Modern data analysis from theory to practice
We carry out research on theory, methods and applications of modern statistical and algorithmic data analysis, blending elements from various fields including statistical machine learning/AI, probabilistic programming, numerical ecology, and data science among others.
Our focus is on data-rich applications ranging from molecular life sciences to computational history. The research team is led by PI Leo Lahti who coordinates the Computational biosciences group in University of Turku, Finland. We are also founding members and part of Helsinki Computational History Group.
There is a great demand for targeted algorithmic methods to extract information and insights from data with minimal human intervention to guide modeling and experimentation. By combining information across multiple, complementary sources it is possible to overcome some of the limitations and statistical uncertainties associated with individual data sets. Open research practices play an important role in our all work.
A hierarchical Ornstein-Uhlenbeck model for stochastic time series analysis IDA preprint available.15 August 2018