Research Supervisor Details

This page provides additional information about our research supervisors. You can either browser supervisors by department or search for them by keyword. Most supervisors also have a personal webpage where you can find out more about them.

Find by:
Please select the department to view:

Dr Behzad Abdolmaleki
behzad.abdolmaleki@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Professor Nikolaos Aletras
n.aletras@sheffield.ac.uk
Personal Webpage

Department of Computer Science
  • NLP
  • Computational Social Science
  • Legal NLP
  • Data Science
  • Machine Learning
Professor Jon Barker
j.p.barker@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Professor Barker’s research is concerned with speech processing in both humans and machines. His work in human speech processing involves developing computational models of human speech perception and applying these models in applications such as hearing aid signal processing and speech intelligibility prediction/enhancement. His work on machine speech processing focuses on distant microphone speech recognition for deployment in noisy environments. 

Dr Harsh Beohar
h.beohar@sheffield.ac.uk
Personal Webpage

Department of Computer Science

My research interests lie in developing new techniques or improve the existing ones for the behavioural analysis of concurrent systems. To this end, I use methods from algebra, logic, or/and category theory. In the past, I've worked on the following topics: coalgebras and their modal logic, model based testing of software product lines, semantics of hybrid systems, (pre)sheaves models for concurrency, and verification of asynchronous systems.

Dr Kirill Bogdanov
k.bogdanov@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

In traditional software development, specification and testing do not play an important role. In particular, changes to software code do not normally get reflected in a specificaton. At the same time, specification-based testing methods are very important for maintaing software quality, for identification of missing or incorrectly-implemented behaviour. K.Bogdanov's research aims to develop a method and a tool to take an incomplete state-based specification, hints for developers as to how it relates to code and both (1) extract an up-to-date specification and (2) generate tests from it.
A number of existing specificaton-based testing methods rely on a program under test being built with testing in mind, and lose a lot in power if this is not true. In his work, observation of program behaviour under test is used to make up for the missing information about a system, making it more amenable to testing using these methods. 
More recent work focuses on passive inference of software models from logs, where it is not possible to attempt experiments on a system being reverse-engineered.

Professor Kalina Bontcheva
k.bontcheva@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Natural Language Processing

Professor Kalina Bontcheva leads the Natural Language Processing (NLP) research group. Her main research interests are in NLP methods for online abuse and disinformation analysis, social media mining and summarisation, and biomedical text analysis. Kalina has published over 150 peer reviewed papers on these topics. She regularly reviews papers for high profile conferences and journals in the field of AI and its applications.

 

PhD Supervision

Professor Bontcheva is particularly interested in hearing from research students interested in the following areas:

  • Detection and analysis of online harm (disinformation, abuse etc)
  • Social media Analytics
  • NLP Infrastructures
Professor Guy Brown
g.j.brown@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Speech and Hearing

Professor Brown's main research interest is Computational Auditory Scene Analysis (CASA), which aims to build machine systems that mimic the ability of human listeners to segregate complex mixtures of sound. He has particular interests in reverberation robustness, models of auditory function in normal and impaired hearing, and sound localisation via binaural models. He is the co-editor (with DeLiang Wang) of Computational auditory scene analysis: Principles, Algorithms, and Applications (IEEE Press/Wiley-Interscience). A recent strand of work in his lab is looking at AI-enabled tools for music generation.

 

PhD Supervision

Professor Brown is particularly interested in hearing from research students interested in the following areas:

  • Sound source separation
  • Models of hearing impairment
  • Noise-robust methods for binaural sound localisation (e.g. by mobile robots)
  • Applications of AI to music generation
  • Deep neural networks for audio analysis
Dr Zhixiang Chen
Zhixiang.Chen@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Dr Chen Chen
chen.chen2@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Computer Vision for Healthcare

Dr. Chen Chen Chen is a Lecturer in Computer Vision at the University of Sheffield. Dr. Chen's research primarily revolves around the intersection of artificial intelligence (AI) and healthcare. Her work aims to develop and validate robust, data-efficient, and reliable machine learning algorithms that can enhance the scalability of AI-driven medical data analysis in practical applications. 

Dr Chen welcomes research students interested in the following areas:

  • Robust machine learning

  • Data-efficient learning: self-supervised learning, few-shot learning, semi-supervised learning

  • Multi-task and multi-modal learning, e,g., large language model guided representation learning

  • Adaptive machine learning, including unsupervised domain adaptation at training/test time

  • Algorithms with fairness, privacy, robustness, and interpretability in mind, including uncertainty-aware training, test time model calibration;

as  well as in their clinical applications including:

  • Medical image analysis (image segmentation, registration, motion analysis, and shape remodelling)

  • Quality control (e.g. topology preserving, uncertainty measurement, explainable AI for medicine)

  • Follow-up diagnosis, prognosis, survival/risk prediction, treatment planning

Please visit her personal website for more information.

