Our alumni

Tom Green

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

Supervisor: Dr Diana Maynard

Industry partner: Tribepad

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

Viva date: 29 November 2023

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

Industry partner: 3M Health Information Systems

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

Viva date: 2 May 2024

Current employer: Bose

Viva passed with minor corrections


I got my start in academia in electronic engineering and mathematics. I also had a keen interest in audio signal processing. I came to Sheffield to dive deeper into the machine learning of audio processing with respect to speech. My thesis was focused on speech enhancement and separation technologies with a view to multi speaker speech recognition.

PhD Summary 

Speech separation has seen significant advancements in recent years due to advanced deep learning techniques. However speech and reverberation still degrade the performance of these models. In this thesis, some inherent assumptions about the design of these models is challenged and a number new techniques for designing and training these models are proposed. These techniques result in improved model generalization, robustness and computational efficiency.


This thesis demonstrated the massive redundancies in some common approaches to speech separation research. It showed that performance of lightweight models can by improved by more intelligent analysis instead of massively increasing computational requirements. It also showed the same from the opposite end where large models contained significant redundancies because researchers had as yet failed to challenge preconceived notions about other model architectures.

The main benefit here is in the reduced training requirements for these models. Firstly, this is better for the environment which is better for society as a whole. Secondly, it demonstrates to researchers and practitioners they can save time and cost if they design their models more intelligently and take the time to challenge preconceived notions that have dominated this research field for many years now. 


Meg Thomas

Thesis: A multidisciplinary investigation of conversation and disfluencies in cognitive decline

Supervisors: Dr Traci Walker and Professor Heidi Christensen

Industry partner: Apple

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

Viva date: 13 June 2024

Cohort 1 Student Representative

Viva passed with major corrections


Peter Vickers

Thesis: Navigating Multimodal Complexity: Advances in Model Design, Dataset Creation, and Evaluation Techniques

Supervisor: Professor Nikos Aletras and Dr Loïc Barrault

Industry partner: Amazon

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

Viva date: 7 June 2024

Viva passed with minor corrections


Danae Sánchez Villegas

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

Supervisor: Professor Nikos Aletras

Industry supporter: Emotech

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

Viva date: 23 October 2023

Current employer: University of Copenhagen


Sebastian Vincent

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

Supervisor: Dr Carolina Scarton

Industry partner: ZOO Digital

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

Viva date: 28 November 2023

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.