AI Research Engineer and Data Scientist
PhD Researcher in Data Science and AI, University of Hull
Multilingual NLP | Low-resource language technologies | Agentic AI | Emotion modelling | Responsible AI

I build machine learning, natural language processing, retrieval-augmented generation, and multi-agent AI systems for multilingual and culturally grounded settings. My research focuses on low-resource African languages, code-mixed text, emotion recognition, music lyrics, responsible AI, and trustworthy workflows for research and decision-making.

I am currently a PhD Researcher in Data Science and AI at the University of Hull, where my thesis explores cross-cultural musical elements, emotional expression, and genre characteristics in contemporary global music lyrics using NLP and deep learning. I was awarded a fully funded Faculty of Science and Engineering PhD Scholarship in Data Science. I also teach machine learning, NLP, and deep learning laboratory sessions to MSc students, supporting practical model development, Python engineering, and applied AI evaluation.

• Low-resource and multilingual NLP: African language processing, code-mixed text, tokenisation, language identification, cross-lingual transfer, and evaluation for underrepresented languages.
• Emotion recognition and music AI: Fine-grained emotion modelling for multilingual lyrics, cultural context, class imbalance, and responsible interpretation of affective labels.
• Agentic AI and research automation: LangGraph-style multi-agent systems for systematic literature reviews, protocol generation, evidence screening, synthesis, auditability, and structured outputs.
• Retrieval-augmented generation: RAG and retrieval-augmented classification for grounding model outputs in examples, cultural knowledge, and traceable evidence.
• Responsible AI and cultural heritage: Community-led data governance, digital/AI literacy, bias-aware NLP, ethical dataset use, and locally responsible AI frameworks.

• Low-Resource NLP Toolkit
  A Python toolkit for African language pre-processing, emotion-label mapping, evaluation, and language/dialect routing. The project is designed as a clean public package for multilingual NLP research and low-resource AI experimentation.

• DimABSA 2026 DAIM Research
  Multilingual 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.

• Portfolio
  My AI research engineering and data science portfolio, with selected projects, research interests, talks, service, and technical skills.

• Co-author and presenter, Digital Humanities 2025, Lisbon: locally responsible artificial intelligence frameworks for community-led digital data governance of cultural heritage in Burkina Faso.
• Co-author, Detection of Persuasion in Memes Across Languages with Ensemble Learning and External Knowledge: multilingual persuasion detection work in the ACL/SemEval shared-task research space.
• Research contributor on British Academy ODA and UNESCO capacity-building work for responsible technology use, AI literacy, intellectual property, digital visibility, and intangible cultural heritage practitioners.
• PhD research on multilingual emotion recognition, retrieval-augmented classification, African language processing, and culturally grounded music emotion modelling.

• UKRI Member, EPSRC and NERC Peer Review Colleges
• Reviewer, International Conference on Learning Representations
• Reviewer, Deep Learning Indaba
• Associate Fellow, Advance HE
• Professional Member, BCS - The Chartered Institute for IT

• Panel Speaker, 6th European Chatbot and Conversational AI Summit, Edinburgh
• Co-author and Presenter, Digital Humanities 2025, Lisbon
• Invited Speaker, PyCon Lithuania 2025
• Lead Organiser and Speaker, Towards Transparent and Responsible AI Conference, University of Hull
• Invited Speaker, Pint of Science UK, Hull
• Featured Contributor, BBC News Interview for National AI Day

Languages and frameworks: Python, PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, LangChain, LangGraph, Bash
AI and NLP: multilingual NLP, low-resource language processing, emotion classification, LLM fine-tuning, RAG, agentic AI, machine learning evaluation, bias and fairness metrics
Data and engineering: pandas, NumPy, reproducible research tooling, structured JSON/CSV outputs, Git, Docker, Azure
Human languages: English, Yoruba, Spanish, French

• Portfolio: oyinkanchekwas.github.io
• LinkedIn: linkedin.com/in/oyinkan-chekwas
• GitHub: github.com/oyinkanchekwas

I use this GitHub profile to make my AI research engineering work easier to discover, reproduce, and build on.