Data is the bedrock of digital transformation and innovation in the 21st century.
Data science is one of our most powerful tools for driving solutions for a changing future. Across all industrial sectors, data has helped unlock insights, improve efficiency, support new business models, improve safety and security, improve personalisation and consumer support, and give governments new tools for supporting populations.
At the University of York, we are using data in new ways to enable our partners to stay at the cutting edge of data science, whether the purpose is to enable safer data storage, analyse audience behaviour or creating more adaptable gaming software. We solve problems in active partnerships to deliver benefits to industry, society, and the environment, and we are training the next generation of data scientists to work with people as well as systems.
We welcome inquiries from industry, organisations, research institutions and researchers interested in working with world-leading, interdisciplinary researchers.
Our work on film
- Esports: The University of York hosts the strongest research environment worldwide on esports - video games played competitively. Here, our staff talk more about their work.
- Academic-industry partnerships for positive change in GUR: The world is facing unprecedented challenges. But games have an amazing potential to help. University of York researchers and international colleagues wat to continue the conversation.
I am a Professor at the University of York, part of the team managing DC Labs, as well as Lead Analyst for Weavr, Lead Industry Liaison for IGGI, and Co-Director for ARC, and affiliated with various universities, committees and networks.
I work with various forms of data from digital applications in order to improve decision-making across all stakeholders, including design, development, user experience and business processes in the Creative Industries and beyond. The reason I work with digital experiences is simple: They provide unprecedented access to incredibly detailed measures of behaviour. Using logging technology, it is possible to capture the second-by-second interaction between user and digital product.
I form part of an international community of data scientists and HCI experts in the private and academic sectors who try to derive meaning from user behaviour in digital applications and -environments, as well as their context, in order to inform design, development and business.
Read more about my work and potential student projects or see:
I am interested in Bayesian statistics and Probabilistic Programming. In particular, I am developing and applying Bayesian methods to animal social networks data to better understand animal societies and how that links to conservation. I am also interested in the use of Probabilistic Bayesian Programming for interpretable AI. Currently, black-box methods are popular in AI. Although they are good for predictions, they are not interpretable, and interpretability is essential for some problems.
I'm interested in humans and processes: in software engineering, in systems engineering and in innovation. For my PhD I examined software design and requirements engineering, and spent several years studying cyber-physical engineering processes, an area which brings engineers and computer scientists together to design systems collaboratively.
Currently, I'm researching how small firms in the creative industry collaborate with others to deliver innovative new ideas.
I am interested in the automated engineering of distributed and polyglot data persistence and analytics solutions. Relevant work in my team includes a framework for polyglot stream processing (Crossflow), a toolkit for engineering data vault applications which replicate personal data on user-owned devices and communicate via end-to-end encrypted channels (Vaultage), and an architecture for combining relational and non-relational databases to form scalable big-data polystores.
I am interested in representing and solving problems with decision variables and constraints, such as planning, scheduling, and industrial design.
Imagine assembling a wind turbine: various tasks (e.g. welding two pieces together) must be completed, and the entire assembly must be finished as quickly as possible. Each task has a duration, and some tasks cannot begin until another task is finished. Two tasks using one resource (e.g. a specialised machine) cannot overlap. Project scheduling problems such as this are theoretically hard and can be very challenging in practice.
Most of my research is on how to represent problems so that they can be solved efficiently.
I am a Lecturer in cyber cecurity and interested in data analytics for security and privacy. More specifically, I'm interested in data-driven approaches for designing better privacy-enhancing technologies, measurement, analysis, and combating web tracking, designing usable security and privacy mechanisms, and understanding human factors in security and privacy.
More information about my publications, contact details, and academic service can be found on my homepage.
Tommy is interested in argumentation, dialogue systems, natural language processing for argumentation and the application of machine learning techniques to various argumentation tasks, e.g. the use of reinforcement learning for agent learning to argue, graph neural networks for argument labelling, and LSTM with attention models for argument mining and fake news detection.
He has been supervising a few PhD in the area and he is fascinated in data-driven approach as well as knowledge-based approach to argumentation.
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Our students' projects
Our students are working on new ideas all the time. Learn about their projects here.Explore projects