Machine Learning

The Machine Learning theme consolidates a broad range of research activities in Informatics that are related to machine learning. The activities are related to methodology (models and algorithms), systems, as well as applications, and they span several areas including artificial intelligence, biomedical/health informatics, communications, computer vision, cybersecurity, data analysis, and robotics. Broad questions that have or are being addressed in the context of machine learning include: how to classify or cluster objects into meaningful categories, how to detect actionable patterns or extract relationships from data, how to explain the output of machine learning to users, how to predict values or trends from data, and how to recommend actions to users.

Key research activities in the area of Machine Learning:

Accountability and explainability of machine learning models
Adversarial machine learning
Automated reasoning processes through machine learning/data mining models
Automatic relationship extraction from unstructured data and for document classification
Clustering algorithms for data privacy protection
Computer vision through neural networks, deep learning, sparse dictionary learning
Communication of Narrowband Internet-of-Things devices and UAVs and resource allocation problems in full-duplex UAV-enabled communication networks
Control engineering applications for robotics
Detection of buried landmines in the ground through classification and prediction of landmine risk
Development of kernel methods, active search, statistical learning theory, large-scale learning methods, Bayesian optimization methods, genetic algorithms
Discrimination in machine learning
Disease classification and patient stratification
EMG/ECG/Bio-signal classification
Extraction of relationships from unstructured data
Hand motions and muscle activity classification
Human movement understanding in the context of robotics
Machine learning in teaching
Pattern mining algorithms for sequential data and data streams
Prediction of content that will be watched in BBC player, bias in social media, user engagement
Recommendation through randomized graph mining processes
Recommendation of social media content
Security and privacy of machine learning in the context of social media
Security through classification of malicious behaviours, concept drift detection in security tasks, frameworks based on supervised, online, and active learning, and evaluation of machine learning tasks in security contexts.
Text document classification

Members

Alfie Abdul-Rahman

Lecturer in Computer Science

Mohammad Abdulaziz

Lecturer in Artificial Intelligence

Laura Aboagye-Ward

Postgraduate Programmes Manager

Mohamed Abouhashem

PhD Student

Ruba Abu-Salma

Lecturer in Computer Science

Mubashara Akhtar

PhD Student

Bashir M. Al-Hashimi

Executive Dean, Faculty of Natural, Mathematical & Engineering Sciences

Kholoud Alghamdi

PhD Student

Rawan Alrahili

PhD Student

Bdour Alwuqaysi

PhD Student

Favour Amuno

Undergraduate Programme Officer

Munkhtulga Battogtokh

PhD Student

Katie Bentley

Senior Lecturer

Caitlin Bentley

Lecturer in AI Education

Davide Bianchi

PhD Student

Camille Bigot

PhD Student

Elizabeth Black

Reader in Artificial Intelligence

Rita Borgo

Head of Human Centred Computing Group

Martim Brandão

Lecturer in Robotics and Autonomous Systems

Neil Brown

Research Fellow in Informatics

Gerard Canal

RAEng UK IC Postdoctoral Research Fellow

Jonathan Cardoso-Silva

Affiliate Researcher

Lorenzo Cavallaro

Visiting Senior Research Fellow

Hana Chockler

Reader in Computer Science

Theo Chow

PhD Student

Oana Cocarascu

Lecturer in Artificial Intelligence

Andrew Coles

Reader in Artificial Intelligence

Amanda Coles

Senior Lecturer in Computer Science

Anna Collins

PhD Student

Michael Cook

Senior Lecturer in Computer Science

Kovila Coopamootoo

Lecturer in Computer Science

Colin Cooper

Emeritus Professor of Theoretical Computer Science

Jacopo Cortellazzi

PhD Student

Humphrey Curtis

PhD Student

Katie Day

Postgraduate Taught Programme Officer

Jacopo de Berardinis

Research Associate

Bart de Keijzer

Lecturer

Giulio De Pasquale

PhD Student

Gabriel Dias Cantareira

Research Associate

Senir Dinar

Lecturer in Computer Science Education

Yali Du

Lecturer in Artificial Intelligence

Partha Dutta

Lecturer in Artificial Intelligence

Jide Edu

PhD Student

Annis Etheridge

Undergraduate Programmes Officer

Maribel Fernandez

Professor in Computer Science

Nuno Ferreira da Silva

Network Coordinator

Xavier Ferrer Aran

Research Associate

Tuna Gönen

Research Software Engineer

Christopher Hampson

Lecturer in Computer Science (Education)

Howard Haughton

Senior Visiting Research Fellow

Nicolas S. Holliman

Director of CUSP London

Dong Huynh

Research Fellow

Costas Iliopoulos

Professor of Algorithm Design

Hari Indran

Departmental Officer

Tasmina Islam

Lecturer in Cybersecurity Education

Gunel Jahangirova

Lecturer in Computer Science

Zeliang Kan

PhD Student

Stephen Kell

Lecturer in Computer Science

Ian Kenny

Senior Lecturer in Computer Science Education

Jeroen Keppens

Senior Lecturer

Amir Kiani

PhD Student

Laura Koesten

Affiliate Researcher

Michael Kölling

Vice-Dean (Education) and Professor of Computer Science

Elisavet Koutsiana

PhD Student

Senka Krivić

Research Associate

Ashwini Kumar Singh

Research Associate

Agi Kurucz

Reader in Logic and Computation

Ievgeniia Kuzminykh

Lecturer in Cybersecurity (Education)

Kevin Lano

Reader in Software Engineering

Junho Lee

PhD Student

Stefanos Leonardos

Lecturer in Machine Learning

Matteo Leonetti

Lecturer in Autonomous Systems

Dimitrios Letsios

Lecturer in Data Science

Yijing Li

AEP Senior Lecturer in Urban Informatics

Derek Long

Professor of Computer Science

ACTION

Horizon 2020 programme that helps 15 citizen science communities around Europe fight pollution

Climate Finance Research Group

The Climate Finance Group is a collaboration between the Qatar Centre for Global Banking & Finance and the Departments of Mathematics and Informatics at King's.

CONSULT

Using wellness sensors to support patient decisions in managing their healthcare.

Data Stories

Engaging with data in a post-truth environment

IMPETUS

Citizen science projects in Europe

MDENet: The expert network for Model-Driven Engineering

MediaFutures

Reshaping the media value chain through responsible, innovative uses of data

Positioning Parent Positive (PPP) Study

A study of stakeholder views about the best way to optimise the clinical value of the Parent Positive digital parenting app.

Supporting Parents And Kids Through Lockdown Experiences (SPARKLE) Trial

A randomised control trial evaluating whether a digital public health parenting intervention can help parents to manage their children’s behaviour problems.