Study

The Centre for Decision Research in Leeds University Business School is keen to encourage applications from students wishing to undertake a PhD in the area of decision making, and would be interested in discussing potential projects with students who would like to study here. Proposals are welcomed in any area of decision research, but at this time the example topic areas listed for potential supervisors in CDR in the list below are of particular interest. To get a broader idea of each supervisors' areas of interest look at our publications or their profile page (links in the list below).

Details of research degree study at Leeds University Business School can be found here: https://business.leeds.ac.uk/research-degrees. This includes links to details on how to apply, and fees and scholarships (in the PhD tab of "In this Section"). We strongly advise discussing your proposal with potential supervisor(s) and naming them on your application.

 

Potential Supervisors in CDR

Professor Peter Ayton

• Effects of Computer Aided Decision Support
• Use of archival sports data to study choice behaviour

Dr Simon van Baal

• Investigations of (the linkages between) impulsivity, self-control and time preferences
• Predicting decisions using natural language
• Ecological approaches to decision-making

Dr Romain Crastes dit Sourd

• Mitigating the hypothetical bias in stated preference surveys
• New revealed preference data sources for non-market valuation

Dr Richard Hodgett

• Development and evaluation of new decision support systems for real industrial problems
• Development and utilisation of new analytical techniques to support industry decision-making

Dr Sarah Jenkins

• Risk perception and communication in applied contexts (e.g. medicine, natural hazards, food safety, product safety)
• Understanding and communication of weather and climate information
• Effects of source reliability and confidence on judgement and decision-making

Dr Gulbanu Kaptan

• Food-related decision making, sustainability-related decision making
• Developing risk (benefit) communications, and other interventions in relation to food and sustainability

Dr Irene Mussio

• The impact of risk and uncertainty in environment, health and behaviour using experimental economics methods (primarily lab-in-the-field and field experiments)
• Non-monetary valuation of environmental health hazards (including risk-risk trade-off methods)

Dr Nabi Omidvar

• Investigating machine learning and deep learning techniques for predictive modelling in financial and economic systems, using various modalities such as time series, images, and relational data
• Investigating how affective computing can reveal the links between affect and downstream financial behaviour, with applications in financial decision-making, consumer behaviour, and risk perception
• Investigating generative approaches for synthetic data creation to enhance predictive modelling and enable what-if scenario analysis in financial and economic systems

Dr David Palma

• Modelling time use: applying and developing new quantitative techniques to understand and predict the way people use their time
• Integration of Machine learning and econometrics in business analytics

Dr Edika Quispe-Torreblanca

• Influence of heuristics, mental accounting, and bounded rationality on financial decision-making, and their implications for consumer welfare.
• Consumer behaviour, status-seeking and herd behaviour, income inequality, and their relationship to subjective well-being.
• Topics within the behavioural household finance literature (saving, borrowing and investment decisions)

Dr Peizhi Shi

• Conducting data analysis across different sectors
• Employing machine learning and optimisation techniques (e.g., shallow/deep learning, computer vision, small-sample learning, and generalisable reinforcement learning) to support decision-making and operations management

Dr Sajid Siraj

• Eliciting preferences from decision makers using machine learning and multi-criteria decision analysis
• Mining association rules from large data sets using dominance-based rough analysis
• Investigating ways of structuring decision-making problems

Dr Panagiotis Stamolampros

• Influencer marketing and consumer attitudes
• Online information and cryptocurrencies

Professor Barbara Summers

• Individual decision making, considering cognitive and/ or emotional perspectives
• The effect of time pressure and other stressors on judgment and decision making
• Risk perception and communication

Dr Andrea Taylor

• Risk perception and communication, with particular interest in environmental risks (e.g. weather, climate, geohazards)
• Weather and climate information services for decision support
• The role of emotion in judgement and decision making

Dr Xingjie Wei

• Unstructured multimodal data mining: texts, images...
• Understanding and identifying human traits : faces, expressions, personalities, intelligence, confidence...
• Employing those computational techniques to improve business and management practice

Dr Joshua Weller

• Individual differences in personality, identity status, decision-making, & risk-taking
• Lifespan perspective on decision-making, including identity status, longitudinal assessment of decision-making skills
• Risk perceptions, with a focus on health behaviors and financial behaviors (e.g., food safety, behavioural addictions such as gambling, problematic gaming)

Find out more about postgraduate research at Leeds University Business School.

The MSc Business Analytics and Decision Sciences programme delivers the knowledge and understanding of how business analytics can provide evidence to support management decision-making.

Full details of the programme and how to apply can be found here.

The MSc Business Analytics and Decision Sciences was ranked in the 2025 QS Master in Business Analytics rankings:

• 7^th in the UK
• 21st in Europe
• 51-60 range in the world (up from 61-70)