Our Team

Introduction

The study of network data has traditionally been advanced in separate fields of research independently. However, dynamic network data give rise to analytic challenges across disciplines. Therefore, to tackle these research questions in a systematic way we need to bring probabilists, statisticians and application domain experts together. The NeST investigator team consists of mathematical researchers with complementary theoretical, computational, machine learning and data science expertise across six world-class institutes, collaborating together to drive leading and impactful research in network data science.

Academics

Principal Investigators

Dr Ed Cohen Photo

Dr Ed Cohen

Ed Cohen is a Reader in Statistics at Imperial College London. His research interests lie broadly in statistical signal and image processing, with particular areas of focus including:

  • Models and inference methods or multivariate and network event and count data,
  • Time-frequency methods for time series and point processes.
  • Online methods and changepoint analysis.
  • Spatial statistics.
Prof Nick Heard Photo

Prof Nick Heard

Nick Heard has a chair in statistics position at Imperial College London. His research interests include:

  • Modelling large dynamic networks
  • Statistical methods for cyber-security
  • Changepoint analysis
  • Computational Bayesian inference
  • Statistical approaches to clustering and classification
  • Meta-analysis
Prof Marina Knight Photo

Prof Marina Knight

Marina Knight is Professor of Statistics in the Department of Mathematics at the University of York. Her research interests include nonstationary time series, wavelet multiscale methods, statistical analysis of data collected on irregular and spatial structures such as networks, long-memory processes. These themes are typically stemming from problems arising in scientific fields such as biology, neuroscience and psychology.

Prof Guy Nason Photo

Prof Guy Nason

Guy Nason is Chair in Statistics at Imperial College London. His research interests are in time series, statistical learning, modelling, fair and ethical algorithms.

Dr Matthew Nunes Photo

Dr Matthew Nunes

Matthew Nunes is a Reader in Statistics at the University of Bath. His research interests include:

  • Models and inference methods for network data
  • Wavelet methods in statistics
  • Time series and image analysis
  • Differential privacy
  • Bayesian computation
Prof Patrick Rubin-Delanchy Photo

Prof Patrick Rubin-Delanchy

Patrick Rubin-Delanchy is Chair of Statistical Learning at the University of Edinburgh. His research interests span the fields of Statistics, Machine-Learning, Data Science and AI, and include data exploration; statistical testing; clustering; anomaly detection; embedding; graph analytics; behaviour analytics; manifold learning; topological data analysis; non-parametric statistics; high-dimensional statistics; representation learning; unsupervised learning; machine learning.

Prof Gesine Reinert Photo

Prof Gesine Reinert

Gesine Reinert is a Professor in Statistics at the Department of Statistics at the University of Oxford. Her research interests are centered around network analysis: probabilistic approximations, often using Stein’s method; statistical method development, and GNN approaches for network prediction tasks.

Prof Almut Veraart Photo

Prof Almut Veraart

Almut Veraart is a Professor of Statistics at the Department of Mathematics at Imperial College London. Her research focusses broadly on mathematical statistics, statistical methods for stochastic processes, ambit stochastics, financial econometrics and extreme value theory. Her specific research interests include continuous-time modelling of (network) time series, stochastic volatility models, spatio-temporal statistics, high-frequency financial data, modelling of energy markets, multivariate extremes and extremal clustering.

Prof Qiwei Yao Photo

Prof Qiwei Yao

Qiwei Yao is Professor of Statistics at London School of Economics and Political Science. His research interest includes High-dimensional time series, factor models, dynamic network, spatio-temporal processes, non-stational processes and cointegration, and nonlinear processes.

Email subscription

Stay up to date with our events