I am a PhD student in the DAI group, aligned with the UKRI Centre for Doctoral Training in Safe and Trusted AI (STAI CDT). My research focuses on developing causal machine learning methods to analyze complex genomic data. I aim to uncover how genes and molecular pathways interact to drive biological processes, with applications in predictive healthcare, stratified medicine, and biomarker discovery.
My work contributes to safe and trusted AI by prioritizing explainability and transparency in healthcare applications. By identifying causal mechanisms and actionable insights, I ensure that AI-driven tools in genomics are interpretable, biologically meaningful, and reliable for clinical use. This approach enhances trust in AI systems while supporting better diagnosis, precision in treatment, and improved patient outcomes.
I joined the STAI CDT because of its interdisciplinary approach to advancing AI which is both scientifically rigorous and ethically responsible. The CDT’s emphasis on explainability and societal impact aligns with my goal of developing AI solutions that empower researchers and clinicians while upholding safety, fairness, and trustworthiness in healthcare.
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Undergraduate Qualification: BSc Mathematics, Kwame Nkrumah University of Science and Technology
LinkedIn: https://www.linkedin.com/in/stephen-asiedu-maranatha/