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.

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

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.

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

Matthew Nunes is Professor of 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

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.

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.

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.

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.

Adrian obtained his MSc degree in Mathematics from Karlsruhe Institute of Technology in 2020 and his PhD from Université libre de Bruxelles in 2023. Since October 2023 he has been a Postdoctoral Researcher at the University of Oxford under supervision of Gesine Reinert, and will continue this collaboration as a PDRA under the NeST project.

Cristian is a research associate in Network Stochastic Processes and Time Series at the University of York working with Professor Marina Knight on the modelling of time series data collected over the nodes and edges of dynamic networks.

Prior to this, he completed his PhD in Statistics at King Abdullah University of Science and Technology (KAUST), supervised by Professor Ying Sun in the Environmental Statistics research group. Before his time at KAUST, he earned a master’s degree in applied mathematics from Universidad Nacional de Colombia Sede Manizales (UNAL), Colombia. Additionally, he served as a lecturer in the Department of Mathematics and Statistics at UNAL (2016-2020) and in the Department of Physics and Mathematics at Universidad Autónoma de Manizales (2017-2020).

Cristian’s research interests predominantly lie in time series and functional data analysis. His work is centred on developing statistical methods for modelling diverse data types, particularly those prevalent in environmental, economic, and financial applications.

Mahmoud Khabou is an associate researcher at the Imperial College London and holds a PhD in mathematics from the University of Toulouse III, France. Mahmoud’s research revolves around cross exciting/inhibiting point processes and count series. More specifically, he is interested in the modelling of seasonal count networks, as well as the stochastic control and the mean-field approximation of multivariate auto-regressive processes.

Currently, Mahmoud is working on the parametric estimation of a count network with time-changing regression coefficients.

Alexander Modell is a researcher in statistics and machine-learning at Imperial College London. His research is about understanding low-dimensional geometric structures in high-dimensional data, such as clusters, hierarchies, subspaces, and manifolds. Alex is particularly interested in spectral methods, kernel methods and deep learning and applies this work to the exploratory analysis of complex datasets, including large graphs, dynamic networks, natural language, and genomics data.

Amandine Pierrot is a Research Associate in the Department of Mathematical Sciences at the University of Bath, working on new models and analysis techniques for dynamic network data, applied to challenges arising from industry.

Before moving to Bath, Amandine graduated with a PhD from the Technical University of Denmark. Her thesis focused on forecasting offshore wind energy, through the online learning of probability distributions for bounded random variables. Prior to her PhD, she worked for eleven years as a research engineer in statistics for EDF, the main electric utility in France.

Amandine’s research interests include mathematics more broadly related to statistical learning, with a keen interest in time series forecasting and online learning. In particular, she is interested in probabilistic forecasting and forecast verification, hierarchical forecasting, distributed learning, game theory, differential privacy for time series, and modelling and forecasting dynamic network data.

Xinyang Yu is a researcher in the Department of Statistics, London School of Economics and Political Science. He is generally interested in different areas in Statistics, and his recent research topics include dynamic network modelling. More specifically, he is mainly working on linkage prediction, community detection and model selection of dynamic network with different structures.