We aim to set the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. Established in 2015, the Data Science Institute (DSI) has over 300 members and has raised £50 million in research grants.
In 2025, the Data Science Institute (DSI) at Lancaster University proudly marks its 10th anniversary. Since its founding in 2015, the DSI has established itself as a leading hub for cutting-edge research, interdisciplinary collaboration, and real-world impact in data science and artificial intelligence. Over the past decade, our researchers and partners have tackled some of the most pressing challenges in society, science, and industry—advancing the foundations of data science, fostering ethical and trustworthy AI, driving innovation across sectors and training 100s of data science practitioners.
As we celebrate this milestone, we reflect on the achievements of our vibrant research community and the transformative projects that have shaped the field. Looking ahead, the DSI remains committed to pushing the boundaries of data science and AI research, strengthening global collaborations, and supporting the next generation of data scientists.
Apply for funding for interdisciplinary research workshops
The Data Science Institute supports interdisciplinary research across a wide range of disciplines and interests. Institute members are passionately engaged in a wide range of data-intensive activity, which is addressing critical societal, economic and environmental questions, as well as generating new mathematical models and methods of digital innovation. We are delighted to collaborate with a wide range of centres across the University and take a broad definition of ‘data’ and ‘science’ - reflecting an inclusive outlook, which is critical to our mission.
To support the development of cross-disciplinary data intensive activity at Lancaster we are offering workshop funding for your projects. We welcome applications from colleagues with interests, which may include: the foundations of data science and AI, environmental data science, health data science, AI for design, AI and society, data and inclusion, digital innovation, citizen science and data visualisation. If you are unsure your proposal fits – please do drop a line to DSI.
Your workshops should aim to bring colleagues together for exciting dialogue and exchange, you may want to invite external colleagues from other universities, industry, policy, charity or civil society. Our only stipulation is that you must demonstrate collaboration beyond a single discipline.
We would like your suggestions for speakers for Autumn 2025 - please get in touch if you would like to present or have a nomination to make!
Data Dialogues is an informal, discussion-driven event where members of the DSI and the broader university community share insights into their work, spark interdisciplinary conversations and explore potential collaborations. The focus is on interactive engagement rather than formal presentations—so no slides (or just a few, if needed)! Instead, the idea is to introduce your work in an accessible way, followed by an open discussion and Q&A with attendees.
Get fresh perspectives and think about new ways of approaching your own research, meet new people and explore potential research collaborations. Come be part of the DSI community!
Title: A Mixed-Integer Linear Programming Framework for the Adversarial Training of Neural Networks
Abstract: The training of neural networks (NNs) is a necessary task to improve their generalisation ability, measured by their performance on unseen inputs. However, even trained NNs can be vulnerable to adversarial inputs, which are minimally perturbed versions of standard inputs that are incorrectly labelled by the NN. The adversarial training of NNs can help to increase their robustness and guard against adversarial attacks. Recently, mixed-integer linear programming (MILP) models have been presented which are used to model the process of classification through trained NNs. One prominent application of such models is the ability to generate adversarial examples through providing constraints on the target input while minimising the level of perturbation. MILP models have also been presented for training NNs, showcasing comparable accuracy to traditional stochastic gradient descent approaches.
In this talk, we use recent advances in the field of MILP to present the adversarial training of NNs as an optimisation problem. We present a number of settings for the presented framework which allow for training against various settings of adversarially generated inputs, with the goal of increased robustness at minimal cost to performance. Experimental results on the MNIST data set of handwritten digits evaluate the performance of the proposed approach, and we discuss how the framework fits within the state-of-the-art.
Dr Nathan Jones presents Cultivate, a cross-campus initiative linking culture, technology, and innovation, highlighting how it creates opportunities for collaboration between data science and the creative industries.
In January 2026, UKRI and AHRC will launch the next round of Creative Industries Clusters, each worth £6.5m over five years, with a focus on new geographies and ideas. With both AHRC and Visit England prioritising rural and coastal regeneration, including the Southport–Barrow corridor, Lancaster is ideally positioned to lead a nationally distinctive cluster with civic university partners. Building on major regional investments -- including Eden Project North, Barrow Rising, the Energy Coast, and Blackpool’s Silicon Sands -- the bid will demonstrate how culture and technology can work together to drive inclusive growth and regenerative practice. This represents a significant opportunity for colleagues working in data innovation, environmental research, and digital creativity to co-design research and ventures that shape the future of the North West’s coastal–rural economy. Cultivate provides the platform for developing this case, as a framework for culture that strengthens teaching, research, and engagement across our disciplines.
Date: 24th October
Location: Sky Lounge, Infolab 21, Lancaster University
Time: 11:00-14:00
Free event plus pizza lunch!
Are you an early career researcher (PhDs, postdocs and more) at Lancaster Uni or UKCEH working in ecology, environmental science or conservation? Whether you’re already using data science (includes AI and machine learning) or eager to explore new ways it can advance your research, this interactive session is for you.
Data Science at Lancaster was founded in 2015 on Lancaster’s historic research strengths in Computer Science, Statistics and Operational Research. The environment is further enriched by a broad community of data-driven researchers in a variety of other disciplines including the environmental sciences, health and medicine, sociology and the creative arts.
Foundations research sits at the interface of methods and application: with an aim to develop novel methodology inspired by the real-world challenge. These could be studies about the transportation of people, goods & services, energy consumption and the impact of changes to global weather patterns.
The Health theme has a wide scope. Current areas of strength include spatial and spatiotemporal methods in global public health, design and analysis of clinical trials, epidemic forecasting and demographic modelling, health informatics and genetics.
Data Science has brought new approaches to understanding long-standing social problems concerning energy use, climate change, crime, migration, the knowledge economy, ecologies of media, design and communication in everyday life, or the distribution of wealth in financialised economies.
The focus of the environment theme has been to seek methodological innovations that can transform our understanding and management of the natural environment. Data Science will help us understand how the environment has evolved to its current state and how it might change in the future.
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The Data Engineering theme aims to explore how we can utilise digital technologies to accelerate and enhance our research processes across the University.
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