PhD Projects for GTA Applicants - September 2020

For information about the GTA posts and how to apply, click here.

If you apply for one of our Graduate Teaching Assistantships for September 2020 start, you need to name potential PhD supervisors and submit a research proposal as part of your application:

  • Any member of academic staff (Lecturer, Senior Lecturer, Reader, Associate Professor, Professor) in the Department can be your supervisor.
  • Any research topic related to the departmental research themes can be your PhD topic.

Some examples of exciting PhD projects for which supervisors are currently looking for students are:

Information about these PhD projects is provided below. Further projects may still be added. If you apply for a GTA post, you can apply for one of these projects, but you can also propose any other member of academic staff as supervisor and propose any research topic related to the departmental research themes as your PhD topic.

Adaptive Learning through Cloud Computing: A Decision-making Framework for Higher Education

Supervisor: Dr Karim Mualla

It has been predicted that in the near future students will design their own educational degrees based on their interests, learning approaches, and career objectives. Adaptive learning is defined as an educational method in which students’ bespoke interests are identified, measured, and continuously monitored through information and communication technologies. As a result, this process will enable students to create a customised learning pathway for their undergraduate and postgraduate studies. Cloud computing utilises the internet’s capabilities to facilitate scalable processing, storage, retrieval of information, and the development of applications. These services are delivered and measured based on accurate user-demand, and through a real-time, location-independence, and self-service model. While cloud computing is accessed through a broad network, this creates an ideal environment for designing an interdisciplinary adaptive learning platform. This research will examine the applicability of utilising new generation cloud solutions to develop a scalable methodology for adaptive learning. The objective is to design and implement both a speculative, and a directional decision-making system for managers in higher education. This framework is aimed to reduce ICT management effort and cost, raise students’ interest to learn, and compel educators to perform better teaching by utilising cloud-based adaptive learning processes.

Automated Accessibility Testing of Mobile Apps

Supervisors: Dr José Miguel Rojas and Prof. Mohammad Mousavi

An estimated 15% of the world population live with some form of disability and must face multiple barriers in their day-to-day life. In the UK, two million people suffer from sight problems (e.g., blindness, low vision, or colour blindness) and the number is predicted to rise to over 2,250,000 by 2020, according to the Royal National Institute of Blind People. In a society increasingly populated by computer systems, enabling these users to access computer technology effectively is a major concern. According to the Ofcom’s 2015 Communications Market Report, smartphones are the most popular device to access the Internet in the UK, with a third (33%) of British internet users preferring their smartphone over their laptops for going online. This scenario sets an important challenge to the software industry: We must produce mobile apps that not only satisfy basic functional requirements, but also support the population of users with accessibility requirements.

Even when software vendors and developers are aware of accessibility needs, there is an evident lack of tool support to develop accessible apps or assess existing apps’ accessibility. While some accessibility properties can be checked statically, modern development practice indicates that mobile user interfaces are often created dynamically and therefore are not amenable to static checking. And while basic accessibility checking frameworks exist to analyse accessibility properties or mitigate accessibility limitations at runtime, they either require substantial additional effort from developers or are simply not suitable for current mobile development processes. The main aim of this project is to overcome these issues through the use of automated test generation to analyse, assess, and improve the accessibility of mobile apps.

The research objectives for this project are:

  • Develop a fully automated test generation approach to check the accessibility of mobile Android apps.
  • Evaluate the usefulness of the approach by assessing the accessibility of existing apps and interacting with their developers.
  • Evaluate the severity and relevance of the accessibility issues identified with the automated approach, as well as issues missed, using observational studies and interviews with real users with accessibility needs.

