I'm entering my second year at HKU this fall. It wasn't until now that I have somewhat solidified my interests to be at the intersection of Sustainability, Conservation, and Urban Planning of Asian Cities. Before joining uni, I took over three gap years working around the world and reflecting on my many identities. My work has spanned across Edtech, Heritage Conservation, Food Security, Nature Conservation, Policy Analysis, and Social Impact Sectors across the Global South. Beyond my work and research, I love to listen to music produced during from the 60s to 80s in anywhere around the world. I also love doing crafts as it notoriously saves me from supposed doomscrolling on Instagram. Also, I am secretly considering to debut as a private chef someday... who knows!?

I am currently a junior at Barnard College of Columbia University studying Computational Biology with a minor in Economics. Through my coursework and research experiences, I have developed a strong foundation in data analysis, quantitative modeling, and computational approaches to complex biological and social systems. My background in economics has further shaped how I think about resource allocation, structural inequality, and policy impacts at scale. I am particularly interested in applying data science and computational methods to questions in public health, migration, environmental justice, and global health systems. Much of my work focuses on how large-scale systems, whether healthcare infrastructure, environmental conditions, or social policy, shape health outcomes for marginalized populations. I am especially drawn to research involving spatial analysis, longitudinal datasets, and health data infrastructure, as well as projects that bridge technical analysis with real-world policy and community impact.

Hello! My name is Lochlann, and I am a student at Trinity College Dublin and part of the 2026 cohort of Laidlaw Scholars. My Summer 1 research project focuses on the role of private supplementary tuition (“grinds”) in the academic achievement gap between public and private schools in Ireland. Using quantitative analysis of Irish educational data, I aim to investigate whether access to grinds contributes to differences in examination performance and broader educational inequality. Through this research, my overarching goal is to contribute to conversations surrounding fairness, access to opportunity and educational policy. By examining how socio-economic advantage may shape educational outcomes, I hope to better understand the structures that influence inequality within education systems. If you have any questions or would like to connect, please feel free to reach out!

Hi everyone! My name is Anna and I'm part of the 2026 Laidlaw Scholars cohort at Trinity College Dublin, where I study Mathematics with a minor in Statistics. For my research project, I'll be exploring a question that is becoming more important as AI develops: who is better at predicting the future, humans or algorithms? I'll be comparing forecasts from machine learning models with those from prediction markets, where collective human judgement is turned into live probabilities. After the 2024 US elections, prediction markets rapidly gained attention for correctly predicting the outcome before many traditional forecasts. At the same time, AI is making data-driven forecasting more powerful than ever. Through quantitative analysis, I'm curious to see whether the best predictions come from human insight, algorithmic pattern recognition, or perhaps a combination of both. More broadly, I am interested in using mathematics to better understand and solve real-world problems like this one. I'm especially drawn to the way it combines logic with creativity, allowing us to approach challenges from different angles and develop efficient and innovative solutions. Outside of academics, I'm also a huge music lover, from hip hop and rock to hard techno. For me, a good time almost always involves music, dancing, or both :) Feel free to reach out if you want to discuss research, share music recommendations, or simply chat!