Our alumni

Tom Green

Thesis: Using NLP to Resolve Mismatches Between Jobseekers and Positions in Recruitment

Supervisor: Dr Diana Maynard

Thesis Examiners: Dr Mark Stevenson (University of Sheffield) and Dr Rudy Arthur (University of Exeter)

Industry partner: Tribepad

Current employer: UK Government Department of Work and Pensions


Tom joined the CDT with a background in Psychology and Product Management. Keen to apply NLP techniques to live settings, Tom collaborated with recruitment software provider TribePad to investigate job recommendation and candidate ranking algorithms. He was supervised by Diana Maynard and Chenghua Lin. 

PhD Summary 

Recruiting through online portals has seen a dramatic increase in recent decades and it is challenging for jobseekers to evaluate the overwhelming amount of data to efficiently identify positions that align with their skills and qualifications. This research addresses this issue by investigating automatic approaches that leverage recent developments in Natural Language Processing (NLP) that search, parse, and evaluate the often unstructured data in order to find appropriate matches. 


This research informs the early stages of the recruitment process when candidates are searching for jobs to submit an application for that align with their particular skills and experience. This research improves upon simplistic methods that are commonly used, by leveraging state-of-the-art NLP techniques that are able to learn from past application outcomes, and considering the semantic content of salient entities in candidate profiles and job descriptions. Jobseekers will benefit from this research in terms of a more appropriate ranking of the available jobs which will reduce the amount of data to review to find appropriate matches, and recruiting agents will benefit from this research in terms of more appropriate ranking of candidates which will reduce the amount of data to review to find appropriate matches.    


Will Ravenscroft

Thesis: Mixed Audio Quality Meeting Transcription for Highly Reverberant Environments

Supervisor: Professor Thomas Hain

Thesis Examiners: Dr Yoshi Gotoh (University of Sheffield) and Prof Patrick Naylor (Imperial College London)

Industry partner: 3M Health Information Systems

Current employer: Bose

Viva passed with minor corrections


Meg Thomas

Thesis: A multidisciplinary investigation of disfluencies and cognitive decline

Supervisors: Dr Traci Walker and Professor Heidi Christensen

Thesis Examiners: Dr Stuart Cunningham (University of Sheffield) and Dr Leendert Plug (University of Leeds)

Industry partner: Apple

Cohort 1 Student Representative

Awaiting viva


Peter Vickers

Thesis: Common-Sense Reasoning from Multimodal Data

Supervisor: Professor Nikos Aletras and Dr Loic Barrault

Thesis Examiners: Dr Mark Stevenson (University of Sheffield) and Prof Yulan He (King’s College London)

Industry partner: Amazon

Awaiting viva


Danae Sánchez Villegas

Thesis: Beyond Words: Analyzing Social Media with Text and Images

Supervisor: Professor Nikos Aletras

Thesis Examiners: Dr Carolina Scarton (University of Sheffield) and Prof Andreas Vlachos (University of Cambridge)

Industry supporter: Emotech

Current employer: University of Copenhagen


Sebastian Vincent

Thesis: Context-Based Personalisation in Neural Machine Translation of Dialogue

Supervisor: Dr Carolina Scarton

Thesis Examiners: Prof Nikos Aletras (University of Sheffield) and Dr Alexandra Birch-Mayne (University of Edinburgh)

Industry partner: ZOO Digital

Current employer: ZOO Digital


I was born in Poland, moved to study in the UK in 2016. I have a BSc degree in Computer Science and AI. My PhD thesis is entitled "Context-Based Personalisation in Neural Machine Translation of Dialogue”. After the PhD I became an AI Research Scientist at ZOO Digital. 

PhD Summary 

My thesis explores personalisation of neural machine translation through the use of extra-textual information such as character and production metadata, in the domain of scripted dialogue. It puts forward a novel framework for working with such information and described an evaluation scheme for capturing how specific the queried translation (human or machine sourced) are to the provided context.