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Imperial College London is unique in the UK as the only university to focus solely on science, engineering, medicine and business.
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.
The Department of Mathematics at Imperial College London is an internationally renowned department within one of the world's most prestigious universities. With their 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.
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 is Professor of Statistics, and Head of the Statistics section 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 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 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 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.
Emma McCoy is a Professor of Statistics at Imperial College London and a Strategic Leader for the 'Monitoring of complex systems' Grand Challenge of the Turing's programme in Data-centric Engineering. Emma’s research interests include time series, wavelets and causal inference, with a particular interest in transport studies. Emma will be teaching Exploratory Data Analytics and Visualisation in term 2.
Mark Briers is Programme Director for The Alan Turing. Prior to joining Turing, Mark worked in the defence and security sector for over 16 years, directing research programmes in the area of statistical data analysis, and leading large teams to drive impactful research outputs. He is an Honorary Senior Lecturer at Imperial College London, where he teaches methodological techniques for use in a Big Data environment and conducts research into statistical methods for cyber security, and he is a Council Member at the Royal Statistical Society. He is an Industrial Fellow alumnus of the Royal Commission for the Exhibition of 1851. Mark will teach Big Data: Scalability in term 3.
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 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.
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 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.
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