Data Science Research Programme
Science
The Faculty of Science
The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are driven by curiosity to expand fundamental knowledge and to look beyond the borders of their own discipline; their aim is to benefit science and to make a contribution to addressing the major societal challenges of the future.
The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organised in eight institutes. These institutes offer eight bachelor’s and fifteen master’s programmes. The Faculty has grown strongly in recent years and now has more than 2,300 staff and over 5,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together.
Data Science Research Projects
A new era for nature conservation using hyperspectral and lidar data; Oostvaardersplassen as a case study
Nuno César de Sá
This project aims to develop advanced data analysis methods for monitoring and increasing our understanding on biodiversity dynamics in nature reserves such as the Oostvaardersplassen. Earth observation methodologies have incredibly improved over the past decade. As a result, applications to nature management come in range, but these demand new ecoinformatics tools for nature conservation, e.g., for tracking animals based on hyperspectral data, and for linking spatial and temporal patterns of animal movement to vegetation characteristics.
Socially Embedded AI Systems
Tom Kouwenhoven
This interdisciplinary project lies at the intersection of AI, Cognitive Psychology, and Linguistics. It explores several adaptive machine learning methods which can give insight into the interaction between human and machine. The ultimate goal is open and natural communication between humans and AI that should result in mutual trust, cooperation and coordination possibilities between both. To do so, we attempt to create a natural setting that allows machine learning algorithms to learn complex human- and social characteristics.
Modeling interactions to unravel biomarkers for disease progression and treatment response
Laura Zwep
Large biobanking studies of healthy volunteers and patients are increasingly conducted for analyzing using molecular high-throughput molecular profiling (“omics”) technologies such as genomics, transcriptomics and metabolomics to obtain insights in molecular alterations underlying disease.
A major challenge for the analysis of such large clinical datasets associated with multiple high-dimensional datasets represents the integration of multiple omics technologies and typically longitudinally measured clinical data in a statistically and biologically meaningful way.