Developed LLM to enhance candidate feedback during psychometric tests. Aligned model development with company values, leveraging insights from research materials through collaborative teamwork.

Conducted an innovative study using advanced forecasting techniques and analytical methods to explore the relationship between beliefs, life satisfaction, fairness perceptions, and conspiracy theory endorsement. This research unveiled a significant correlation, revealing that higher life satisfaction correlates with reduced conspiracy theory endorsement, thus emphasising the profound influence of individual well-being on belief systems. Furthermore, distinct patterns of conspiracy theory endorsement were identified based on justice perceptions, offering valuable insights for interventions aimed at enhancing well-being and combatting misinformation.

Collaborated within the Nudge Unit of the Warwick Behavioural Insights on sustainability initiatives, focusing on reducing campus-wide water usage. Conducted community-oriented research, including surveys and interviews, resulting in a 2% decrease in water usage over 8 weeks, aligning with the university's sustainability goals. Employed a combination of behavioural analysis, engagement strategies, and collaborative initiatives to foster sustainable shower habits among students, leading to a significant decline in campus-wide water usage while grounding initiatives in practical approaches to enhance satisfaction and support conservation efforts, ultimately reducing water consumption on campus and fostering innovation in conservation methods.

Led an interdisciplinary team in comparing and optimising classification models for autism detection across three age groups, resulting in significant advancements in detection methodologies. Utilised JASP and R-Studio for comprehensive data analysis, achieving a remarkable 10% performance enhancement in Naïve Bayes and J48 models. These contributions not only drive technological progress but also directly benefit community welfare through improved autism detection methodologies.

Investigated the 'mad genius' hypothesis through a detailed analysis of Vincent van Gogh's letters spanning from 1872 to 1890. Employed Natural Language Processing and data science methods to uncover a significant decline in language concreteness over time. These findings shed light on the correlation between Van Gogh's mental health decline and shifts in artistic expression, contributing to notable advancements in art history.

Utilised statistical tools to analyse consumer demographics and assess fast fashion's negative impact, providing valuable insights for sustainable fashion strategies. Through these efforts, contributed to a significant 15% increase in local adoption of sustainable fashion practices, aligning business objectives with environmental consciousness and social responsibility.