Ferran is an Enrichment PhD student working between The Alan Turing Institute and the CoMPLEX centre at University College London (UCL). His PhD research involves text mining and natural language processing (NLP) for biomedical information extraction. Previously, he obtained a BEng in Biosystems Engineering from the Universitat Politècnica de Catalunya where he joined the BIOCOMSC group and carried out research on agent-based modelling of anti-tuberculosis drugs. Following his graduation, he moved to London to pursue an MRes in computational biology at the CoMPLEX centre at UCL. His master's research ranged from molecular dynamics simulations of cell vesicles to machine learning approaches in cognitive neuroscience.
Outside his research, Ferran is highly involved in mentoring and teaching activities and has a keen interest in effective communication and education practices. Ferran is an avid chess player and has participated in numerous national and international competitions. In 2019, he won the blitz British Universities Chess Champion. As well as playing competitively, he currently coaches junior players ranging from beginner to advanced levels.
Ferran's current research focuses on centralising and standardising pharmacometric evidence found across the scientific literature by combining rule-based and machine learning approaches. His research is undertaken with support from BenevolentAI, and mainly involves document and sentence classification, biomedical named-entity recognition, entity linking and relation extraction from unstructured (full-text) and semi-structured (tables) sources. Ferran is particularly interested in efficiently dealing with the scarcity of annotated examples in the biomedical domain by applying techniques such as transfer learning, distant supervision and active learning.