Bio
Nilo is a Turing Research Fellow working at the intersection of Humanities and Data Science.
He obtained his PhD in Linguistics from the University of Oxford and specializes in corpus linguistics, language typology, and the syntax-semantics interface, focusing on the computational modeling and statistical analysis of historical languages.
His current research at the Institute aims at automatically modeling discourse around poor mental health and working conditions to detect covert blame attribution in historical British newspaper data.
He was previously a Research Associate in corpus-based digital humanities within the Living with Machines project at The Alan Turing Institute and a Research Software Engineer for the Digital Scholarship @ Oxford project (DiSC) at the University of Oxford.
He is also particularly keen on issues around digital sustainability and data reproducibility in computational humanities, and he is currently a Fellow at RROx, the Oxford network of the UK Reproducibility Network (UKRN)
Research interests
Nilo's main research interest is the study of systematic cross-linguistic variation in the grammatical encoding of concepts and pragmatic functions.
He is particularly interested in adapting research methods from outside linguistics to linguistic questions and improving our understanding of how historical data can inform current real-world issues.
