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Supervision and Mentoring

I supervise and mentor postgraduate researchers working on trustworthy AI, responsible LLMs, safety-critical ML, fairness, explainability, digital twins, EdgeAI, generative AI, and dependable autonomous systems.

Completed or Near-Completion Students

Student Award / route Topic
Victoria Bessonova AURA CDT Scholarship Climate change impacts on the accessibility of offshore structures
Zeinab Dehghani MSc by Research, self-funded Addressing explainability of generative AI
Mostafa Anoosha MSc by Research, self-funded Vertical Federated Learning and EdgeAI
Kuniko Paxton DAIM Scholarship Redefining fairness in ML through a pigment-based approach to skin colour analysis

Current First Supervision

Student Role Topic
Mohadeseh Mollapour MSc by Research, self-funded Self-explainable generative AI solutions using SMILE
Shadie Mohammadi MSc by Research, self-funded Geo-SMILE for explainability in geospatial and temporal models
Louis Donaldson AURA Scholarship OPtimization EXplainability for maintenance scheduling of offshore wind farms

Current Co-Supervision

Student Partner / route Topic
Connor Walker with Yiannis Papadopoulos SafeLLM: safety monitoring for large language models in offshore wind maintenance
Parvin Ghaffarzadeh DAIM Scholarship ML-based remote monitoring to enhance bone health in peri- and post-menopausal women
Hari Neupane Microsoft Responsible large language models
Ankit Nathu Google Agentic AI safety and scheming detection
Sola Adewumi Ireland Weather Office Time-series explainability with SMILE
Razieh Arshadizadeh CS GTA Combining SafeML with Bayesian networks and federated learning

Mentoring

Period Area Focus
2023-present Postgraduate research mentoring Research design, method development, academic writing, and publication planning
2023-present Publication mentoring Journal, conference, and workshop paper development
2024-present Responsible AI research mentoring SafeML, SafeLLM, SMILE, explainability, fairness, and AI safety
2024-present Proposal development mentoring Project ideas, work packages, impact plans, and funding applications

Prospective PhD and MSc by Research Students

I am interested in hearing from students with strong motivation in:

  • AI safety and trustworthy machine learning.
  • Responsible LLMs and multimodal AI alignment.
  • Explainable AI for safety-critical applications.
  • Runtime monitoring and uncertainty quantification.
  • Dependability, digital twins, and autonomous systems.

Please include a short research statement, CV, and links to relevant code, publications, or project work when getting in touch.