United Kingdom
I’m an interdisciplinary data scientist with experience in simulation, machine learning, generative agent-based modeling, complex systems, and large-scale behavioral and urban data. My work spans designing data-collection experiments, building simulation and AI models, and turning messy, real-world datasets into actionable insights for decision-making. I’ve developed frameworks for social simulation, mapped global sustainability networks, built mobility and sensing data pipelines, and analyzed disruptions in rail systems. Across projects, I focus on combining data, modeling, and domain understanding to solve complex human and societal challenges. I’m driven by impactful, applied work, whether in climate, mobility, governance, or organizational systems, and enjoy collaborating across disciplines to build models that help people make better decisions.
- Developed a generative agent-based modelling framework to study polarization and tipping points in societal responses to climate action. - Led the writing of a perspective piece introducing a new generative-agent framework for social simulation and climate decision-making. - Co-organized a research retreat in Brussels with ~22 expert participants to explore new Generative AI methods for understanding social and socio-ecological systems.
- Built a network representation of ScotRail’s system (100+ stations and connections) and led an analysis of how major disruptions (e.g., the Ayr station fire) propagate through the network. Combined one year of rail data with road traffic data to offer insights for improving rail-service resilience. - Wrote a blog for the Leeds Institute for Data Analytics (LIDA) titled “Cracking Enigmas with Alan Turing Institute’s Data Study Group,” sharing key insights from my experience.
- Led a team of three to collect and process large-scale organizational data from online sources, covering 100,000+ entities. - Built a social network of 100,000+ organizations using NLP techniques, including name standardization and string-matching with distance metrics. - Analyzed network data for 200+ voluntary sustainability standards (VSSOs) and 50,000+ associated organizations to identify influential actors in the global sustainability landscape. - Studied 130+ agri-food VSSOs to uncover the role of national institutions in the geographic diffusion of sustainability standards.
- Developed a machine learning framework with 98% accuracy to detect human movement. Processed ~100 million data rows of multi-sensor data and fine-tuned 6 ML models to choose the best one. Ran the ML pipeline from data annotation, feature selection, model training, to fine-tuning hyperparameters. - Analyzed 500k+ urban movement trajectories (geospatial data) from 20,000 people in Singapore to uncover mobility patterns and the interplay between vertical and horizontal travel, generating insights for future transport and infrastructure design. - Performed spatial-network and correlation analysis on real user-movement data (50+ individuals, 500+ journeys) to understand how the layout and functions of an integrated development shape movement patterns.