From sleep analysis, brain image analysis, and health prediction models over physics language models, AI writing assistants, and image segmentation to arctic climate and food design. Witness the pervasive presence of data science in all aspects of life with the new research projects in the years to come from the 16 new DDSA PhD and Postdoc fellows. Data science is everywhere!

More than 100 people from the Danish data science environment were gathered when the 10 new DDSA PhD fellows and the 6 first DDSA Postdoc fellows and their research projects were announced at ‘Celebrating Danish Data Science’ at Utzon Center in Ålborg this summer. We are proud to support their academic pursuits and are confident in their ability to make cutting-edge discoveries within data science together with their hosting universities.

Data Science is Everywhere!

Spanning the fields of Mathematics, Bioinformatics, Human-Centered AI, Cognitive Science, Statistics, Computer Science, and Physics among others, the 16 new DDSA fellows show the wide variety in the use of data science within different academic fields and disciplines. Methods and tools are spanning deep learning, computer vision, causal interference, machine learning, text analysis, deep generative models, neural networks, quantum inspired algorithms, 3D image analysis, and natural language processing, just to mention a few.

Stay tuned as we will bring you more in-depth insights into the innovative research of these talented data scientists on the journey of advancing, shaping and growing Danish data science. In the meantime, take a sneak peek into their exciting research projects in the videos below, or take a glance at the fascinating research projects of our first 10 DDSA PhD fellows 2022.

DDSA is funded by Novo Nordisk Foundation and VILLUM FONDEN.

Meet Our 6 DDSA Postdoc Fellows and get a Glimpse of their Fascinating Research

University of Copenhagen

Dustin Wright

‘Supporting Faithful Reporting on Scientific Research with AI Writing Assistant’

Technical University of Denmark

Ignacio Peis Aznarte

‘INR-Gen: Implicit Neural Representations Generation for Efficiently Handling Incomplete Data’

University of Copenhagen

Beatriz Quintanilla Cases

‘Exploratory Gastronomy (EXPLOGA): Turning Flavour Chemistry into Gastronomy Through Advanced Data Analysis’

University of Copenhagen

Daniel Murnane

‘Learning the Language of Reality: A Multi-tasking, Multi-scale Physics Language Model for High Energy Physics’

University of Copenhagen

Laura Helene Rasmussen

‘When Winter is Weird: Quantifying the Change in Winters Across the Arctic’

University of Copenhagen

Luigi Gresele

‘Causal Representation Learning: Conceptual Foundations, Identifiability and Scientific Applications’

Meet Our 10 New PhD Fellows, Who are Set to Make Significant Contributions to the Field of Data Science

University of Copenhagen

Arman Simonyan

‘New Machine Learning-Driven Approaches for Peptide Hormone and Drug Discovery’

IT University of Copenhagen

Sebastian Loeschcke

‘Tensor Networks for Efficient Image Rendering’

Aalborg University

Mikkel Runason Simonsen

‘Improving the Clinical Utility of Prognostic Tools Through Calibration’

University of Copenhagen

Javier Garcia Ciudad

‘Modelling Electrophysiological Features of Sleep and the Variation Induced by Differences in Species, Gender, Age, or Disease Using Deep Learning’

University of Copenhagen

Christoffer Sejling

‘New Methods for Functional Data to Quantify Clinically Relevant Traits of CGM (Continuous Glucose Monitor) Measurement Patterns and Guide Clinical Decision Making’

University of Copenhagen

Asbjørn Munk

‘Towards Theoretically Grounded Domain Adaptation for Brain Image Segmentation’

Technical University of Denmark

Jakob Lønborg Christensen

‘Diffusion Models for Image Segmentation’

Aarhus University

Jette Steinbach

‘The Impact of Genetic and Environmental Heterogeneity on Health Prediction Models’

University of Copenhagen

Mikkel Werling

‘Increasing Predictive Performance and Generalizability of AI in Healthcare Using Meta-learning and Federated Learning in an International Collaboration’

University of Copenhagen

Thomas Gade Koefoed

‘Resolving Insulin Resistance Heterogeneity in Human Skeletal Muscle Using Multimodal Single-nucleus Sequencing’