Professor Heidi Christensen
heidi.christensen@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Speech and Hearing

Professor Heidi Christensen is a Senior Lecturer in Computer Science at the University of Sheffield. Her research interests are on the application of AI-based voice technologies to healthcare. In particular, the detection and monitoring of people’s physical and mental health including verbal and non-verbal traits for expressions of emotion, anxiety, depression and neurodegenerative conditions in e.g., therapeutic or diagnostic settings.

 

PhD Supervison

Professor Christensen is particularly interested in hearing from research students interested in the following areas:

  • AI-based voice technologies in healthcare
  • Detection and monitoring of people's physical and mental health
Professor John Clark
john.clark@sheffield.ac.uk

Department of Computer Science
I have a general interest in dependable systems and high integrity software and systems but my primary focus is on cybersecurity aspects.  I also have significant interests in the Internet of Things. I have particular interests in applying AI to problems in cybersecurity. Below is a summary of my main interests:
 
*  safe and secure systems
*  security of manufacturing systems, security of robotics and security of buildings
*. approaches to user authentication.
*. use of AI for crypto design and analysis
*  use of AI in quantum information processing (with a security focus)
*  use of AI for testing of modern critical systems (e.g. autonomous ones)
*  security and safety of AI
*  use of AI to reverse engineer hidden phenomena
*  use of AI in malware detection and intrusion detection.
*  use of AI in digital forensics
Professor Richard Clayton
r.h.clayton@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Complex Systems Modelling

Professor Richard Clayton's main research interest is developing computational and image-based models of electrical activation in the heart. This activity includes developing models of individual patients, analysis of data recorded from patients, fitting models to observations, and high performance computing for solving computationally intensive models.

 

PhD Supervision

Professor Clayton is particularly interested in hearing from research students interested in the following areas:

  • Using machine learning to calibrate models of individual patients.
  • Digital twins for medicine.
  • Reduced order models of complex systems.
  • Sensitivity and uncertainty analysis of cardiac models.
Professor Hamish Cunningham
hamish@gate.ac.uk
Personal Webpage

Department of Computer Science

Research interests

My work centers on Internet of Things (IoT) devices for sustainable food production. Increases in domestic and community food production promise to reduce transport-related carbon emissions and promote resilience in face of supply chain interruptions or economic shocks. Aquaponics is a low-impact, high-density agriculture method which has a recognised potential to increase sustainability of food production, according to the UN’s FAO and others (Somerville et al. 2014; Kotzen et al. 2013; Goddek et al. 2015). By combining elements of aquaculture and hydroponics, it offers reduced input requirements and waste disposal load in comparison to each, while still providing high volume fish and vegetable outputs. Year-round growing is possible even in unfavourable climates, and operations can be tailored to many types of environment, including arid, urban and peri-urban areas. The recirculation and biofiltration of water performed in aquaponics systems can reduce load on agricultural water sources (in contrast to both hydroponics and soil-based growing).
 
The use of aquaponics is increasing, but there are several factors which create barriers to entry, including:
  • Expertise. The method relies on a continuously balanced multi-element ecosystem and requires a combination of technical and agricultural skills that are not widespread. Further, optimal configuration varies radically according to local conditions, and a formulaic statement of how to tailor the method is not yet available for many cases.
  • Setup costs; energy usage. These are of the order of aquaculture and hydroponics systems, i.e. significantly higher than soil-based growing.
We are working to to lower these barriers by a combination of technical innovations (IoT devices, cloud-based data analytics and control dashboards) and social interventions.
 
We develop the unPhone ESP32-based IoT development platform, and the WaterElf aquaponics control and monitoring device.
Professor John Derrick
j.derrick@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Specification, refinement and testing using formal methods:

  • Refinement in state-based systems
  • Verification of concurrent algorithms
  • Testing distributed and concurrent systems
  • Integrated formal methods
  • Testing of formal specifications
  • Process algebraic refinement
  • Frameworks for distributed systems: architectural semantics, specification templates, object orientation, interfaces

I have specific interest in the use and theory of refinement in specifications languages. We have recently been applying this to the verification and liearizability of concurrent algorithms. Work on testing includes that on property-based testing for distributed applications (e.g. those written in Erlang), and reverse engineering. I have coordinated two EU FP7 grants in this area (ProTest and Prowess).


Dr Jefersson Dos Santos
j.santos@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Dr Benjamin Dowling
B.Dowling@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Dr Matthew Ellis
m.o.ellis@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Professor Rob Gaizauskas
r.gaizauskas@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Rob's research interests are in natural language processing, specifically in information extraction from natural language texts, software architectures for natural language processing and evaluation of language processing systems.

Dr Stefan Goetze
s.goetze@sheffield.ac.uk

Department of Computer Science
Dr Prosanta Gope
p.gope@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Security of Advanced Systems

Dr Prosanta Gope (PG) is a Lecturer in Cybersecurity at University of Sheffield (TUoS). He was a Research Fellow at National University of Singapore (NUS), working on two research projects: NETS (Network Engineering Techniques for Wireless Security), and NUS-Singtel Cybersecurity Project funded by Ministry of Defence Singapore (MINDEF), Singtel-Telecom Singapore, and Prime Minister Office Singapore, respectively. Dr. Gope has served as TPC Member/Chair in several international conferences such as IEEE GLOBECOM, ARES, IEEE TrustCom etc. He currently serves as an Associate Editor for the IEEE Internet of Things Journal, IEEE Systems Journal, IEEE Sensors Journal, the Security and Communication Networks.

