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 over £40 million in research grants.
We are working to create a world-class Data Science Institute at Lancaster (DSI@Lancaster) that sets the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. DSI@Lancaster aims to have an internationally recognised and distinctive strength in being able to provide an end-to-end interdisciplinary research capability - from infrastructure and fundamentals through to globally relevant problem domains and the social, legal and ethical issues raised by the use of Data Science.
The Institute is initially focusing on the fundamentals of Data Science including security and privacy together with cross-cutting theme areas consisting of environment, resilience and sustainability;health and ageing, data and society and creating a world-leading institute with over 300 affiliated academics, researchers, and students.
Our data science, health data science and business analytics programmes have launched the careers of hundreds of data professionals over the last 10 years. Students from our programmes have progressed to data science roles at Amazon, PWC, Ernst & Young, Hawaiian Airlines, eBay, Zurich Insurance, the Co-operative Group, N Brown, the NHS and many others - please look at our Education pages for further details of the courses on offer.
Novel geospatial methods for combining data at multiple spatial and temporal scales in the context of historical disease mapping Home status students only
With the advent of advanced technology, a vast amount of data has become available, making the integration of information across spatial and temporal scales increasingly crucial for understanding complex systems and making informed decisions. For instance, in environmental sciences, researchers often seek to understand the impact of climate change on ecosystems and biodiversity. To achieve this, data must be integrated from various spatial scales, ranging from local habitats to regional landscapes, and temporal scales, spanning decades or even centuries. Similarly, in epidemiology, the spread of infectious diseases is influenced by factors operating at different spatial and temporal scales. For example, the transmission of vector-borne diseases like malaria or dengue fever depends not only on local environmental conditions but also on global climate patterns and human mobility.
However, existing geospatial methods often fail to effectively account for the misalignment in time and/or space of data from multiple sources, with an increasing need of novel methods that are tailored to the characteristics and complexities of the data being integrated.
This research project aims to develop a novel geospatial modelling framework to address these challenges and improve the integration of data across different spatial and temporal scales in the context of historical disease mapping. This framework will then be applied to mapping, analysing, and interpreting sixteenth-century primary sources on diseases and epidemics, utilizing datasets and methods from the ESRC project Digging into Early Colonial Mexico. Epidemics, like the recent COVID-19, have had profound and enduring consequences throughout history. The application area of this project will be in the context of the introduction of diseases, like smallpox, during the Conquest of America that led to catastrophic declines in populations, with mortality rates reaching 97% in some regions. Traditional research methods struggle with the sheer volume, incompleteness, and uncertainty of historical data. In this project we will leverage advancements in machine learning, corpus linguistics, and geographic information sciences presents opportunities to explore vast historical collections and better address data variability and uncertainty. Specifically, the student will:
The successful applicant will be supervised by an interdisciplinary team consisting of experts from history, linguistics, statistics and geography.
Prospective candidates should have a first or upper second-class honours degree, or a combination of qualifications or experience equivalent to that level in a relevant subject.
For informal enquiries about the project please contact Duncan Whyatt (d.whyatt@lancaster.ac.uk), Patricia Murrieta-Flores (p.murrieta@lancaster.ac.uk) or Emanuele Giorgi (e.giorgi@lancaster.ac.uk).
To apply, please send a CV and cover letter demonstrating your motivation for the post to dsi@lancaster.ac.uk . The closing date for applications is 15th July 2024 and we anticipate a start date of October 2024 for the successful candidate.
On the 29th – 30th July 2024, Lancaster University is hosting the first international conference on Natural Language Processing and Artificial Intelligence for Cyber Security.
This is the very first event of its kind and it could not come at a more critical time. In today’s digital age, cyber security has emerged as a heightened priority for both individual users and organisations. With the surge in online information, new innovative solutions are required to address the challenge of tradition security measures falling short against evolving threats.
The conference will also have a special theme track with the goal of stimulating discussion on the future of cyber security in the era of Large Language Models (LLMs) and generative AI. Delegates will examine the challenges, risks and safety issues associated with employing these models in everyday tasks, focusing on aspects including fairness, ethics, and responsibility.
Lancaster staff and students who want to attend, please fill out this form https://forms.office.com/e/mc9C3kChxP or contact Julia Carradus if you have any questions.
For more information on the conference please see the NLPAICS website or email info@nlpaics.com.
We are delighted to announce our workshops for 2024. Further details will follow in the DSI newsletter.
Katie McDonough (History) & Daniel Wilson (Turing Institute) Advertising Machines - 21st & 22nd March 2024
Elisa Rubegni, PhD (SCC) & Kate Cain (Psychology) Empowering Tomorrow: Bridging Disciplines for Inclusive Child-Tech - 2nd & 3rd May 2024
Kate Cain (Psychology) and Anastasia Ushakova (CHICAS/FHM) T-READS Tracking Reading and Educational Attainment through Data Science - 3rd & 4th June
Jess Bridgen (Mathematical Sciences) and Jon Read (Lancaster Medical School) Real-time Modelling of Nosocomial Transmission: the unanswered Questions - 11th June 2024
David Parkes (LEC), Luke Rhodes-Leader (Management Science), Paul Cureton (LICA), Eduard Campillo-Funollet (Mathematical Sciences). Calibration and validation for complex system modelling - 11th & 12th June
Paul Rayson (SCC), Jo Knight (CHICAS/FHM), Daisy Harvey and Nick King HealTAC 2024 Healthcare Text Analysis Conference 12th - 14th June 2024
Paul Smith (Mathematical Sciences), Alex Bush (LEC), Emma Eastoe (Mathematical Sciences) Environmental and Ecological Statistics Group - 1st-3rd July 2024 at Lancaster University
Zoe Alker (History) and Esmorie Miller (Sociology), The Technologies of Everyday Bordering, Past, Present and Future - 5th July at The Storey
Barbara Shih (Biomedical and Life Science) & Richard Mort (Biomedical & Life Science) Disentangling the genes contributing to Dalmatian spotting and associated disease - 10th July, Lancaster University
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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