About Imperial College London

Imperial College London is the UK’s only university to focus solely on science, engineering, medicine, and business. Consistently ranked amongst the top 10 universities in the world, Imperial is home to a global community of scientists, engineers, medics, and business experts.

Their research-led approach shapes the way Imperial College educate students through teaching that opens everything up to question. It’s a style of learning that relies on learning by discovery and prepares graduates to bring fresh perspectives to the ever-evolving landscape of technology.

  • #6-ranked university in the world (QS World University Rankings 2023)
  • University of the Year for the Student Experience (The Times and Sunday Times Good University Guide 2022)
  • #1 in Graduate Employability Ranking (Guardian University Guide 2022)
  • #1-ranked UK university for “world-leading” research (REF 2021)
  • 1st in the UK for most innovative university (Reuters)

About the Department

The Department of Mathematics at Imperial College London is an internationally renowned department within one of the world's most prestigious universities. With an outstanding level of teaching and research, their principal aim is to train professional mathematicians and statisticians to pursue the study of scientific and technological problems using mathematical methods, and to undertake research in various branches of the subject.

There is great demand, both in research and in industry, for well-trained, highly qualified mathematicians and statisticians. Imperial College teaching and research programmes adapt and develop to meet this, aiming to give our students the skills they need to pursue the career of their choice.

  • Top 10 university in the mathematics and statistics ranking (Times Higher Education World Rankings 2020).
  • Imperial is home to world-famous mathematicians, including three winners of the Fields Medal, which recognizes outstanding mathematics achievement.

Featured Faculty

Nicholas Heard

Nicholas Heard

Chair in Statistics, Imperial College London

Nicholas Heard received the PhD degree from the Department of Mathematics at Imperial College London (ICL) in 2001 and currently holds a chair in statistics at ICL. His research interests include developing statistical methods for cyber-security, finding community structure in large dynamic networks, clustering and changepoint analysis, meta-analysis and computational Bayesian inference. Nick will be teaching Bayesian Methods in the second year of the programme.

Niall Adams

Niall Adams

Professor of Statistics, Imperial College London

Niall Adams is Professor of Statistics in the Department of Mathematics, Imperial College London. His research interests are focussed on anomaly detection and streaming data methods with application to areas such as cyber-security. He is the author of more than 80 refereed papers, and editor of 10 books. In the period 2011-2016, he leads the data science team at the Heilbronn Institute for Mathematical Research at the University of Bristol. Professor Adams will lead Programming for Data Science in term 1.

Christoforos Anagnostopoulos

Christoforos Anagnostopoulos

Head of Research, Improbable (Enterprise) & Honorary Senior Lecturer in Statistics, Imperial College London

Christoforos Anagnostopoulos is an Honorary Senior Lecturer in Statistics in the Department of Mathematics in Imperial College London, with a particular focus on probabilistic programming, graphical modelling and streaming data analysis. His research has been applied to a number of domains, ranging from cybersecurity to neuroimaging, and he has been working as an industrial R&D lead for several years in AI programmes for large enterprises. Christoforos will teach Ethics in Data Science and Artificial Intelligence in the programme.

Marina Evangelou

Marina Evangelou

Senior Lecturer in Statistics, Imperial College London

Marina Evangelou is a Senior Lecturer in Statistics in the Statistics Section of the Department of Mathematics. Throughout her career she has been interested in the development of statistical methods for the analysis of high dimensional and complex datasets from the fields of biology, health and medicine. Other interests include the modelling of cyber-security data-sources for the development of anomaly detection techniques. Dr. Evangelou will teach Unsupervised Learning in the second year.

Kevin Webster

Kevin Webster

Senior Teaching Fellow, Imperial College London

Kevin Webster is a Senior Teaching Fellow in the department of Mathematics at Imperial College London. His research interests are in the areas of machine learning, deep learning, dynamical systems, statistical learning theory and music information retrieval. Kevin Webster will be teaching Deep Learning during term 4 of the programme.

Chris Hallsworth

Chris Hallsworth

Senior Teaching Fellow, Imperial College London

Chris joined Imperial in September 2017 as Senior Teaching Fellow in Statistics. He is an applied statistician, with experience in a variety of problem domains, including genomics, continuous health monitoring and randomized controlled trials. Areas of statistical interest include time-structured data, smoothing models and neural networks. Chris is an active member of the Royal Statistical Society, sitting on the committees of the Business and Industry section and the Teaching Statistics special interest group.

James Martin

James Martin

Senior Teaching Fellow, Imperial College London

James Martin is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London. His research interests include the use of machine learning techniques in engineering problems, the use of spatial statistics in ecological applications and the development of likelihood-free approaches to Bayesian inference. James will be teaching the Applicable Maths course in Term 1.

Francesco Sanna Passino

Francesco Sanna Passino

Lecturer, Imperial College London

Francesco Sanna Passino is a Lecturer in Statistics in the Department of Mathematics. His research interests are broadly based on statistical analysis of large dynamic networks, mostly from a Bayesian perspective. His work has been mainly applied to statistical cyber-security problems, but also to social networks, music streaming services, and bike sharing systems. Francesco teaches the module ‘Big Data: Statistical Scalability with PySpark’ in Term 3.

Dean Bodenham

Dean Bodenham

Lecturer, Imperial College London

Dean Bodenham is a Lecturer in Statistics in the Department of Mathematics, Imperial College London. His main research interests are changepoint and outlier detection, particularly in streaming data. Dean teaches Programming for Data Science in term 1.

Ed Cohen

Ed Cohen

Reader in Statistics, Imperial College London

Ed Cohen is a Reader in Statistics in the Department of Mathematics. His research interests are broadly in the field of statistical signal and image processing, working at the interface of the mathematics, statistics, the natural sciences and engineering. This includes spatial statistics, for the analysis of multivariate event data, change point detection, and on-line estimation. I'm motivated by a range of applications, including biological imaging and cyber-security. He’s module lead for Unstructured Data Analysis, lectures Time Series Analysis on the undergraduate programme, and has previously taught Multivariate Analysis, Statistical Pattern Recognition, and Nonparametric Statistics.

Anthea Monod

Anthea Monod

Lecturer, Imperial College London

Anthea Monod is a Lecturer in Biomathematics at the Department of Mathematics at Imperial College London. Her research entails developing algebraic methods for handling structurally complex and nonstandard data arising from biology; she works in topological data analysis and algebraic statistics. Anthea will be teaching Unstructured Data Analysis in the second year.

Ciara Pike-Burke

Ciara Pike-Burke

Lecturer, Imperial College London

Ciara Pike-Burke is a Lecturer in Statistics in the Department of Mathematics at Imperial College London. Her research is in the field of statistical machine learning with a focus on sequential decision making problems. In particular, she has worked on multi-armed bandit, online learning, and reinforcement learning problems. Ciara will be teaching the Learning Agents module in the second year.

Zak Varty

Zak Varty

Teaching Fellow, Imperial College London

Zak Varty is a Teaching Fellow in Statistics and Imperial College London. His research focuses on developing novel methodology for industrial and environmental applications of statistics. This work often uses techniques from extreme value theory, point process modelling and spatial statistics. Zak teaches two modules: Ethics in Data Science and Artificial Intelligence and Supervised Learning.

Applications are open for the Fall 2023 cohort!

You will need to apply online directly with Imperial College London by registering and applying for the programme.

Please review the admissions requirements for the programme before applying.

If you have any questions about the admissions process or the programme, please do not hesitate to ask via ml-online-msc@imperial.ac.uk.

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