Master of Science in Machine Learning and Data Science

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.

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.

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.

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 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.

Mikko Pakkanen is a Reader in Data Science and Quantitative Finance in the Department of Mathematics at Imperial College London. In the past, Mikko has held academic positions at the University of Waterloo, Canada, and at Aarhus University, Denmark. He received his undergraduate and doctoral degrees in Mathematics and Applied Mathematics, respectively, from the University of Helsinki, Finland. Mikko’s research interests include stochastic processes, machine learning and quantitative finance. He currently teaches modules in machine learning and statistical modelling of financial data. Mikko received a Faculty of Natural Sciences Prize for Excellence in Teaching at Imperial in 2018.

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.

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.

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.

Anna Calissano studied mathematical engineering at Politecnico di Milano in Italy and received her Ph.D. in collaboration with Danmarks Tekniske Universitet (DTU) in Denmark. Her research lays at the intersection between geometry and statistics and focuses on defining statistical methods for the analysis of sets of graphs and networks. Her methods have been applied to landscape designing, public transport modelling, and the analysis of medical images.

Deniz is a Lecturer in Statistics at Department of Mathematics, Imperial College London. His research interests span statistical machine learning and computational Monte Carlo methods. Previously, he was a Research Associate at The Alan Turing Institute as a part of the programme on Data-centric Engineering. He was also a member of the Computational Statistics and Machine Learning Group (CSML) at the University of Cambridge as a Visiting Research Fellow. Prior to that, he was a Research Fellow of the University of Warwick, based at The Turing, between 2019 and 2021.

He received his BSc & MSc degrees from Telecom Engineering from Istanbul Technical University (with specialisation on signal processing) and his PhD degree (Signal Processing) from Carlos III University of Madrid in 2019. During his PhD, he has held visiting researcher positions at Imperial College London, Fraunhofer HHI Institute (Berlin), and The Alan Turing Institute. Before starting his PhD, he worked as a quantitative researcher in a startup based at Istanbul, focusing on algorithmic trading, for a year.