Current PhD students

Constanza conducts research on the causal impact of front-of-package food labelling at the Cathie Marsh Institute for Social Research, University of Manchester. Her work integrates experimental and observational data to study consumer behaviour and health outcomes, using advanced statistical and causal-inference methods.

Aysha develops data-driven methods to estimate and forecast human migration at the University of Manchester and the Max Planck Institute for Demographic Research. Her work combines mathematical, statistical and computational tools to better understand demographic change.

Jessamy’s research uses mixed-methods, including secondary analysis of the Crime Survey for England and Wales and a Delphi Expert Survey, to explore student drug use and related health inequalities, aiming to inform harm-reduction approaches within higher-education settings.

Maria is an economist and data scientist at the University of Manchester whose research uses Topological Data Analysis to explore social-science challenges. She focuses on gender equality, inequality reduction and sustainable cities.

Ahmed is a researcher at the University of Manchester and the University of Tokyo whose work integrates machine learning into structural equation models. He applies advanced computational and statistical approaches to psychological and neuroscientific research.

Jingyi is based at the Cathie Marsh Institute for Social Research, University of Manchester. Her research focuses on policy diffusion within the field of social statistics.

Xinyi Kou is a researcher whose work focuses on harmonising international migration flow statistics in Africa. Her research applies mathematical and statistical methods to improve the measurement of migration.

Rahul researches statistical and computational methods of human mobility at the University of Manchester. His work focuses on estimating and forecasting migration flows from India, exploring how duration of stay and origin–destination dynamics shape migration patterns.

Zhaoyang Li focuses on the patterns of communication and meaning-making within feminist activism on Chinese social media, using social-semantic network analysis and longitudinal statistical models to capture online interaction dynamics.

Jackson is a researcher whose work focuses on migration, displacement and crisis-affected populations. He applies statistical and demographic methods to improve understanding and support humanitarian and policy responses.

Bahrul researches the application of computational statistics and machine-learning methods to evidence-based policy, particularly in Indonesia. His current work focuses on synthetic data publishing using deep generative models and is affiliated with the University of Manchester.

Kristians works at the intersection of computational social science, data science and market research. His research explores how micro-level social interactions produce macro-level outcomes, using agent-based modelling, social network analysis and machine learning.

Abdur-Rahman integrates statistical, computational and social-science approaches to explore how smartphone experience sampling methods can measure people’s wellbeing in regenerated urban public spaces. His work focuses on methodological challenges at the intersection of data analytics and society at the University of Manchester.

Md Mushtahid is part of the School of Social Sciences, University of Manchester, where he contributes to research in social statistics and data analysis.

Xiaomeng’s research focuses on working time quality and its implications for subjective well-being, particularly examining multitasking, fragmentation of working time and schedule instability using data from the UK Time Use Survey. She explores how these temporal patterns of work contribute to inequalities in well-being across gender and social class.

Ziyue researches non-response and attrition in longitudinal surveys, developing machine-learning and structural-equation models to enhance data quality and representativeness.

Isabella researches mental health and well-being among young people in the UK, focusing on social class, masculinity and the impact of New Labour government policies. She holds a Master’s in Epidemiology and a Bachelor’s in Life Sciences from Queen’s University at Kingston.

Xiaoyu Xu researches how family structure and intergenerational living arrangements relate to mental health, using longitudinal survey data. Her work applies social statistical methods to explore the links between family dynamics and wellbeing.

Dianqi researches migrant health and the well-being of older adults across the life course. Her work explores how individual behaviours and social determinants interact to shape health outcomes.

Wei’s research centres on gender and family inequality, focusing on parenthood, behaviours in paid and unpaid work, and well-being among dual-earner couples in the UK. She applies advanced quantitative methods to large-scale survey and time-use data to explore how household dynamics affect labour and life outcomes.