I am keen to supervise students for doctoral studies at Cardiff University’s School of Computer Science & Informatics. There are some proposal below that cover the kinds of things that I’d like to supervise.

I’m very open to enquiries from students from a variety of different disciplines, but especially so from Computer Science or behavioural sciences (e.g., Psychology, Sociology). My own undergraduate degree was in Psychology. I am mostly looking to recruit students with interests that match my own, but if you have an idea that relates to people and technology, we can certainly talk it over.

I am very happy to informally discuss your ideas, the process or a potential application at goulds@cardiff.ac.uk. Please do get in contact if you’re interested – I will not be able to support an application from a student I have never talked to. Looking forward to hearing from you!

Keeping users and citizen scientists in the loop in transport modelling

Modelling transport of all forms (car, public transport, walking and cycling) remains highly relevant to Net Zero goals, inclusivity and economic development. Transport modelling however typically takes place within an organizational silo, in which consultants deliver a final model to local authorities who then proceed to public consultation. Transport models typically remain opaque to non-specialists in the communities whose futures they inform.

This project will attempt to take a citizen science approach to design methods for human-in-the-loop transport models, creating web interfaces and supporting communities to allow non-specialists to understand why the model predicts what it does, and to contribute local knowledge to the modelling process by raising concerns when modelling assumptions appear to contradict community knowledge.

You will be supervised by Sandy Gould (citizen science expertise) and Crispin Cooper (transport modelling expertise) on this exciting and highly interdisciplinary project.

Relevant publications from supervisors:

Rudnicka, A; Cox, AL; Gould, SJJ; (2019) Why Do You Need This? Selective Disclosure of Data Among Citizen Scientists. In Proceedings of CHI Conference on Human Factors in Computing Systems Proceedings. Paper #392 [PDF]

Rudnicka, A; Gould, SJJ; Cox, AL (2022). Citizen Scientists Are Not Just Quiz Takers: Information about Project Type Influences Data Disclosure in Online Psychological Surveys. Citizen Science: Theory and Practice [HTML] [PDF]

Cooper, C.H.V., Harvey, I., Orford, S. et al. Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows. Transportation 48, 643–672 (2021). [PDF]

Chan, E.Y.C., Cooper, C.H.V. Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling. Sci Rep 9, 19724 (2019). [PDF]

Is ‘flexible’ work flexible enough to be adapted to people’s routines and preferences?

You might have heard of the idea of ‘morning’ people and ‘evening’ people, or ‘larks’ and ‘owls’. We know these circadian rhythms play a role in for example, performance at School. A 9-5 job suits some people’s rhythms better than others. One of the promises of the ‘future of work’ is that technology-mediated work platforms and remote work will give people more control over where and when they work. Could this flexibility give ‘owls’ and ‘larks’ the chance to be productive at times that best suit them? Work has changed substantially and very quickly over the last year and answering questions about the ability of flexible working technology to serve workers’ needs is imperative if these changes are going to have a positive and lasting effect.

Project goals

The goal of the project is to understand relations between flexible work technologies and people’s daily rhythms, work preferences and routines. To do this, the project will need to:

• Build a deep understanding of flexible work technologies, especially the gap between what these tools promise and what they actually deliver;
• Empirically explore the connection between people’s circadian rhythms and work flexibility; and
• Develop new knowledge about the connections between working routines, flexible work technologies and rhythms.

The project could focus on different working contexts, for example where flexibility is ostensibly built-in (crowdsourcing platform work) and/or in more traditional forms of work where the nature of flexible work is changing.

Methods

The project will make use of empirical methods to understand people’s attitudes and behaviours in relation to work, routines and technology. Methods will adapt to the prior experience of the successful applicant, but could include:

• Telemetry-based data collection from digital devices (e.g., to understand people’s daily working patterns)
• Field experiments/interventions (e.g., to test whether theory-led changes to routines can be supported by technology)
• Diary collection
• Questionnaires (e.g., to understand workers’ attitudes toward flexible work technology)
• Interviews and focus groups The supervisors will support the successful applicant in developing research methods skills suitable for conducting high-quality mixed-methods and multidisciplinary research.

Keywords

Work, flexible work technologies, platform work, crowdwork, gig economy, habits, routines, circadian rhythms

Privacy-preserving telemetric data minimisation in remote work contexts

“Bossware” is a kind of technology used to surveil employees while they work. It has become more prevalent during the pandemic as people moved en masse to remote work, but telemetry-based tools for collecting, storing and managing information about workers’ behaviour have been used for a long time. There are a number of privacy and non-privacy concerns with these kinds of surveillance technologies. One is that they do not fit with the principle of data minimisation, which requires the necessary data for a purpose and no more to be collected. Collecting excessive amounts of data about worker behaviour increases the potential for harmful breaches and unnecessarily compromises workers’ privacy and autonomy for little tangible benefit. How can we measure important aspects of work using less data? How can we map and reduce high-frequency privacy-compromising measures into more tractable and manageable measures on people’s own devices? How can we give individual workers more control over the form in which they share their data with co-workers?

Project goals

The goal of the project is to understand how data minimisation methods can be used to reduce the amount of telemetry that devices need to ‘send home’. To do this, the project will need to:

• Develop an understanding of people attitudes toward workplace telemetry and data minimisation principles
• Explore the application of data minimisation methods to behavioural telemetry
• Demonstrate the practical effects of data minimisation methods on worker privacy and control. The project could focus on different working contexts, for example new digital-first work (e.g., platform work, crowdsourcing) and/or in more traditional forms of work where digital surveillance might be less integral to work.

Methods

The project will make use of empirical methods to develop a deeper understanding of telemetry and data minimisation. Methods will adapt to the prior experience of the successful applicant, but could include:

• Statistical analyses (e.g., to understand the contribution of different telemetry sources)
• Telemetry-based data collection from digital devices (so that minimised data can be compared with baselines).
• Field experiments/interventions (e.g., to test data minimisation techniques)
• Questionnaires
• Interviews and focus groups (e.g., to understand workers’ attitudes workplace telemetry) The supervisors will support the successful applicant in developing research methods skills suitable for conducting high-quality mixed-methods and multidisciplinary research.

Keywords

Bossware, telemetry collection, remote work, future of work, crowdwork, data minimisation, privacy