Oluseyi Olukola

Oluseyi Olukola is a PhD candidate in Computer Science at the University of Southern Mississippi's Cyber Innovation Lab, where his doctoral research focuses on Trustworthy AI for High-Stakes Human Applications spanning educational AI safety, adversarial cybersecurity defence, medical diagnostics, and child safety systems. His flagship research contributions include the MC-CPO framework (Mastery-Conditioned Constrained Policy Optimization), a constrained reinforcement learning approach for safe intelligent tutoring systems, and the formalisation of pedagogical safety via the Reward Hacking Safety Index (RHSI) both peer-reviewed and published on arXiv. His cybersecurity AI work, the AMDS system, is currently under review at IEEE Transactions on Information Forensics and Security. He holds an MSc in Applied Data Science with Merit from Teesside University, UK, where he developed the expertise in machine learning, statistical modelling, and data analytics that underpins his doctoral and entrepreneurial work. He also serves as Instructor of Record at the University of Southern Mississippi, teaching Web Application Security and IT Project Management at undergraduate level bringing real-world engineering practice directly into the classroom. Beyond the lab, Oluseyi translates research into working systems. He designs and ships production-grade applications, from AI-powered companion platforms and mobile apps to IoT hardware integrations and client-facing web products operating comfortably across both AI-native architectures and modern full-stack environments. He is Co-Founder of DataIntell (UK), the decentralised community platform where students, researchers, academics, and professionals publish their research and innovations directly. He is also Founder and CEO of Afritech Medalytics Limited (Nigeria), delivering AI-powered solutions across healthcare, education, enterprise, and cybersecurity in Nigeria, the UK, and the US. He co-founded Grain Rain Holdings Ltd (UK), parent company of Harambee a community childcare platform live on iOS and Google Play. His technical fluency spans the full spectrum of modern AI and software engineering from statistical modelling and machine learning to computer vision, NLP, and reinforcement learning, built on a strong foundation in Python, R, and SQL. He architects and ships production applications across both AI-native backends and modern web stacks, bringing research from whiteboard to deployment with the same rigour he applies in the lab.

Data Analyst Approach to Data Investigation

In this practical segment, Oluseyi Olukola demonstrates the foundational work of a Data Analyst within a high-performing data team. He showcases how raw, often messy data is transformed into a clean, reliable asset through systematic investigation. Key highlights include: - The Investigative Approach: Oluseyi shares the specific techniques and frameworks data analysts use to audit and understand a new dataset. - Data Cleaning & Pre-processing: A live walkthrough of the cleaning process, ensuring the data is free from errors and inconsistencies before it reaches the modeling or visualization stages. - EDA Techniques: Demonstrating how to uncover hidden patterns, spot anomalies, and test hypotheses using Exploratory Data Analysis. - The Synergy Handoff: Showing how a clean dataset serves as the vital link that allows the Business Analyst to build dashboards and the Data Scientist to create algorithms.