Our Research Students

I am a PhD student in Sustainable Accounting and Finance at the University of Edinburgh Business School, where I also serve as a CircHive and NatWest Group fellow and member of B-CCaS. My research specialises in accounting, financial, and economic empirical analysis of environmental geospatial data, focusing on pollution, biodiversity, circular economy, and climate change.

My methodology centers on quantitative analysis of firm-level data using econometric models, enhanced by data visualisation techniques. I approach financial metrics from the bottom up through accounting while understanding broader economic phenomena through political economy. Geospatial data forms the cornerstone of my research, allowing me to translate environmental insights to the firm level.

Beyond my academic pursuits in sustainable accounting and finance, I'm passionate about singing, dancing, video games, AI, science fiction movies, documentaries, and outdoor activities including swimming, tennis, hiking, and horse riding.

I am currently a PhD student at the Artificial Intelligence Applications Institute at School of Informatics in University of Edinburgh, proud to be supervised by Prof Tiejun Ma.

I finished my Master by Research degree (2021-2022) at the Institute for Language, Cognition and Computation in University of Edinburgh with Prof Shay Cohen and my Bachelors degree at University of Edinburgh as well.

Since the advent of large language models (LLMs), my recent research has primarily focused on applying LLMs and learning-to-rank models in financial applications. I also have experience in fundamental research on deep learning based ranking algorithms, retrieval-augmented generation (RAG), and financial synthetic data generation (SDV). Before LLMs emerged, I researched language model pretraining using mathematical corpora and syntactic parsing.

My name is Johnny Myung Won Lee and I am a PhD student at the University of Edinburgh under the supervision of Prof Miguel de Carvalho and Dr Daniel Paulin. I am affiliated with the School of Mathematics, Maxwell Institue for Mathematical Sciences and Edinburgh Centre for Financial Innovations. My research is currently fully funded by the School of Mathematics PhD Studentships.

My primary research interests lie in the field of Bayesian statistics, Bayesian regularisation, extreme value theory and Kolmogorov-Arnold neural networks. They share the same goal of utilising the information and discovering insights through data. Currently, I am developing regression frameworks for heavy-tailed distributions and extremes, incorporating shrinkage priors with applications in financial return modelling. Additionally, I am increasingly passionate about the intersection of statistics and everyday life. This includes applications that draw inspiration from extreme events, finance, natural language processing that use machine learning to accelerate scientific discovery.

As a PhD researcher in Statistics at the University of Edinburgh’s School of Mathematics, I explore LLM posterior sampling to refine AI-generated responses. Currently I'm collaborating with Abrdn Quant Team on AI-driven investment strategies. I enjoy solving abstract problems and finding clarity in complexity.

My research focus explores sampling techniques for large language models by extending the classical Metropolis-Hastings algorithm. In this framework, candidate replies are conditionally generated and then selectively accepted or rejected based on prior outputs, forming a Markov chain that progressively refines response quality toward a target distribution aligned with specific objectives such as enhanced creativity or improved politeness.

Additionally, the approach quantifies the semantic characteristics of generated replies by mapping them into a high-dimensional embedding space and evaluating their global similarity. An empirical probability mass function is then derived from these global similarity scores.

I am a Master of Research student at Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh. With a cross-disciplinary background in informatics, finance, and mathematics, my research focuses on fairness in generative AI, decentralised learning, and economic empirical analysis.

My research explores how generative AI, and synthetic data can reshape the foundations of fairness in intelligent systems. Drawing on approaches and perspectives from informatics, finance, and mathematics, I investigate the potential of generative AI in synthetic data generation, with a particular focus on fairness. I am also interested in decentralised learning systems, particularly in how collaboration can emerge without central coordination. A central theme in my work is the design of fairness-aware mechanisms that promote more responsible systems. Through this lens, my work contributes to broader conversations on building machine learning systems that are not only technically robust, but also socially responsive.