I'm a PhD candidate in Computer Science at the National University of Water and Environmental Engineering. My research is about making generative AI usable in decisions that matter — where a confident wrong answer is worse than no answer.
In practice that means multi-stage LLM pipelines and how to evaluate them, multi-criteria decision analysis, and using the labour market as a live data source for questions about skills, hiring and curriculum design.
Academic profiles are being set up. Until then, every paper below links to its DOI.
2025
A semantic module extracts and clusters the skills employers actually ask for in job ads; a generative model turns those clusters into adaptive course structures. The point is curricula that track real market demand instead of lagging years behind it.
2024
A career test scoring 40 factors across four groups — career growth, work environment, benefits, and personal values — matched against employer profiles built from employee feedback, so students and juniors get recommendations grounded in something other than employer branding.
2023
Automatic categorisation of job vacancies built on managed AWS services — ECS, DynamoDB, Glue, SageMaker and Lambda — and what that architecture actually buys you in scalability and running cost.