 

PhD Supervision

Dr Gope is particularly interested in hearing from research students interested in the following areas:

  • Lightweight and Anonymous Authentication Protocol Design
  • Security and Privacy in Internet of Things
  • Security and Privacy in Mobile Communication
  • WSN and RFID Security
  • Security and Privacy in Smart-Grid
  • Hardware Security of the IoT Devices
  • 5G and Next Generaion Communication Security
  • Decentralised Communication Security (D2D Communication Security, Machine-type Communication Security)
  • Security and Privacy Tactile-Internet-based Applications
Dr Yoshi Gotoh
y.gotoh@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Yoshi has been working in the field of speech and spoken language processing for years. His current interests include audio visual processing, in particular, video analysis and video information retrieval.

Professor Thomas Hain
t.hain@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Thomas' research interests cover many areas in natural language processing, speech, audio and multimedia technology, machine learning, and complex system optimisation and design.

His interests include: large vocabulary continuous speech recognition, non-linear methods in speech processing, low bit-rate speech coding, machine learning, multi-modal systems, image classification, microphone arrays, system and resource optimisation.

Professor Jungong Han
jungong.han@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Prof. Jungong Han is the Chair of Computer Vision at the Department of Computer Science. He has published 90+ IEEE/ACM Transactions papers and 29 CVPR/ICCV/ECCV/ICML/NeurIPS/ICLR papers. He is the Fellow of the International Association of Pattern Recognition, the Associate Editor-in-Chief of Elsevier Neurocomputing and the Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Circuits and Systems for Video Technology, and Pattern Recognition.

 

PhD Supervision

 

Prof. Han is particularly interested in hearing from research students interested in the following areas:

1) Teaching machines to “see” and “understand” what happened, what is happening and what will be happening via computer vision and machine learning techniques;

2) Developing algorithms, e.g., learning to hash, to solve nearest neighbour search problems in order to facilitate fast large-scale data, e.g., image, video and text, cross-search;

3) Investigating open-world deep learning to solve a) the lack of labelled training data via self-supervised learning, few/zero-shot learning, and b) the shortage of computing resources on embedded devices like mobile via compressing and accelerating deep models.

Dr Mark Hepple
m.r.hepple@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Dr Hepple has wide-ranging interests across Computational Linguistics and Natural Language Processing, and has published on many topics, including formal grammar and parsing, information extraction, clinical text mining, temporal information processing, robust dialogue processing, and efficient storage of large-scale linguistic data.

Professor Robert Hierons
r.hierons@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Testing

Professor Rob Hierons’ research largely concerns software testing. The main aim of this research is to devise automated techniques (and tools) that generate efficient, systematic test suites on the basis of program code, models or specifications. Progress in this area can help industry to produce higher quality software and potentially to do so more quickly. He has recently become interested in the testing of autonomous systems, with a particular focus on robotics.


PhD Supervison

Professor Hierons is particularly interested in hearing from research students interested in the following areas:

  • Testing from formal specifications
  • Search-based testing
  • Automated test generation
Professor Vitaveska Lanfranchi
v.lanfranchi@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Her research has a fundamental interdisciplinary nature, and has developed both in industry and in academia. It concerns the intersection among ubiquitous computing, knowledge capture and visualization and human computer interaction in fields as diverse emergency response, mobility, smart cities, manufacturing, aerospace and more recently wellbeing. Her research focuses on user participatory design methods to develop novel methodologies and interfaces for ubiquitous and mobile computing.

Dr James Law
j.law@sheffield.ac.uk

Department of Computer Science
Dr Robert Loftin
R.Loftin@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Professor Haiping Lu
h.lu@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Professor Lu’s current research focuses on machine learning, brain imaging, and tensor analysis. His research also covers related areas such as big data, biomedical engineering, computer vision, and signal/image processing. His core expertise is tensor analysis and learning.

Dr Ning Ma


Department of Computer Science
Dr Steve Maddock
s.maddock@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Dr Steve Maddock's research interests include computer facial modelling and animation, surface deformation, AR and VR technology and applications, and sketch-based interfaces for simulation.

Dr Michael Mangan
m.mangan@sheffield.ac.uk
Personal Webpage

Department of Computer Science

My group uses bio-robotic methods to investigate how animals solve complex problems such as navigation before abstracting lessons learned to solve engineering goals. 

To reveal how animals function we utilise methods from computational neuroscience, behavioural ecology, graphics, information theory, computer vision, machine learning, and robotics disciplines. 

We then use more standard robotic and engineering methods to apply lessons to specific problem areas including robot controllers, novel sensing, and new methods of AI and machine learning inspired by natural intelligence.  We celebrate this truly multidisciplinary approach which we find both stimulating and challenging. 

Therefore we welcome exceptional candidates from across fields but those with strong backgrounds in mathematical, physical sciences and engineering disciplines (including computer science and computational neuroscience) are particularly well suited to research in my group.  

