Data science for understanding evolutionary processes and how life works in general, primarilly based on the global fossil record, as well as present day animal communities, climate, vegetation and environemnt. Research directions include large scale reconstruction of paleoclimate, understanding evolution of faunal communities, analyzing contexts of human evolution, and providing quantitative reasoning and future insights in the ongoing efforts to mitigate climate change.

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

Key publications

Media 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: