Statistical Science


Get involved with us

We welcome opportunities to work with external organisations including commercial organisations, government and NGOs. We have extensive experience of working with partners outside academia, and can deal with problems in a wide range of application areas. This page describes some of the ways in which we can work with your organisation, and the impact of some of our previous collaborations. For more information, please see the contact details at the bottom of the page.

Why UCL Statistical Science?

Our approach to any collaboration reflects UCL's strong interdisciplinary ethos. Our expertise can be envisaged as falling into four core streams reflecting areas of current importance in a data-driven world, linked through the statistical interpretation of evidence. For more information about the expertise available, see the department’s research themes.

Working with Us
  • Co-funded research projects. Research projects may address specialist questions requiring considerable technical expertise, or they may be used to translate state-of-the-art research and technology into a partner organisation. Such projects are often financed by public bodies such as the UK research councils, with a contribution from the partner organisation who is expected to benefit. Project durations typically vary from a few months to a few years. Examples of past projects include the economic evaluation of pharmaceutical interventions (in collaboration with Merck), the analysis of retail customer data (in collaboration with dunnhumby) and the modelling of extreme ocean environments for marine design (in collaboration with Shell).
  • Funded PhD studentshipsPartners can contribute to direct funding of PhD studentships – for example in full (approximately a total of £80,000 over 4 years), or in collaboration with UCL (eg through "Industrial CASE studentships", which are co-funded 50:50 by an academic and an industrial partner). We work with our partners to co-design and co-supervise PhD projects, ensuring both that the project topics are relevant for the whole supervisory team (including other academics or industrial partners) and that they contain a level of statistical innovation that is appropriate for doctoral-level study in Statistics. Industrial partners help us drive the research by exploring applied problems and helping seeking out innovative solutions. Co-supervision usually involves close collaboration between the student and the company, with potential for internships or working experiences. Examples of past projects include fully funded PhD, with sponsorships from companies including Buhler Sortex, Bresmed and dunnhumby, as well as the Defence and Science Technology Laboratory; and EPSRC Industrial CASE studentship in partnership with Shell Plc “Statistical Modelling of Extreme Ocean Environments for Marine Design”;
  • Training and professional development. We can provide training events, building on our cutting-edge expertise in the most relevant and current statistical methodology and applications. Events may be run by individual members of staff, or by teams of staff and research students with relevant expertise.
  • Undergraduate and Masters-level projects and internships.  Our undergraduate and Masters students are being trained to become independent quantitative problem-solvers and communicators. A student project, carried out as part of their degree programme, or an independent internship, can be an effective way for our partner organisations to scope out ideas and techniques in settings that are not mission-critical. Typically, such projects would involve a data-related task for the student, which benefits their academic progression as well as the partner. We work with our partners to co-design and co-supervise such projects. Examples include MSc projects developed in collaboration with companies including Buhler Sortex, Parexel and ICON
Examples of Training Events

As mentioned above, we have run a number of training and personal development events in the past. Examples include:

  • Short courses​ on computational methods. These short courses impart practical expertise in data-driven techniques to participants from government and industry and can lead to specific software prototypes, thereby making a tangible societal impact.
  • Hackathons hosted by selected partners to tackle real-world problems.
  • Placements at academic and non-academic partners​ to exploit synergies between our methodological aptitudes and the partners’ need for solutions, which in turn propels the state of the art in statistical science up the commercial value chain.
  • Software prototypes​ arising out of the short courses or hackathons. By providing both online and downloadable applications, prototypes will engage the wider public in raising awareness of the importance of data-driven analytics. Examples include our web-applications.
  • Media engagement to promote public understanding of data-driven decision making.
Our Research Impact

We work on several important research and application areas and the outcome of our collaborative work has produce notable impact on our industrial partners. Examples include the following.

  • Heart surgery risk calculator: Prof Rumana Omar and Dr Menelaos Pavlou have worked in close collaboration with clinical academics at UCLH to develop an innovative risk prediction model to assess the risk of sudden cardiac death and need for implantable cardioverter defibrillators in hypertrophic cardiomyopathy. The risk calculator has been recommended as the most appropriate tool by the European Society of Cardiology and has been used widely in clinical practice, since its introduction in 2017.
  • Rglimclim synthetic weather sequences: Research conducted in UCL’s Department of Statistical Science and led by Prof Richard Chandler developed a state-of-the-art software package for generating synthetic weather sequences. The software have been successfully adapted by engineers and policymakers internationally for assessing the effectiveness of potential mitigation and adaptation strategies in applications such as flood risk and water resource management. This led to the direct impact on commerce performance at the UK company, Mott Macdonald, that received USD360,000 in direct fees and on infrastructure development in Jamaica and Barbados to benefit approximately 350,000 people at a cost of approximately USD85,400,000. Additionally, the close working relationship between UCL and Mott Macdonald contributed to professional development of consultants at Mott Macdonald.
  • Statistical modelling for health technology assessment: Prof Gianluca Baio and the Statistics for Health Economics research group have worked on several collaborative projects with industrial partners. These projects mainly consists in applied methodological research to quantify the value-for-money of new health technologies. Examples include the assessment of the cost-effectiveness of universal programmes of vaccination against Human Papillomavirus. This research has been used to inform change in the Italian guidelines, implementing universal instead of females-only vaccination - a strategy later pursued by the UK government.

Make an Enquiry
To explore the possibility of working with us, please contact the department using stats.enterprise@ucl.ac.uk. When doing so, please provide a brief description of what you would be interested in collaborating on. Where relevant, please also provide information on practical matters such as anticipated time scales and potential confidentiality issues e.g. due to use of commercially sensitive data.