Dr Luca Manneschi


Department of Computer Science
Professor James Marshall
james.marshall@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Professor Marshall's research interests cover modelling of collective behaviour, particularly in social insects, evolutionary theory, decision theory, robotics, and theoretical neuroscience

Dr Diana Maynard
d.maynard@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Natural Language Processing

Dr Diana Maynard's research focuses mainly on developing information extraction tools to understand and aggregate information from text, especially those dealing with media and social media analysis, such as sentiment analysis and online abuse.

She is particularly interested in combining text analysis with behavioural and social information, and welcomes multi-disciplinary research in this area, especially in the food, environment/sustainability, and journalism domains. She is an active member of the Centre for Freedom of the Media and is involved with research around media freedom and safety of journalists. She also has interests in medical and legal NLP, and widespread uses of NLP in the humanities domains, including topics such as semantic enrichment in the GLAM (Galleries, Libraries, Archives and Museums) domain.

 

PhD Supervison 

Dr Maynard is particularly interested in hearing from research students interested in the following areas:

  • News and social media analysis 
  • Online abuse
  • NLP for food and sustainability issues
  • Semantic enrichment
  • Media Freedom and Safety
  • NLP and Social Behaviour
Professor Phil McMinn
p.mcminn@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Professor McMinn's broad areas of research are in software engineering and software testing. 
 
He is particularly interested in developing automated techniques to test case generation, fault detection, and fault fixing. His main expertise is in applying search heuristics to these problems – an area known as search-based software engineering (SBSE).
 
He is currently working on:
 
* Automatic test data generation for programs
* Automatic generation of tests for databases and database-centric software
* Automatic detection and fixing of "presentation failures" in web applications – visual discrepancies in the layout of a web page
* Automatic grading and feedback provision for programming assignments.
Dr Aryan Mohammadi Pasikhani
a.mohammadipasikhani@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Professor Roger Moore
r.k.moore@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Prof. Moore has over forty years experience in speech technology R&D, and much of his research has been based on insights derived from human speech perception and production.  In recent years, he has been working on a unified theory of spoken language processing in the general area of 'Cognitive Informatics' called 'PRESENCE' (PREdictive SENsorimotor Control and Emulation). PRESENCE weaves together accounts from a wide variety of different disciplines concerned with the behaviour of living systems - many of them outside the normal realms of spoken language - and compiles them into a new framework that is intended to breathe life into a new generation of research into spoken language processing, especially for Autonomous Social Agents and Human-Robot Interaction.

Prof. Moore is involved in collaborations aimed at Clinical Applications of Speech Technology (particularly for  individuals with speaking difficulties) as well as Creative Applications of Speech Technology through interactions with colleagues from the performing arts.

Dr Sagnik Mukhopadhyay
S.Mukhopadhyay@sheffield.ac.uk
Personal Webpage

Department of Computer Science

No details available

Dr Siobhan North
s.north@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Dr North currently works in two areas; XML databases and formal languages. The XML database work currently concerns indexing and compression techniques and the formal language work relates to translation between Isabelle and SAL.

Professor Pietro Oliveto
p.oliveto@sheffield.ac.uk
Personal Webpage

Department of Computer Science

My main research interests are in randomized search heuristics and randomized algorithms in general, with strong emphasis on runtime analysis and computational complexity. My main expertise is the analysis of bio-inspired search heuristics such as evolutionary algorithms, ant colony optimisation and artificial immune systems. My work focuses on understanding the working principles of algorithms inspired by nature and on analysing their performance.

I am currently working on:

  • population-based bio-inspired heuristics
  • parallel genetic algorithms
  • genetic programming
  • evolutionary dynamic optimisation
  • black box complexity
  • Fixed budget computation
  • bio-inspired heuristics for combinatorial optimisation.
Dr Venet Osmani
v.osmani@sheffield.ac.uk
Personal Webpage

Information School
The Medical School
Department of Computer Science

Research Interests

My research interests are in developing machine learning methods, to address some of the fundamental questions in medicine. These include:

- predictive modelling

- explainable AI

- generative adversarial approaches (GAN)

- causal inference

- health inequality and bias

My work focuses on analysis of large-scale, longitudinal health records, including:

- biomarkers

- imaging

- multi-omics

- routine care data 

The aim is to optimise treatment strategies, improve patient care, and provide novel insights to health institutions.

Apart from clinical data, I also work on incorporating human behaviour data, such as those generated from wearable devices, with a particular focus on mental health.

The overarching objective of my research is to integrate predictive modelling in the bedside and bring the acquired evidence back, in a continuously improving feedback loop, consequently establishing a learning health system.

 

PhD Supervision

I will consider project proposals that relate to the aspects mentioned above.

