I’m a postdoctoral researcher at the University of Manchester, UK, with research interests in Data Science, Human-Computer Interaction, and Recommender Systems.
Postdoctoral Research Associate
Leading a project with a start-up to build data pipelines to enable content recommendation based on user behaviour with a mobile app. The app is a work-based e-learning tool used by employees to learn skills relevant to their roles within the company.
The theme of my PhD is investigating how user interactions (such as clicks, keystrokes, and touch gestures) can be modelled to garner an understanding of the user’s experience with online and broadcast media. Currently, I’m exploring how large logs of interaction data, collected from live production experiences, can be used to discover predictive features of engagement and abandonment.
The application of my work is to:
- Have a positive impact on the user’s experience while consuming BBC content;
- Inform content creators through data-driven recommendations;
- Provide the BBC with an in-depth picture of the user’s experience through understandable metrics.
As part of my PhD, I’m lucky enough to be embedded into a development team in BBC R&D, where I work closely to shape how and what data is collected from live systems.
Before starting my PhD, I achieved a first-class MComp (Hons) degree in Computer Science at Newcastle University specialising in Big Data & Cloud Computing (2017).
While at Newcastle, I was a Research Assistant (Summer 2017) in the Scalable Computing Group on efficient entomological monitoring of dengue fever, under the supervision of Dr Paolo Missier, where I worked on evaluating a user ranking algorithm that was developed as part of my master’s and bachelor’s thesis. The position formed the continuation of an academic collaboration between Newcastle University and PUC-RIO.
For more detailed information, please refer to my CV.