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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Imaging science
We use and develop advanced techniques to extract useful information from images and videos from a range of domains, including medicine, engineering, and biology.
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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
- Blockchain-based Local Energy Markets
- Computational Models to Predict Overnight Hypoglycemia in Type 1 Diabetes
- Contextualised Multimedia Information Retrieval via Representation Learning
- Data Integration & Exploration on Data Lakes
- 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
- Dynamic Resource Management for Intelligent Transportation System Applications
- Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
- Explainable and Interpretable Machine Learning
- Finding a way through the Fog from the Edge to the Cloud
- Fishing in the Data Lake
- Hardware Aware Training for AI Systems
- Integrated text and table mining
- Learning of user models in human-in-the-loop machine learning
- 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
- Multi-task Learning and Applications
- Probabilistic modelling and Bayesian machine learning
- Problems in large graphs representing social networks
- Representation Learning and Its Applications
- 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
- Verification Based Model Extraction Attack and Defence for Deep Neural Networks
- Zero-Shot Learning and Applications
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
- Machine Learning and Cognitive Modelling Applied to Video Games
- Multi-task Learning and Applications
- Zero-Shot Learning and Applications
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
Andre Freitas projects
Simon Harper projects
Samuel Kaski projects
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
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