Dr Andrei Popescu
a.popescu@sheffield.ac.uk

Department of Computer Science
Professor Tony Prescott
t.j.prescott@sheffield.ac.uk
Personal Webpage

Department of Computer Science

My research is within the areas of cognitive neuroscience, artificial intelligence and bio-inspired robotics. A particular focus is on the investigation of biomimetic and biohybrid systems: an example of the former would be an animal-like or humanoid robot, of the latter a human-machine interface. Current research is directed towards (i) social cognition for humanoid robots including the possibility synthetic autobiographical memory and of a robot "sense of self”; (ii) active touch sensing for attention, orienting, and spatial memory; (iii) assistive and companion robotic technologies; (iv) haptic interfaces for sensory augmentation; (v) telepresence robotics, and (vi) societal and ethical issues in technology.

Dr Anton Ragni
a.ragni@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Current state-of-the-art technology in speech and language processing is limited and fragile. Though appealing, the hope that generic machine learning would learn the necessary skills from large quantities of data is not well founded. The general purpose machinery cannot accurately describe complex processes of a natural language and the supply of data can not cope quickly enough with the demand for new applications. Such reliance on generic machine learning and highly-specific data sets does not scale - it is infeasible to collect large volumes of data for solving every single task. How is that we, humans, do not need to know, say, every single type of abuse to recognise hate speech but a machine does? Thus, there is a clear need in custom, smart, machine learning solutions that integrate language specific peculiarities at their core and can truly generalise from vast amounts of data already available.

Research Topics:

Core automatic speech recognition
Efficient and expressive speech synthesis
Spoken language translation
Information retrieval
Conversation modelling

Professor Paul Richmond
p.richmond@sheffield.ac.uk
Personal Webpage

Department of Computer Science

My current research relates to the acceleration of complex systems simulations using accelerator architectures such as GPUs. More generally my research interests relate to the software engineering challenges of how complex systems can be described using high level or domain specific tools and how automated mapping to parallel and distributed hardware architectures can be achieved. I am particularly interesting in applying agent based techniques to cellular biology, computational neuroscience, pedestrian and transport system as well as working with industry.

Within previous research positions I have worked on developing novel parallel languages and techniques which will allow neuroscientists to run and analyse simulations of up to a billion spiking neurons. In addition to computational neuroscience, I am particularly interested in the use of the Graphics Processing Unit (GPU) to accelerate computational simulations. I have previously created the FLAME GPU software framework which allows non GPU specialists to harness the GPUs performance for real time simulation and visualisation. Whilst my background is in high performance parallel computation and computer graphics, I have a general interest in GPU algorithms and in computer graphics techniques for simulation, animation, rendering, serious games, automatic building generation (including aspects of GIS) and a general interest in aerial robotics.

Dr José Miguel Rojas
j.rojas@sheffield.ac.uk

Department of Computer Science

Dr José Miguel Rojas Siles is a Lecturer in Software Testing at the Department of Computer Science. He received a PhD in Software and Systems from the Technical University of Madrid (Spain, 2013) and was a Research Associate at the Department of Computer Science at Sheffield (2014-2017) before joining the University of Leicester as a Lecturer in Software Engineering.

His research work focuses on search-based automated test generation and its application in real-world software development scenarios. His interests include empirical software engineering, automated software testing, and software engineering education.

His work has been published in the top venues of logic programming (ICLP), software engineering (ICSE and ASE), software testing (ISSTA and ICST) and search-based software engineering (SSBSE and GECCO).

He has co-chaired multiple workshops and tracks: MUTATION 2017, MUTATION 2018, SSBSE 2018 Challenge Track, SBST 2019 (co-located
with ICSE 2019).

Research Interests:

  • Automated Software Testing
  • Search-based Software Engineering
  • Software Engineering Education
  • Empirical Software Engineering
Dr Carolina Scarton
c.scarton@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Dr Scarton's research area is Natural Language Processing (NLP). She is particularly interested in text adaptation, machine translation, online misinformation detection and verification, evaluation of NLP task outputs, NLP applied to healthcare and robotics, and dialog systems.

Dr Donghwan Shin
D.Shin@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Dr Shin is interested in software testing, mutation testing, and testing for ML-enabled autonomous systems (e.g., automated driving systems). Ensuring the reliability and safety of such software systems is the ultimate goal. To achieve this for complex, real-world systems, he has successfully leveraged search-based software testing (SBST), surrogate-assisted optimisation (SAO), and reinforcement learning (RL). He has published many research papers at top-tier venues such as ICSE, ICST, ISSTA, and MODELS and prestigious journals such as TSE, EMSE, and STVR. See https://dshin.info for more details.

PhD Supervision
Dr Shin is particularly looking forward to hearing from research students interested in testing and debugging autonomous systems using SBST, SAO, and RL.

Dr Anthony Simons
a.j.simons@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Dr Simons’ research focuses on turning formal results from verification and testing into practical benefits for software engineering. His current research areas include model-based testing and model-driven engineering, with applications to Cloud computing. He has also published widely in object-oriented software engineering, including type theory and software development methods. He is inventor of the JWalk automatic software testing tool for Java; and the JAST library for processing XML in Java. He is co-author of the OPEN Toolbox of Techniques.

