Jessica is a part-time DPhil student in Social Data Science. Her research lies at the intersection of Natural Language Processing (NLP) and representation learning, investigating how attention unfolds across time and contexts as a temporally structured phenomenon.
Her doctoral work addresses a structural tension between attention as interpreted in social systems and attention as implemented as a computational mechanism in NLP. She investigates how representation learning can reconcile these perspectives by treating attention as a temporally structured object and by encoding regularities and constraints on its development within learned models.
Alongside her research, Jessica works full-time as a Lead Data Scientist, developing machine learning systems for language modelling and complex information environments. She holds an MSc in Computer Science with a focus on NLP and Machine Learning.
Natural Language Processing; Representation Learning; Temporal Modelling; Language Modelling; Semantic Modelling
