The development and mathematical/statistical analysis of algorithms that extract information from high-dimensional noisy data sets, network time series, and certain computationally-hard inverse problems on large graphs. Particular areas of focus include the statistical analysis of big financial data, statistical arbitrage, market microstructure, limit order books, synthetic data generation, as well as nonlinear dimensionality reduction techniques for high-dimensional time series data. (Lead: Mihai Cucuringu) 

Causal inference, in particular the identification and estimation of causal effects in situations where standard estimation techniques are invalid due to the presence of unobserved confounders. Research focuses on Instrumental Variables estimation, in particular testing for underidentification, weak instruments and the performance of weak instruments robust inference, and selection of valid instruments, incorporating machine learning techniques. These methods are applied across many fields of study, including biostatistics, epidemiology, where Mendelian Randomisation studies use genetic markers as instrumental variables for modifiable phenotypes, social sciences and asset pricing models in finance. (Lead: Frank Windmeijer) 

The group is actively involved in exploring statistical and mathematical problems that arise from the study of biological populations. Much of this work is devoted in particular to questions of biological ageing, including problems of mathematical evolutionary theory, mathematical ecology, methods for longitudinal social and medical data, and survival analysis. This work implicates a broad range of technical areas, from stochastic dynamical systems through Bayesian computation to kernel methods and deep learning, and applies these tools to relevant data that will help to illuminate fundamental issues about life history in humans and other organisms. (Lead: David Steinsaltz)