Get to know me!
I am a Junior Data Scientist with a background in Cognitive Neuroscience and postgraduate training in Big Data Analytics and Artificial Intelligence for Society. I combine experimental research experience with applied machine learning to turn real-world problems into data-driven tools, interpretable models, and AI-powered prototypes.
My work spans Predictive Maintenance and IoT Analytics, Time-Series Modelling, NLP and Graph-RAG, and Machine Learning applied to neural and physiological signals. I have experience developing analytical pipelines, evaluating model performance, and communicating findings through interactive dashboards and data-driven reports.
Across my projects, I take a problem-first approach: understanding the underlying need, assessing whether the available data can support the intended solution, and building systems that can be inspected and tested. Depending on the challenge, this may involve an analytical pipeline, a decision-support dashboard, a document-retrieval system, or an interactive application.
I pay particular attention to data quality, reliability, interpretability, and the gap between a working prototype and a validated solution.
I am particularly interested in Applied AI and Industrial R&D, especially in Digital Health, Physiological Computing, Intelligent Systems, and Behavioural Analytics, where interdisciplinary thinking can help translate research into practical applications.
