• NLP
• Computational Social Science
• Legal NLP
• Data Science
• Machine Learning

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.

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

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

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.

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).

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.

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

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.

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.

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.

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.  

Research interests

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

Research interests

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.

No details available

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.

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.

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 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.

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 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

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

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

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

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 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)

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