PhD Researcher | Agentic AI Engineer | Multilingual NLP
I build machine learning, multilingual NLP, retrieval-augmented generation, and multi-agent AI systems for underrepresented languages, cultural heritage, music emotion analysis, and responsible decision-making.
Thesis research explores cross-cultural musical elements, emotional expression, and genre characteristics in contemporary global music lyrics using NLP and deep learning. Awarded a fully funded Faculty of Science and Engineering PhD Scholarship in Data Science.
Developing multilingual emotion recognition pipelines for African languages, with attention to class imbalance, code-mixed text, tokenisation, cross-lingual embedding alignment, and responsible model evaluation.
Contributing to British Academy ODA and UNESCO-linked work on AI literacy, intangible cultural heritage, data governance, digital visibility, and community-led frameworks for cultural data use.
Building LangGraph-based multi-agent systems for systematic literature review tasks, including protocol generation, screening, evidence synthesis, structured JSON/CSV outputs, and human review checkpoints.
Co-author and presenter on locally responsible AI frameworks for cultural heritage governance, with additional work in persuasion detection, multilingual NLP, invited talks, and public engagement.
Develop and evaluate machine learning, NLP, and deep learning pipelines for multilingual emotion modelling. Teach machine learning, NLP, and deep learning laboratory sessions to 150+ MSc students, supporting Python, model development, and applied AI workflows.
Designed components of a LangGraph-based multi-agent AI system for systematic literature review workflows, including protocol generation, screening, evidence synthesis, auditability, and reproducible structured outputs.
Developed and refined responsible AI, digital literacy, and cultural data governance materials for intangible cultural heritage practitioners. Built Python and Hugging Face data processing workflows and contributed to facilitator-ready training materials for international partners.
Analysed programme and operational datasets with Python to evaluate food redistribution impact, improve reporting quality, and support evidence-based service decisions. Awarded Outstanding Achievement Intern Award.
A public Python package for African language pre-processing, emotion-label mapping, evaluation, and dialect/language routing.
View repositoryEmotion analysis for English poetry with NRC lexicons, deterministic train/validation/test splits, supervised baselines, and reproducible metrics.
View repositoryMultilingual NLP research for predicting how people feel about specific aspects in text, using valence-arousal scores for finer-grained sentiment beyond positive or negative labels.
View repositoryVolunteer academic support tutor with IntoUniversity Hull East, research mentor through University of Hull and Nuffield Research Placements, founder of Tech Help Group, and community contributor with Hull Afro-Caribbean Association.
Multilingual NLP, low-resource language processing, emotion classification, LLM fine-tuning, retrieval-augmented generation, agentic AI, bias and fairness metrics, and model evaluation.
Data pipelines with pandas and NumPy, scikit-learn, Bash, structured outputs, reproducible documentation, and experiment-ready Python tooling.
English and Yoruba fluency, intermediate Spanish, and basic French, with research interests in multilingual and cross-cultural language technologies.
University of Hull: PhD in Data Science and AI, 2023 - present; MSc Artificial Intelligence and Data Science, Distinction, 2022 - 2023.
University of Lagos: BSc Geophysics, Second Class Upper, 2015 - 2020.