Dr Michael Smith

Personal Webpage

Department of Computer Science

Machine Learning

Dr Michael Smith's work is mainly focused in the field of uncertainty quantification and Gaussian processes, in the field of Machine learning. Previously he has worked on applying Differential privacy to Gaussian process regression and classification & bounding the capacity of adversarial attack to GP classification. Currently Dr Smith's main focus is on the problem of calibration over a network of air pollution sensors, working with colleagues in Kampala to develop a robust low-cost air pollution monitoring system and developing a method for tracking bumblebees during flight and other bumblebee related projects (classification from video, analysis of beewalk data, etc).

 

PhD Supervision

Dr Smith is particularly interested in hearing from research students interested in the following areas:

  • Ecological modelling using probabilistic models (in particular Gaussian processes)
  • Air pollution spatiotemporal modelling
  • Other applications of the variational network calibration approach devised for the air pollution project
Dr Xingyi Song
x.song@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Dr Xingyi Song, an Academic Fellow at the Department of Computer Science, University of Sheffield, UK. He is a member of the Natural Language Processing group and GATE team (https://gate.ac.uk/)

Previously he worked as a machine translation specialist at Iconic Translation Machine (2015-2016) and Research Associate for several EU funded projects such as Kconnect, Knowmak and Risis2 (from 2016-2021)) at the University of Sheffield. 

He completed his MSc and PhD in Natural Language Processing group at the University of Sheffield.

His research interests are in:

  • Natural Language Processing
  • Computational Social Science
  • Sentiment analysis
  • Bio-medical text processing
Dr Mike Stannett
m.stannett@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Dr Stannett is interested in many areas of research, including Heterotic Computing, Unconventional Computing, Physics and Computation, Hypercomputation Theory, and First-Order Relativity Theories. He also has strong research links with members of the Algebraic Logic group at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences, Budapest).

Dr Mark Stevenson
mark.stevenson@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Natural Language Processing 

Dr Mark Stevenson’s research focuses on Natural Language Processing and Information Retrieval. He has worked on a range of topics in these areas including word sense disambiguation, Information Extraction, plagiarism/reuse detection, author identification, cross-lingual information retrieval and exploratory search. His research includes applications of these technologies to a range of areas including analysis of medical documents (study identification and evidence synthesis for systematic reviews; data mining information from corpora) and exploratory search (automatic organisation of large collections of documents, interpretability of topic models).

 

PhD Supervision

Dr Stevenson is particularly interested in hearing from research students interested in the following areas:

  • Interpretation of scientific literature, particularly in the health domain
  • Development of tools and techniques to support evidence synthesis (e.g. identification and analysis of research evidence)
  • Supporting access to large collections of information
Professor Georg Struth
g.struth@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Georg works mainly on logical and algebraic methods in computer science, formalised mathematics with interactive theorem provers and program verification and correctness. His interests range from foundational work on the axiomatisation and semantics of sequential and concurrent computing systems to applications in the design and implementation of program verification software.

Dr Daniele Tartarini
d.tartarini@sheffield.ac.uk

Department of Computer Science
Department of Mechanical Engineering
Department of Civil and Structural Engineering
Professor Eleni Vasilaki
e.vasilaki@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Machine Learning

As a Computational Scientist and Engineer with extensive cross disciplinary experience, Professor Eleni Vasilaki contributes to understanding brain learning principles. Together with her team she takes inspiration from these principles to design novel, machine learning techniques, and in particular reinforcement learning methods.  They develop data analytics frameworks for neuroscientists, and also work closely with engineers from other disciplines to design hardware that computes in a brain-like manner. 


PhD Supervision

Professor Vasilaki is particularly interested in hearing from research students interested in the following areas:

  • Neural Networks (and Spirking Neural Networks in particular)
  • Reservior Computing
  • Reinforcement Learning
  • Clustering
  • Computational Neuroscience
Dr Maria-Cruz Villa-Uriol
m.villa-uriol@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Dr Maria-Cruz Villa-Uriol's main research interests are:

  • the personalisation of models using computational imaging and modelling techniques, 
  • the composition of scientific workflows, 
  • and the use and development of data-driven decision-making strategies to support clinical decisions using heterogeneous data sources.

The data sources typically used in ther research are:

  • personalised VPH (Virtual Physiological Human) models, 
  • clinical databases, 
  • mobile sensors capturing a wide variety of variables describing an individual and his/her environment and mobile healthcare. 

Her primary area of interest is in the cardiovascular domain with an emphasis in clinical translation.

She is member of the Organisations, Information and Knowledge Group (Oak), INSIGNEO institute for in silico Modelling and Center for Assistive Technology and Connected Healthcare (CATCH).

