Supervisor: Dr Vasileios Klimis
Project Description
This project aims to develop fundamental insights into the integration of fuzz testing and machine learning for validating formal models and constructing data-driven ones. The research will explore how machine learning can inform and enhance learning algorithms, with a focus on applying these techniques to empirically validate theoretical formulations against real-world systems. For a glimpse of some fuzzing applications I have been working on, take a look here.
PhD topics for this position could explore any combination of the following areas:
- Memory Persistency Semantics Validation: investigating how model learning can empirically infer persistency semantics by observing memory traces from systems under diverse crash scenarios. AI techniques such as neural sequence modelling or clustering could be used to identify patterns of behaviour that deviate from expected semantics, enabling a black-box validation approach to detect subtle inconsistencies in persistency guarantees.
- Quantum Simulators Validation: developing approaches for testing quantum computing simulators. This research could leverage randomised or property-based testing to automatically create comprehensive and targeted test suites. These methods would aim to stress simulators to uncover bugs, validate their correctness, and evaluate their behaviour under extreme or edge-case scenarios.
- RDMA Memory Semantics Validation: leveraging AI techniques to explore and validate Remote Direct Memory Access (RDMA) operations. The project could involve building models of RDMA behaviour by systematically testing and learning access patterns, memory consistency properties, and failure modes, enabling automated detection of unexpected violations in high-performance distributed systems.
While I have several ideas related to fuzzing, I am also open to supervising a PhD project that explores alternative applications of randomised testing techniques.
Prerequisites:
- A Master’s degree (Distinction or equivalent) or an expected completion of such qualifications before starting the PhD.
- A keen interest in testing methodologies
- Familiarity with formal reasoning principles.
- Solid programming skills and a drive to create dependable, high-quality software.
The PhD studentship is funded by EPSRC Doctoral Landscape Award open to those with Home and International fee status. However, the number of students with international fee status who can be recruited is capped according to the EPSRC terms and conditions, so competition for international places is particularly strong. The PhD student will receive an annual stipend of £21,237 for the academic year 2024/25, with funding available for a duration of up to 3.5 years.
How to apply
Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas.
Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/.
The application should include the following:
- CV (max 2 pages)
- Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)
- Research proposal (max 500 words)
- 2 References
- Certificate of English Language (for students whose first language is not English)
- Other Certificates
Please note that in order to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)
Application Deadline
The deadline for applications is the 29th January 2025.
For general enquiries contact Mrs. Melissa Yeo (administrative enquiries) or Dr. Arkaitz Zubiaga (academic enquiries) with the subject “EECS 2025 PhD scholarships enquiry”.
For specific enquiries contact Dr. Vasileios Klimis.