Health Data Science Day 2025
Health Data Science Day 2025 will take place on 6 November 2025 at the University of Copenhagen!
Health Data Science Day (HDSD) aims to bring together students, researchers, and industry employees that work across the wide field of health data science. The Center for Health Data Science (HeaDS) aims to assemble everyone from master's students, PhD fellows, early career researchers and established experts.
HDSD is a meeting place for those engaged or interested in health data science, no matter the path they took to get there.
Key information
Registration is free and open!
Speakers will be revealed on an ongoing basis - Keep your eyes on the HeaDS website, LinkedIn, and Mastodon to stay in the loop!
Important deadlines:
- Last day to register AND submit an abstract for a poster/lightning talk - 1 September
- Last day to register - 1 October
Health Data Science Day is a robust one-day conference:
Keynote Speakers – Two international keynote speakers will take the stage for 45 minutes each, with time built in for discussion.
Local Talent Talks – In an effort to highlight local(ish) talent, HDSD has invited 4 speakers across Denmark, Sweden, and Germany to give 15-minute presentations on their work.
Lightning talks – There are so many incredible researchers in and around Denmark. There will be a lightning talk showcase so we can hear from as many as we can. Talks will be 3 minutes each, challenging speakers to provide brief, informative, fun snapshots of their research. Attendees that submit poster abstracts will have the opportunity to opt in for lightning talk consideration. There will be a prize for the best lightning talk.
Poster sessions – There will be two reserved blocks of time for poster sessions. In each session, half of the poster authors will be stationed at their posters to talk to attendees and answer questions. There will be a prize for the best poster.
Networking – While attendees are encouraged to speak with their fellow conference-goers, we will host a reception following the second keynote speaker to allow attendees to connect with one another before going their separate ways.
Refreshments – HDSD will provide a light breakfast in the morning, coffee and tea throughout the day, lunch, an afternoon snack, and light fare during the networking session at the end of the day.
Keynote speakers:
Comparing calendars, making travel arrangements... Stay tuned!
Here's a hint: Big data. Quantitative genomics. Molecular bioscience. Complex psychiatric disease.
Unlocking the Potential of Health Data: Navigating the Challenges of Predictive Modeling in Clinical Decision-Making
The adoption of AI systems in medicine has become an essential asset for analyzing the vast amounts of multimodal health-related data generated daily. However, several challenges persist, including limited sample sizes, systematic biases, and the lack of interpretable and reliable predictions. To address these challenges, novel machine learning and bioinformatics algorithms are essential to support the analysis of the emerging complex multimodal biomedical data and their transition toward clinical applications.
Anne-Christin Hauschild heads the Institute for Predictive Deep Learning for Medicine and Healthcare at Justus-Liebig University Gießen. She was previously a junior professor at the Institute of Medical Informatics at the University Medical Center Göttingen and headed the Clinical Decision Support group. Her research is dedicated to developing machine learning methods and systems medicine approaches for analyzing biomedical data and their transfer into clinical practice.
Her group focuses on integrative analysis of multi-modal medical data, such as electronic patient records, molecular data such as gene and protein expression, and medical imaging data to support disease prediction and therapy optimization. ML technologies have been applied successfully in numerous health research domains, such as oncology, psychiatry, and cardiology. However, several challenges persist, impeding the translation of these advances into research and practice. Specifically, limited sample sizes, data privacy, and systematic biases within individual patient cohorts contribute to data scarcity and heterogeneity in medical registries and biomedical data. Additionally, the lack of interpretable and reliable predictions undermines trust in otherwise highly accurate models. Prof. Hauschilds group aims to address these challenges through employing and developing novel computational architectures and algorithms, including transfer learning to overcome data integration issues and accommodate small sample sizes, online and time-critical event prediction, federated learning for integrating distributed medical datasets and databases, as well as the use of explainable artificial intelligence methods to enhance model and prediction interpretability.
Local Talent Talks:
A Hub for Pharmaceutical Data Science Research and Education
Introduction, people, organization, ideas - How can we, the Center for Pharmaceutical Data Science Education, help you? How can we work towards increased data literacy in people and organizations across the pharma value chain? And what can we do with it?
Morten Lindow is the Head of Center for the Center for Pharmaceutical Data Science Education and Professor in the Department of Drug Design and Pharmacology. CPDSE is a close collaboration between the University of Copenhagen and the University of Southern Denmark. The goal of the center is to advance Danish education within pharmaceutical sciences through integration of data science.
Morten brings 17 years of experience to his work. He has turned numerous ideas into drug candidates, with five reaching clinical stages. A pioneer in computational design of oligonucleotide drugs, he has contributed to innovation through structure-activity relationships and kinetic models. Passionate about human-centered scientific collaboration, he has led projects at Roche and Santaris, integrating data science with drug discovery to advance RNA-therapy. His work has made significant strides in bioinformatics and organizational design, fostering cross-functional excellence.
Title - To be announced
Adam Hulman is a Senior Researcher leading the Machine Learning & Clinical Prediction Lab at Steno Diabetes Center Aarhus, Aarhus University Hospital, Denmark. He is also an Associate Professor at the Department of Public Health at Aarhus University. He has a PhD and 15 years of experience in working with diabetes epidemiology. His team’s overarching goal is to turn health data into clinical insights and applications by using advanced statistical and machine learning methods. More specifically, the group works on the development and application of deep learning methods to be able to integrate clinical data of different types (tabular, images, time series, voice) in risk prediction of diabetic complications.
He is committed to hearing the users’ voices about AI and to building bridges between diabetes researchers, clinicians and the data science community. Adam is an active academic citizen: steering committee member of the European Diabetes Epidemiology Group, member of the EASD Guidelines Oversight Committee, and chairman of the Danish Data Science Academy’s Cross-academy Collaboration Committee. He is associate editor at Diabetolgia and PLOS Digital Health. He received the Danish Diabetes & Endocrine Academy Education & Networking Award for his outstanding engagement in diabetes research education and training of early-career researchers in 2024.
Stay tuned for more speaker announcements!
How about a hint: Machine learning. Heterogeneous datasets. Multi-omics in time and space.
Stay tuned for more speaker announcements!
Okay okay one more clue: Computer science. Design thinking. Cyber-physical systems. Transdisciplinary research.
Questions?
Please reach out to heads-admin@sund.ku.dk with any questions.