Professor Aline Villavicencio
a.villavivencio@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Aline Villavicencio received her PhD and MPhil degrees from the University of Cambridge (UK) and MSc in Computer Science from the Federal University of Rio Grande do Sul (Brazil). She was a Visiting Scholar at the Massachusetts Institute of Technology (USA) (in the Department of Linguistics and Philosophy in 2014/2015 and in the Laboratory of Information and Decision Systems in 2011/2012) at the Labo­ra­toire LaTTiCe at the École Normale Supé­rieure (France) in 2014, an Erasmus-Mundus Visiting Scholar at Saarland University (Germany) in 2012/2013, and at the University of Bath in 2006-2009. From 2007-2017 she held a Research Fellowship from the Brazilian Scientific Research Council (CNPq). She is also affiliated to the Federal University of Rio Grande do Sul (Brazil)

Some of her recent activities include being the PC co-chair of the Conference on Computational Natural Language Learning (CoNLL-2019), Area Chair for events like ACL-2019, NAACL-2018COLING 2018, and General co-chair for the 13th International Conference on Computational Processing of Portuguese (PROPOR 2018). She is a member of the advisory board of WiNLP, of the editorial board of TACL, JNLE, Journal of Language Modelling and Linguamatica, and a reviewer for various conferences, in addition to having co-chaired numerous *ACL workshops on Cognitive Aspects of Computational Language Acquisition and on Multiword Expressions. She has also co-edited special issues and books dedicated to these topics.

She is a member of the Natural Language Processing group at the University of Sheffield and of the Neurocomputational and Language Processing Laboratory of the Federal University of Rio Grande do Sul (Brazil).

Her research interests are in lexical semantics, multilinguality, and cognitively motivated NLP. This work includes techniques for Multiword Expression treatment using statistical methods and distributional semantic models, and applications like Text Simplification and Question Answering, for languages like English and Portuguese.

Dr Jonni Virtema
J.T.Virtema@sheffield.ac.uk
Personal Webpage

Department of Computer Science
Dr. Jonni Virtema is keen to supervise students in any area of his current research, which relate to the interplay of logic and complexity theory. Current topics include logics and complexity theory related to numerical data, and temporal logics designed to express so-called hyperproperties, which are important in information flow and security. A further emerging topic is to study foundations of neural networks using the machinery of logics and complexity theory related to numerical data.
 
See http://www.virtema.fi/ for further details.
Dr Dawn Walker
d.c.walker@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

  • Agent (individual) based Modelling in predicting emergent properties of biological tissues
  • Multiscale Modelling in biological and biomedical applications
  • Electrical Impedance Spectroscopy (EIS) for the diagnosis of early cancerous changes in epithelial tissues


Dr Neil Walkinshaw
n.walkinshaw@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Testing

Dr Neil Walkinshaw is interested in three areas of research.

  • Software testing - particularly in the testing of systems with complex inputs and output structures. I am currently leading the CITCOM project, a three year project on testing computational models, which can have particularly complex input configurations and output formats.
  • Techniques to manage uncertainty in software engineering. How can we, for example, reason about the (un)certainty involved in the safety assessments of safety-critical systems, or in the conclusions arising from an empirical study?
  • Reverse engineering - given a complex system that may be decades old with millions of lines of code, how can we derive a useful description or model of its behaviour and structure?

PhD Supervision

Dr Walkinshaw is particularly interested in hearing from research students interested in the following areas:

  • Software testing
  • Techniques to manage uncertainty in software engineering
  • Reverse engineering
Professor Paul Watton
p.watton@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Mathematical and computational biomechanics and mechanobiology; constitutive modelling of soft biological tissues; theoretical and computational analyses of growth and remodeling; cardiovascular mechanics; arterial mechanics, biofluid mechanics, continuum mechanics, vascular mechanobiology, aneurysms.

Dr Joab Winkler
j.r.winkler@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Research interests

Joab Winkler’s main research interests are image processing, and  algebraic and numerical properties of curves and surfaces in computer-aided design systems.

  • IMAGE PROCESSING: The removal of blur and other degradations from an image arises in many applications and it may be considered a preprocessing operation before the image is interrogated for, for example, medical diagnosis. The most challenging problem arises when prior information on the source of the degradations and the exact image is not known, in which case the problem is called blind image deconvolution. My research is concerned with the application of polynomial computations, implemented using structure-preserving matrix methods, for the solution of this problem. The next stage of this work on image improvement is its extension from static images to video images for the observation of dynamic events, for example, the flow of blood.
 
  • GEOMETRIC MODELLING: Curves and surfaces in computer aided design systems are represented by polynomials. Computational problems arise because the coefficients of these polynomials are corrupted by noise due to manufacturing tolerances and numerical approximations, and robust computations on polynomials are therefore required. Recent work on these robust computations includes the computation of a structured low rank approximation of the Sylvester resultant matrix, and the devlopment of a polynomial root solver for the determination of multiple roots of the theoretically exact form of a polynomial, when the coefficients of the given polynomial are corrupted by added noise.
 
  • FEATURE SELECTION: Many problems in science require the identification of the most important features that characterise a system, such that the expected response of the system to new data can be accurately predicted. Problems arise because the given data that is available to identify these important features is usually insufficient to define the system uniquely, which implies that the equation to be solved has an infinite number of solutions, This raises the question as to the solution that is selected from this infinite set of solutions, and the criterion used for this selection. My research is concerned with the development of mathematical theory and methods for the selection of the best solution, defined using a specified criterion. The features that characterise a system may be a combination of numerical data, binary data and categorical data, and a mathematical model that describes a system must include these three classes of data. This problem has many applications, including bioinformatics, signal analysis, atmospheric physics, and in general, problems in which the response (output) is a function of many variables (inputs), only some of which are important and must therefore be identified.
Dr Xu Xu


Department of Computer Science

Dr Xu Xu is a Senior Lecturer in Complex Systems Modelling in the Department of Computer Science and the INSIGNEO Institute for in silico Medicine, at the University of Sheffield, UK. Her current research focuses on haemodynamics and multi-scale modelling for personalised cardiovascular healthcare.

