About me

I was previously a research fellow in the COL laboratory in the University of Nottingham, working in the ENLIVEN European project. I am currently a teaching associate in the same university. This page serves as some sort of point of contact to find me.

I did my PhD on a SICSA Prize studentship to research argumentation stance mining in social media. In the middle of my academic ventures, I also worked as a data scientist in Cognitive Geology, a start-up based in Edinburgh, full of wonderful people, tackling the deficiencies of data analysis in petrophysical modelling software. My interests revolve around applied machine learning and information retrieval, but I am always open to try out new things through collaborative projects.

My CV is available here and my personal e-mail address is firstname.lastname@gmail.com (I tend to batch my e-mails, so don't be surprised if I am slow to respond). If you are a student, do not e-mail me on my personal e-mail address.

Schedule a supervision meeting with me.


If you want me to supervise your MSc project and have no idea what to do, a list of project ideas can be found here. If you want me to supervise your MSc project and know exactly what you want to do, I am open to new ideas.


My scientific interests revolve around applied interdisciplinary research involving machine learning at its core. I am particularly interested and open to collaborative work in the following topics:

  • Machine learning

    • Interpretability and explainability in machine learning models.
    • Sampling methods for budgeted learning.
    • Trust and fairness in algorithmic decision making.

  • Digital humanities

    • Argumentation mining for the detection of computational propaganda, the detection and classification of arguments in natural language, specially focused on forms of argumentation which appear in social media.
    • Social media tracking, more specifically when it comes to tracking social feedback to the spread of information.

  • Digital health

    • Decision making for the management of mental health, i.e. helping users understand their own health through information processing and visualisation.
    • Machine learning for physiological data, and particularly machine learning methods applied to the very small datasets generated by physiological measurements that are hard to acquire.


I am currently a module convenor on the following:

  • Winter term

    • Databases, Interfaces and Software Design Principles (G54DIS)
  • Spring term

    • Database and Interfaces (G51DBI)
    • Computer Graphics (G53GRA)


If you ended up on my page by mistake, you are probably looking for either one of the following:

Social media presence

I tend to be active, though rarely interesting, on the following social media websites:

LinkedIn    Twitter    Reddit