Profile: http://staffprofiles.bournemouth.ac.uk/display/dcetinkaya

Email: dcetinkaya@bournemouth.ac.uk

I am interested in supervising projects in quantum computing, artificial intelligence (AI), and machine learning (ML), particularly their applications in healthcare, medicine, finance, and other real-world problems. Potential topics include quantum algorithms, intelligent healthcare systems, predictive modelling, and AI-driven decision support.

I am also open to interdisciplinary projects and new ideas that align with my research interests. Students are encouraged to discuss their own project proposals and explore potential topics with me.

I am particularly interested in supervising projects that result in a high-quality, functional software artefact alongside a sound research contribution. Therefore, excellent programming and software development skills, together with a solid understanding of AI and machine learning models, workflows, and pipelines, are essential. Students should be motivated to develop robust, well-designed, and thoroughly evaluated software solutions, demonstrating both technical proficiency and the ability to apply relevant concepts to practical problems.

1. Maternity-AI: Evaluation and Comparison of AI Models

This project focuses on the further development and evaluation of an existing Maternity-AI prototype. The aim is to extend the prototype's functionality, and improve the overall quality, security, and robustness of the software. This also covers investigating and comparing the performance of different AI models for maternity-related applications. The project requires excellent programming skills and a strong understanding of AI models, particularly model implementation, evaluation, and performance comparison. The student will also evaluate implemented features and explore the use of AI agents for systematic software testing.

2. Agent-Based Process Simulation and AI-Assisted Business Process Improvement

This project explores the application of agent-based modelling and AI to simulate, analyse, and improve business processes within an engineering company. The student will investigate how process simulation and AI-assisted analysis can identify inefficiencies, evaluate alternative scenarios, and support data-driven process improvement. An interest in simulation, AI, process modelling, and optimisation would be beneficial.

3. Evaluating LLMs for Mental Health Applications: Risks, Ethics, and Responsible Use

This project investigates the use of different large language models (LLMs) in the context of mental health, focusing on their capabilities, limitations, and potential risks. The project will involve developing an interactive dashboard to facilitate experimentation with different models, enabling their outputs and performance to be explored and compared. The student will systematically evaluate model responses to mental health-related scenarios, examining issues such as accuracy, reliability, bias, potentially harmful outputs, and ethical concerns.

4. Quantum Computing and Quantum Algorithms

This project area explores quantum computing, quantum algorithms, and their potential applications to computational problems. Students can investigate a topic of interest within this broad area, focusing on algorithm design, implementation, computational complexity analysis, and systematic benchmarking against classical or alternative approaches. Potential topics include quantum machine learning, quantum optimisation, and QUBO formulations for graph problems. Students will use existing frameworks and programming tools, such as Qiskit, Cirq, and Q#, to develop and experiment with algorithms.