Research themes

Our research is at an intersection of data science and evolutionary palaeontology. We focus on global scale analysis of biospheric change, evolutionary processes, reconstructing past climates and environmental change. In data science our primary focus is on methods for handling concept drift, and reasoning with evolving data. We are also concerned how to make robust, transparent and interpretable models in general.

Data science for understanding evolutionary processes and how life works in general using data of the global fossil record, present day animal communities, climate, vegetation and environemnt models and sensory observations. Research directions include analysis of macroevolutionary processes, large scale reconstruction of paleoclimate, understanding evolution of faunal communities and contexts of human evolution, and providing quantitative insights for mitigating climate change.

Keywords: Red Queen, ecometrics, macroevolution, biospheric change and conservation palaeobiology, human environments

Key publications

Press coverage

Our long tern focus in computational research has been on handling concept drift in machine learning. Traditional machine learning and data mining methods rely on the assumption that data distribution stays the same during model training and operation. As the world is continuously changing, so evolve data that describe it. We develop machine learning methods that can diagnose themselves and adapt to changing data distribution over time.

Keywords: concept drift, change detection, evolving streaming data

Key publications 

Applied data science is about translating human expertise from various domains to computational proxies and then modeling human expertise in a data-driven way. We have worked in data science in various domains, including: palaeobiology, forestry, biomedicine, chemical engineering, hardware design, traffic and mobility analysis, finance, process mining, sales analysis and customer profiling.

Selected publications

Fairness-aware machine learning studies in which circumstances algorithms may become biased towards or against groups of people, and how to make predictive models free from such biases, when data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. 

Selected publications: