel Baun Kjærgaard (University of Southern Denmark)

PS2. Generative models

Organised by Jes Frellsen (Technical University of Denmark)

PS3. Open science

Organised by Melanie Ganz (Rigshospitalet/University of Copenhagen) and Cyril Pernet (Rigshospitalet)

PS4. Data Quality in Data Science

Organised by Jacob Ramlov Jensen (Go Autonomous) and Hua Lu (Roskilde University)

PS5. Data Science in the Wild

Organised by Cecilie Utke Rank (Rigshospitalet/University of Copenhagen), Federica Belmonte (Danish Cancer Research Center) and Sebastian Weichwald (University of Copenhagen)

PS6. Patient benefit from risk assessment models and tools

Models for risk of disease, benefit from treatment, risk of outcome, among others, are increasingly published. As data are being assessable in terms of genetics, proteomics, metabolomics, radiomics, etc., we constantly gain more insight into the underlying biology and produce new models. With the use of machine learning, the models created become more complex, less transparent, and harder to validate. These are some of the reasons for modern risk modelling not being transferred at a high rate into the clinic for patient benefit. This workshop will concentrate on the modern risk model’s translation into the clinic through proper validation and quality assurance. The workshop will show examples from medicine and discuss the proper procedures for ensuring sufficient model quality to benefit patients.

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Organised by Mads Nielsen (University of Copenhagen), Sisse Rye Ostrowski (Rigshospitalet/ University of Copenhagen) and Deirdre Fenton (Aarhus University)

PS7. Applying Causal Methods

Organised by Leonard Henckel (University of Copenhagen)

PS8. Geometry and Topology in Machine Learning

Much of modern data is high dimensional, very complex, and highly nonlinear. Recently, methods building on the centuries of knowledge in mathematics of such structures have been successfully brought in.

This session gathers the Danish community in the area and also invites the rest of the Data Science community to learn about these methods and ideas.

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Organised by Lisbeth Fajstrup (Aalborg University) and Søren Hauberg (Technical University of Denmark)

TUESDAY, NOVEMBER 8, 10:15 - 12:15
PS9. Reproducible AI & Experiment Tracking using MLOps

Organised by Viktor Stenby Johanson (Technical University of Denmark), Joakim Bruslund Haurum (Aalborg University) and Kenneth Borup (Aarhus University)

PS10. Generative models

Organised by Jes Frellsen (Technical University of Denmark)

PS11. Data Science Educations: What is the Road to Success?

Organised by Mikkel Baun Kjærgaard (University of Southern Denmark) and Hua Lu (Roskilde University)

PS12. (Un)code the bias: Algorithmic Fairness in Data Science

Organised by Anastassia Vybornova (IT University of Copenhagen), Jens Ulrik Hansen (Roskilde University) and Federica Belmonte (Danish Cancer Research Center)

PS13. Bioinformatics

Organised by Roald Forsberg (Raven Biosciences)

PS14. Open Source

Organised by Andreas Tind Damgaard (Region Midtjylland/Danish Data Science Community)