I am a machine learning researcher based at the School of Informatics within The University of Edinburgh, where I lead a research programme on verifiable and robust meta-learning funded by the Royal Academy of Engineering. I was previously a postdoc in the Machine Intelligence Group led by Prof Timothy Hospedales. Prior to this, I was a PhD student in the Machine Learning Group at The University of Waikato, supervised by Prof Bernhard Pfahringer.
I have a particular interest in developing and leveraging theoretical advances to improve how machine learning is done in practice. I am interested in applying machine learning to a variety of application domains, including signal processing, computer vision, natural language processing, robotics, and data mining. My current research is focused on developing machine learning systems that can seek out and/or leverage extra contextual information about the tasks to be solved, thus enabling one to learn more robust models with less data. Such contextual information could include examples of good models for related tasks, natural language descriptions of the phenomena being modelled, knowledge of underlying causal mechanisms, or other meta-data associated with tasks.