I am a PhD candidate in the Department of Social Statistics at the University of Manchester, affiliated with the Cathie Marsh Institute for Social Research (CMI) and the Mitchell Centre for Social Network Analysis, where I am supervised by Dr. Todd Hartman, Dr. Philip Leifeld, and Dr. Joshua Becker (UCL).

My doctoral research is based on a NWSSDTP CASE studentship in collaboration with an industrial partner Linney Create, a UK multi-channel marketing services group. Prior to my PhD in Social Statistics, I earned an MSc in Social Network Analysis from the University of Manchester and a BSc in Business Management from the University of Birmingham Business School.

I also have professional experience as a Behavioural Data Scientist for the UK Government's Department of Business and Trade in the Office of Product Safety and Standards, along with other internship experiences in data science/analytics in start-ups and consultancies in the UK and the US. Additionally, I have held Research Assistant positions at the LSE, University of Manchester, University of Birmingham, and a Pre-Doctoral Fellowship at the UCL.

My research lies at the intersection of computational social science, data science, and market research. I am broadly interested in the dynamics of attitude/opinion and emergence of collective human behaviour, or how micro-level interactions produce macro-level outcomes. My core interests include:

• Computational Social Science

• Agent-Based Modelling (ABM)

• Social Network Analysis (SNA)

• Opinion and Attitude Dynamics

• Complex Systems

• Machine Learning & Natural Language Processing (NLP)

• Big Data Extraction and Analysis

My PhD project, "Integrating Agent-based Models with Social Network Analysis: Uncovering Complex Phenomena in Market Research Data," aims to develop advanced statistical methods by combining ABMs and SNA to model and understand market-level data. The research will focus on addressing the long-standing challenge of causal and empirical investigation of complex social phenomena that result from the dynamics of interacting agents.

The aims of this project is to design robust measures to compare simulated consumer networks with observed historical data and to evaluate how modifications to the behavioural rules of agents influence the structure and dynamics of these networks over time. The outcomes of this work hope to contribute to both the understanding of attitudinal diffusion and consumer behaviour, and the practice of complex social systems modelling.

Email:

[email protected]