Can you walk us through your career journey—from those early data entry days in Nigeria to your current role as a senior data engineer at The Planning Inspectorate? What were the game-changing moments that really fueled your passion for data and AI?
You know, it all started pretty humbly, with data entry gigs in Nigeria that drilled into me just how crucial clean, accurate data really is. I moved up through data analysis and eventually into data science, and honestly, those early roles were eye-opening. Even the simplest tasks, like punching in numbers, showed me how foundational good data is for pulling out real insights. During my MTech in Computer Science, I began building my first machine learning systems, predictive models for optimization that made me see data as not just numbers but a living system that could learn. At companies like FTZ Xpress Global Services and Interlink Voyagers, I saw data turn into a superpower, helping companies make smarter moves and innovate.
My MSc in Big Data Technologies in the UK took this further, giving me the tools to architect industrial-scale learning systems. Now, at Planning Inspectorate, I spearhead, develop, and make sure different data sources actually talk to each other, building an operational data warehouse so cross-platform teams can get what they need without jumping through hoops. Every step of the way, from nitty-gritty data work to leading projects, has just reinforced how much I love this field. Seeing how well-analyzed data can actually solve real problems? That's what keeps me hooked.
Your depression severity prediction research has made waves. What’s under the hood of your model, and how is it shaking up mental health care?
Well, traditional methods like PHQ-9 questionnaires are pretty limited; they don’t capture the full picture. Our approach? We throw in everything: demographics, clinical history, even lifestyle and diet. The model itself is a beast, a stacking ensemble that combines heavy hitters like Random Forest, XGBoost, LightGBM, Gradient Boosting, and k-Nearest Neighbors. The real magic? Using Random Forest as a meta-learner. It didn’t just hit crazy accuracy (R² of 0.93, RMSE of 1.33), but it’s also interpretable thanks to SHAP values.
This isn’t just about better predictions; it’s about giving clinicians something they can actually use. We’re pinpointing personalized risk factors, moving past one-size-fits-all diagnostics. It’s proof that AI can do more than just crunch numbers; it can make mental health care smarter and more tailored.
Your work on fraud detection and identity verification sounds like it’s straight out of a spy movie. How does your approach actually make these systems better?
Ha, it’s not quite Nollywood, but yeah, we’ve made some serious upgrades. For fraud detection, our hybrid AI system catches 97% of sketchy transactions while keeping false alarms down to just 4%. That’s a game-changer for banks drowning in millions of transactions daily.
And for ID verification? Forget just facial recognition; we combined it with palm vein patterns. Using a slick fusion method, we hit 96% accuracy with only a 5% error rate. Way more reliable than the old-school single-method stuff. The bottom line? Security doesn’t have to be a pain for users.
You’ve bridged academic research and real-world applications beautifully. Any standout examples where your work directly changed how things operate?
Absolutely. My predictive health research inspired Cloven Technology’s health management system. We took these dense algorithms and made them actually usable for doctors, turning complex predictions into clear, actionable insights. Patients now get monitoring that adapts to their health trends, not some generic template.
It’s one thing to publish a paper; it’s another to see your models actually helping clinicians catch risks earlier. That’s the real win.
From data entry to data leadership, what were the biggest hurdles, and how’d you tackle them?
Three big ones stick out:
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Making models work outside the lab. Early on, I learned the hard way that pristine academic datasets don’t prepare you for real-world chaos. Solution? More iteration, more collaboration.
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Fixing broken data pipelines. At Planning Inspectorate, we automated preprocessing and slashed prep time by 88%. Suddenly, analysts could focus on using data, not cleaning it.
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Keeping solutions human-centered. The flashiest AI means nothing if it doesn’t solve actual problems. Every project now starts with: Who’s this for, and what do they really need?
What’s next in AI and data science, and where do you see your work fitting in?
Mental health tech is going mobile; think real-time mood tracking via wearables. Fraud detection? It’ll need AI that adapts as criminals evolve. And biometrics will shift from deliberate scans to seamless, continuous checks (like palm-and-face combo).
The common thread? AI that’s not just smart but intuitive, working quietly in the background to make life safer, healthier, and easier. That’s the future I’m building toward.
Any advice for aspiring data scientists and AI researchers?
A few hard-earned lessons:
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Data tells stories. Even the dullest spreadsheet has clues; dig for the "why" behind the numbers.
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Build with people, not just code. The best models come from talking to end-users, whether they’re doctors, bankers, or patients.
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Fail like a scientist. When my early biometrics failed on darker skin tones, that "flaw" led to fairer, better systems. Debug, don’t despair.
And hey, never forget that the coolest AI doesn’t replace humans. It gives them superpowers.
Media information Name: Adefemi Ayodele
Title: Senior Data Engineer | AI Researcher | Thought Leader
Industry Expertise: AI & Machine Learning, Data Engineering, Data Analytics, Predictive Analytics, Fraud Detection, Biometric Security, Mental Health AI
Website/Portfolio: www.adefemiayodele.com
Source: Story.KISSPR.com
Release ID: 1392147