Xu obtained a BEng degree in Automation from Xidian University, China, and then an MSc in Control Systems Engineering (with Distinction) and a PhD in Nonlinear Systems and Cellular Maps, both in the University of Sheffield (UoS). She worked as a Postdoctoral Researcher at UoS and the University of Southampton, on mathematical and computational modelling of complex systems and processes, followed by the positions of Lecturer, Senior Lecturer, Reader and Interim Deputy Head of Department for the Department of Engineering and Maths at Sheffield Hallam University (SHU), before returning to UoS as a Senior Lecturer in Oct 2023.

 She has extensive academic leadership experience and served as the Interim Deputy Head of a large department, a SHU Early Career Researcher Representative, an MSc Course Leader and a Postgraduate Research Tutor for engineering MPhil/PhD programs, achieving outstanding PRES overall student satisfactions which were ranked 1st in the engineering sector, in both 2020-2021 and 2021-2022.

She has supervised 6 PhD students to completion and has won 8 Sheffield Hallam University or College awards for inspirational teaching, inspirational research supervising and outstanding academic advising.

 

Research Interests:

• Multi-scale and multi-component lattice Boltzmann simulations of blood flow
• Compartmental cardiovascular model for personalised healthcare
• Uncertainty quantification and parameter identification
• Nonlinear dynamics, control and state estimation
• Cellular automata and swarm robotics

Dr Po Yang
po.yang@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Dr Po Yang is a Senior Lecturer in Large Scale Data Fusion in the Department of Computer Science at the University of Sheffield. He graduated with a BSc (Hons) in Computer Science from Wuhan University in China in 2004, before being awarded his MSc in Computer Science from the University of Bristol in 2006. In 2010 he graduated with a PhD in Electronic Engineering from the University of Staffordshire. From February 2015 to July 2019, he was a Senior Lecturer in Computer Science at Liverpool John Moores University. He worked as a Post-doc Research Fellow in Computer Science at the University of Bedfordshire from January 2012 to January 2015. Previously, he has also held the positions of Research Associate in Computer Science at the University of Teeside from September 2008 to February 2010, a Research Assistant in image processing at the University of Salford from March 2010 to December 2011. Since 2006 he has generated over 90 international journal and conference papers in the fields of Pervasive Healthcare, Image Processing, Parallel Computing and RFID related internet of things (IoT) applications.

He serves as an Associate Editor in IEEE Journal of Translational Engineering in Health and Medicine and IEEE Access.

He has over 12 years full time research experience in computing areas (recent three years working on Pervasive Healthcare), which includes the key participation and local leadership of 6 EU funded projects CALLAS (RA in Affective Computing at Teeside University), IMPACT (RA in Image Processing at Salford University), GPSME, DRINVENTOR, MHA and CHIC (RF in Computer Science at Bedfordshire University) and 3 EPSRC/TSB funded projects.

Dr Po Yang's research interests include: Pervasive Computing, Healthcare Informatics, Data Analytics and Internet of Things (IoT)

Dr Cass Zhao
zhixue.zhao@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Natural Language Processing

Dr Cass Zhixue Zhao is a Lecturer in Computer Science at the University of Sheffield. Her research is broadly on trustworthy and interpretable machine learning, such as interpretability and analysis of NLP models. Her research seeks answers to questions such as: What motivates a model to predict or behave a certain way? How do we ensure a black-box model predicts correctly and also for the right reason? How do we obtain a faithful understanding of both the intended and, more importantly, unintended model behaviours? How to maintain a healthy relationship between human users and AI algorithms? Related research further includes fairness and robustness in models. Her research interests also cover model compression, which contributes to accessible NLP models and inclusive AI. 

 

PhD Supervision

Dr Cass Zhixue Zhao is particularly interested in hearing from research students interested in the following areas:

  • Model explanation and model interpretability
  • Model compression, e.g., pruning, in the era of large language models (LLMs)
  • Model hallucination
  • Reasoning capability of LLMs
  • Responsible AI and Trustworthy AI
Dr Maksim Zhukovskii
M.Zhukovskii@sheffield.ac.uk
Personal Webpage

Department of Computer Science

Probabilistic combinatorics and model theory

Maksim Zhukovskii is a Senior Lecturer in the department of Computer Science at the University of Sheffield. His research interests are in combinatorics, probability, model theory, algorithms, and related areas.

PhD Supervison

Maksim Zhukovskii is particularly interested in hearing from research students interested in the following areas:

  • Distribution of subgraphs in random graphs
  • Turan-type problems in random graphs
  • Logical limit laws
  • Isomorphism and reconstruction of random graphs
  • Saturation in random graphs
  • Random regular graphs and graphs with given degree sequences