Data science
Our expertise spans the full data science life-cycle: from information management and privacy, via machine learning and representational logics, to practical applications in bio-health informatics.
Our facilities
We boast an incredible array of facilities, making our innovative data science research possible.
A key feature of our approach is closely coupling methodology and application. This creates a self-fulfilling loop, where challenging real-world problems drive the methodology research agenda, but also provide a natural route to exploiting new algorithms and methods.
We believe this deeply multidisciplinary approach is one of the distinctive features of data science at Manchester.
Areas of expertise
Our researchers focus their work in the following specialist areas:
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Information management
We design, develop and build state of the art data and knowledge management systems -- spanning from formal underpinnings in knowledge representation and logic, to challenging interdisciplinary work.
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Machine learning and robotics
We develop and apply novel statistical Machine Learning methodologies, from theory to application, and push forward the state of the art in Human-Robot interactions with our Cognitive Robotics Lab.
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Natural language processing and text mining
We develop NLP and text mining techniques to provide insights for social scientists, biologists, and neuroscientists. Our strong interdisciplinary foundations support a world leading expertise in information extraction.
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Postgraduate research projects
Data science projects
- (MRC DTP) Unlocking the research potential of unstructured patient data to improve health and treatment outcomes
- Applying Natural Language Processing to real-world patient data to optimise cancer care
- Automated Repair of Deep Neural Networks
- Automatic Activity Analysis, Detection and Recognition
- Automatic Emotion Detection, Analysis and Recognition
- Automatic Experimental Design with Human in the Loop (2025 entry onward)
- Automatic Learning of Latent Force Models
- Blockchain-based Local Energy Markets
- Collaborative Probabilistic Machine Learning (2025 entry onward)
- Contextualised Multimedia Information Retrieval via Representation Learning
- Controlled Synthesis of Virtual Patient Populations with Multimodal Representation Learning
- Data Integration & Exploration on Data Lakes
- Data Lake Exploration with Modern Artificial Intelligence Techniques
- Data Wrangling
- Data-Science Approaches to Better Understand Multimorbidity and Treatment Outcomes in Patients with Rheumatoid Arthritis
- Deep Learning for Temporal Information Processing
- Design and Implementation of an FPGA-Accelerated Data Analytics Database
- Diabetes Tamagotchi's for Training Clinical Endocrinologists and Diabetologists
- Dynamic Resource Management for Intelligent Transportation System Applications
- Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
- Explainable and Interpretable Machine Learning
- Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
- Finding a way through the Fog from the Edge to the Cloud
- Fishing in the Data Lake
- Generative Artificial Intelligence as a Personalised and Adaptive Bolus Advisor
- Hardware Aware Training for AI Systems
- Integrated text and table mining
- Knowledge Graph for Guidance and Explainability in Machine Learning
- Learning of user models in human-in-the-loop machine learning (2025 entry onward)
- Machine Learning and Cognitive Modelling Applied to Video Games
- Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
- Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
- Multi-task Learning and Applications
- Music Generation and Information Processing via Deep Learning
- Probabilistic modelling and Bayesian machine learning (2025 entry onward)
- Problems in large graphs representing social networks
- Representation Learning and Its Applications
- Retrieved Augmented Generation with Data Lakes and Knowledge Graphs
- Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
- Security and privacy in p2p electricity trading
- Specifying and Optimising Data Wrangling Tasks
- Text Analytics and Blog/Forum Analysis
- Trustworthy Multi-source Learning (2025 entry onward)
- Verification Based Model Extraction Attack and Defence for Deep Neural Networks
- Zero-Shot Learning and Applications
Mauricio Alvarez projects
Richard Banach projects
Ke Chen projects
- Automatic Activity Analysis, Detection and Recognition
- Automatic Emotion Detection, Analysis and Recognition
- Biologically-Plausible Continual Learning
- Contextualised Multimedia Information Retrieval via Representation Learning
- Deep Learning for Temporal Information Processing
- Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
- Explainable and Interpretable Machine Learning
- Generative AI for Video Games
- Machine Learning and Cognitive Modelling Applied to Video Games
- Multi-task Learning and Applications
- Music Generation and Information Processing via Deep Learning
- Zero-Shot Learning and Applications
Jiaoyan Chen projects
Lucas Cordeiro projects
- Application Level Verification of Solidity Smart Contracts
- Automated Repair of Deep Neural Networks
- Automatic Detection and Repair of Software Vulnerabilities in Unmanned Aerial Vehicles
- Combining Concolic Testing with Machine Learning to Find Software Vulnerabilities in the Internet of Things
- Designing Safe & Explainable Neural Models in NLP
- Exploiting Software Vulnerabilities at Large Scale
- Finding Vulnerabilities in IoT Software using Fuzzing, Symbolic Execution and Abstract Interpretation
- Hybrid Fuzzing Concurrent Software using Model Checking and Machine Learning
- Using Program Synthesis for Program Repair in IoT Security
- Verification Based Model Extraction Attack and Defence for Deep Neural Networks
- Verifying Cyber-attacks in CUDA Deep Neural Networks for Self-Driving Cars
Alejandro Frangi projects
Andre Freitas projects
Simon Harper projects
- Diabetes Tamagotchi's for Training Clinical Endocrinologists and Diabetologists
- Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
- Generative Artificial Intelligence as a Personalised and Adaptive Bolus Advisor
- Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
Samuel Kaski projects
- Automatic Experimental Design with Human in the Loop (2025 entry onward)
- Collaborative Probabilistic Machine Learning (2025 entry onward)
- Learning of user models in human-in-the-loop machine learning (2025 entry onward)
- Probabilistic modelling and Bayesian machine learning (2025 entry onward)
- Trustworthy Multi-source Learning (2025 entry onward)
Dirk Koch projects
Tingting Mu projects
Mustafa Mustafa projects
Goran Nenadic projects
- (MRC DTP) Unlocking the research potential of unstructured patient data to improve health and treatment outcomes
- Applying Natural Language Processing to real-world patient data to optimise cancer care
- Data-Science Approaches to Better Understand Multimorbidity and Treatment Outcomes in Patients with Rheumatoid Arthritis
- Integrated text and table mining
- Text Analytics and Blog/Forum Analysis
Paul Nutter projects
- A New Generation of Terahertz Emitters: Exploiting Electron Spin
- Effective Teaching of Programming: A Detailed Investigation
- Extending Behavioural Algorithmics as a Predictor of Type 1 Diabetes Blood Glucose Highs
- Models of Bio-Sensed Body Temperature and Environment as a Refinement of Type 1 Diabetes Blood Glucose Prediction Algorithmics
- Skyrmionic Devices for Neuromorphic Computing
Norman Paton projects
Oliver Rhodes projects
Rizos Sakellariou projects
- Dynamic Resource Management for Intelligent Transportation System Applications
- Finding a way through the Fog from the Edge to the Cloud
- Job and Task Scheduling and Resource Allocation on Parallel/Distributed systems including Cloud, Edge, Fog Computing
- Managing the data deluge for Big Data, Internet-of-Things and/or Industry 4.0 environments
- Problems in large graphs representing social networks
- Scheduling, Resource Management and Decision Making for Cloud / Fog / Edge Computing