Additional reading:

  • Marcelo Medeiros Eler, José Miguel Rojas, Yan Ge, Gordon Fraser. “Automated Accessibility Testing of Mobile Apps,” IEEE Intl. Conference on Software Testing, Verification and Validation (ICST), IEEE, 2018.
  • Alberto Oliveira, Marcelo Eler. “Strategies and Challenges on the Accessibility and Interoperability of e-Government Web Portals: A Case Study on Brazilian Federal Universities,” IEEE Computer Software and Applications Conference (COMPSAC), IEEE, 2017.
  • Camila Silva, Marcelo Medeiros Eler, Gordon Fraser. “A survey on the tool support for the automatic evaluation of mobile accessibility”, Intl. Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI), ACM, 2018.
  • Ermira Daka, José Miguel Rojas, and Gordon Fraser. “Generating Unit Tests with Descriptive Names Or: Would You Name Your Children Thing1 and Thing2?” In ACM Int. Symposium on Software Testing and Analysis (ISSTA), ACM, 2017.
  • José Miguel Rojas, Mattia Vivanti, Andrea Arcuri, and Gordon Fraser. “A detailed investigation of the effectiveness of whole test suite generation.” Empirical Software Engineering (EMSE), Springer, 2016.
  • H. Beohar and M.R. Mousavi. “Input-Output Conformance Testing for Software Product Lines.” Journal of Logic and Algebraic Methods in Programming, 85(6). Elsevier, 2016.

Automated Management of Domain-Driven Design in Microservice Architectures

Supervisor: Dr Artur Boronat

Project informationClick here

Conformance Testing for Quantum Programs

SupervisorProf. Mohammad Mousavi

Quantum computation and communication has gained enormous importance in the last few years and major investments have been made to commercialise this technology. As a results, quantum computation systems are becoming more complex and their quality assurance is a major concern.

The aim of this project is to develop the theory and tools for a conformance testing for quantum programs.To this end, we start with extending property-based testing, a la QuickCheck, to a quantum programming framework such as PytKet ( Subsequently, we develop efficient test-data generation and shrinking mechanism for testing and diagnosing quantum programs.

More reading resources for this project:

Deep neural network for computer-aided cancer detection

Main supervisor: Prof Yu-Dong Zhang
Co-supervisor: Prof Thomas Erlebach

Project information: Click here (PDF)

Differences in Specifications

Supervisor: Dr Jan Oliver Ringert

Project informationClick here

Explainable AI and Synthesis

Supervisor: Dr Jan Oliver Ringert

Project informationClick here

Helping Patients with Chronic Conditions using AI

Supervisor: Dr Artur Boronat

Project informationClick here

High-level search methodologies for problem solving

Supervisors: Dr John Drake, Prof Lu Liu, and Prof Edmund K. Burke

Optimisation problems often explore a search space which is too large to enumerate and exhaustively search for an optimal solution. Various heuristics and metaheuristics have been applied successfully to problems of this nature. One drawback of such approaches is the necessity to manually adapt the method used to solve different problem domains or classes of problem. Hyper-heuristics are a class of high-level search techniques which aim to raise the level of generality at which search methods operate. Unlike traditional techniques, a hyper-heuristic operates on a search space of heuristics rather than directly on the search space of solutions. The last decade or so has seen sustained research effort directed at hyper-heuristic methods, much of which is a result of pioneering work done by researchers at Leicester. This project will apply hyper-heuristic methods to real-world combinatorial optimisation problems, seeking to automate the heuristic design process, reducing the time required to develop such methods and the burden on the human involved in the development cycle.

Please e-mail for further information about this project.

Improved Breast Cancer Diagnosis System based on Deep Neural Network

Main supervisor: Prof Yu-Dong Zhang (School of Informatics)
Co-supervisor: Prof Rajeev Raman (School of Informatics)
Co-supervisor: Dr David Guttery (Leicester Cancer Research Centre)

Project informationClick here (PDF)

Incremental Syncing Policies in Data-driven Cloud Software Applications

Supervisor: Dr Artur Boronat

Project informationClick here

Intelligent Automated Interpretation and Reporting of Medical Images

Main supervisor: Dr Reza Zare
Co-supervisor: Prof Yu-Dong Zhang (School of Informatics)
Co-supervisor: Dr Emma Chung (Department of Cardiovascular Sciences)

