I am a PhD student specialising in the application of machine learning to mental health research, with a focus on predicting psychiatric outcomes using electronic health record data. My work integrates methods from time series analysis, natural language processing, and algorithmic fairness to build clinically relevant and ethically responsible predictive models. I have a background in cognitive science, and my research aims to help bridge the gap between computational methods and clinical practice.
My primary area of responsibility is machine learning for mental health research, including predictive modeling using electronic health record data, time series analysis, and the evaluation of fairness and bias in clinical prediction models. I also contribute to natural language processing projects, academic teaching, and interdisciplinary collaborations at the intersection of psychiatry, data science, and ethics.
