This research investigates cognitive diversity in software engineering. Through eye-tracking and think-aloud studies, it shows that engineers approach problems differently, while existing software tools often favour particular thinking styles. Designing more inclusive tools could improve performance, broaden participation in decision-making, and reduce unintended bias in the technologies we build.

This research investigates how the choice of time interval affects reinforcement learning models for sepsis treatment in intensive care. By comparing one-, two-, four-, and eight-hour patient timelines, it demonstrates that shorter intervals better capture patient dynamics, improve estimated survival outcomes, and may enable more effective AI-assisted clinical decision-making.

This research investigates how people strategically spread gossip by reasoning about social networks. Through laboratory experiments and real-world friendship networks, it shows that individuals balance social distance and popularity to maximize information spread while minimizing personal risk, revealing sophisticated cognitive mechanisms that support human communication and social intelligence.

This research investigates how emotions contribute to financial panic using controlled laboratory stock market experiments. By combining sentiment analysis, facial expression recognition, and personality profiling, it aims to identify the emotional drivers of irrational selling behaviour and provide evidence for policies that promote greater financial market stability.

This research investigates how the brain makes decisions under uncertainty by studying mice navigating reward-based mazes. Rather than relying on memorisation, mice continually update mental models through active exploration. These findings improve our understanding of anxiety disorders and may inspire more adaptive artificial intelligence systems.

This research explores how heterogeneous AI agents can establish common ground during collaboration. By separating communication and action into distinct decision-making policies, agents can engage in micro-conversations that create shared understanding. The work aims to improve teamwork among diverse robots and support future human-AI collaboration in complex environments.

This research examines whether air pollution affects risk-taking behaviour. Using survey data from 40,000 Indonesians and satellite pollution measurements, it shows that higher pollution levels make people more risk-averse. Because risk preferences influence education, careers, entrepreneurship, and innovation, cleaner air may improve both health outcomes and economic decision-making.

This research investigates how reliance on AI systems affects human cognition and reasoning. Using concepts from cognitive offloading, the study compares AI-assisted and independent problem solving, measuring verification behavior, reasoning depth, and decision confidence. The work explores whether increasingly capable AI tools may unintentionally reduce critical thinking and human expertise.

This research uses the Manhattan maze to study rapid learning and memory in mice. The study demonstrates that mice can acquire complex navigation sequences after only a few rewards, retain memories overnight, and generalize learned strategies to new mazes. The findings provide insights into few-shot learning, memory formation, and adaptive intelligence.

This research examines how leadership behavior influences high-stakes decision-making in maritime operations. It highlights how human factors under pressure shape risk perception and outcomes, often more than technical systems. The study proposes a behavior-based decision framework to improve safety, efficiency, and cost-effectiveness in complex, high-risk environments at sea.