Project informationClick here (PDF)

Nominal Gluing

Supervisor: Dr Roy Crole

Project informationClick here

Reversing algorithms

SupervisorDr Irek Ulidowski

Reversible computation is a new computation paradigm that extends the standard forwards-only mode of computation with the ability to execute in reverse. In this paradigm one can execute in reverse as naturally as can go forwards. There are prototype reversible programming languages such as Janus, and a reversible version of a concurrent C-like programming language has been proposed. Moreover, we know how to model undoing of computation in Turing machines, process calculi and Petri nets. These recent advances in reversing different formal and practical models of computation have been applied in developing better approaches to, for example, reversible debugging of concurrent programs and in parallel discrete event simulation.

Not much has been done however developing reversible versions of basic algorithms for standard computation tasks, such as sorting or searching. Also, there has been very little research on providing reversible versions of the basic data structures, such as arrays, trees and graphs, and reversible operations on them. More abstractly, it would be interesting and useful to develop in the proposed PhD programme of research a formal notation for writing reversible algorithms. We can take a lead from the programming language Janus, where all the fundamental language constructs, such as assignments, conditional and loop statements, are given in the reversible form. Moreover, we may also use other irreversible constructs in our notation but then we would need to work out the minimal amount memory needed to make such irreversible constructs reversible. We will then define, given an algorithm written in our notation, the inverse algorithm that un-computes the result of the original algorithm.

Once we have a notation for reversible algorithms and a method for generating inverse algorithms, we shall explore their usefulness. We shall find an application area where there exist already algorithms to do a task and algorithms to un-do the task. Consider, for example, lossless compression and decompression of data, and the algorithms for them. Given a compression algorithm, we shall aim to compare its traditional decompression version with a newly developed decompression algorithm using our algorithm notation and algorithm inversion. It would be interesting then to explore space and time complexity of such pairs of algorithms.

Overall, we propose to develop a new notation for reversible algorithms and a method for producing their inverses. We shall then compare some traditional algorithms and their versions written in our notation in terms of space and time complexity.

Please e-mail for further information about this project.

Safety Analysis for Autnomous Vehicles

Supervisors: Prof. Mohammad Mousavi and Dr. José Miguel Rojas

Establishing trust in autonomous vehicles is a major component of their widespread public adoption. Rigorous and explainable safety analysis of autonomous vehicle functions play a major role in establishing trust. There are existing standards for establishing safety in automotive systems, of which the ISO 26262 standard is the most prominent one. Safety case analysis in these standards involves defining a safety item and analysing and providing a safety case for the item by analysing the hazards in typical scenarios of use and foreseeable misuse. To analyse the hazards rigorously, different safety integrity levels (ASIL) are attached to them, and different analysis techniques are prescribed for different ASIL. At high ASIL, formal verification and model-based testing are recommended as appropriate techniques for the analysis.

Hitherto, much of the safety case analysis process has been manual, involving tedious scrutiny of possible scenarios and turning them into appropriate models for further analysis. For autonomous vehicles, however, such a manual process becomes extremely laborious and error-prone and mechanisation support is inevitable. Also, in the presence of adaptive and AI-enabled systems, adapting safety cases and their analysis should inevitably be mechanised or otherwise will be infeasible. This project aims at providing mechanised support for safety-case analysis of automated and autonomous functions.

To this end, we will build upon our past experience with automated test-case and scenario generation to turn structured English safety case and item descriptions into rigorous models from which use and misuse scenarios are generated automatically.

Please do not hesitate to contact Mohammad Mousavi ( for further information.

Additional information: Click here (PDF)

Vocal Emotions Analysis of Conversations for Supporting Emotional Wellbeing

Supervisors: Prof. Effie Law and Dr Huiyu Zhou

Project information: Click here (PDF)

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