Digital Health and Wellbeing

Research

Data Science & Artificial Intelligence for Healthcare

Our Data Science and AI for healthcare research includes a range of topics from automatic medical image analysis, such as diabetic retinopathy severity grading and pathological tissue segmentation. Our research methods include deep learning neural networks and statistical Bayesian approaches. In order to improve the trustworthiness of deep learning approaches, we aim to develop their explainability with techniques such as uncertainty quantification and adding human-in-the-loop components through interactivity.

Techno-Social Networks & Socio-Economic Modeling 

We provide essential insights into the socio-economic effects of pandemics and the dynamics of social networks through sophisticated data analytics, machine learning, and computational modelling. Our research into residential clustering and the mobility of ethnic minorities in Finland reveals how urban migrational patterns and social factors contribute to spatial distributions and community dynamics. This comprehensive approach aids the policymakers and public alike, offering new perspectives on managing societal challenges and promoting wellbeing.

Our current projects

You can find our current research projects and their descriptions from the link below:

https://research.aalto.fi/en/persons/kimmo-kaski/projects/

Interested in joining us?

We are looking for Doctoral students and Postdoctoral researchers in Data Science & AI for Healthcare. Contact [email protected]