Building at the intersection of engineering, artificial intelligence, and creative design
I'm a Mechanical Engineering Graduate of the University of Nigeria, Nsukka, specializing in data science/Analytics, AI/ML applications in energy, and project management. My work spans from building physics-informed neural networks for petroleum engineering to designing intuitive user interfaces.
- 🔬 Research Focus: AI/ML in unconventional reservoir engineering, predictive modeling, PINN architectures
- 📊 Data Analytics: EDA, time series analysis, clustering, NLP sentiment analysis, interactive dashboards
- 🎨 Design: UI/UX design, brand identity, minimalist aesthetics
- 📈 Project Management: Agile methodologies, Scrum, lean, project leadership
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URTeC 2026 Paper | Physics-Informed Neural Networks for EUR Prediction in Unconventional Wells
Paper ID: URTeC-4495058 | Submitted & Published
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SPE NAICE 2026 | AI-Driven Bradley Curve Analysis for HSE Incident Prediction
Paper ID: SPE-NAICE2026-1017 | Introducing the Environmental Incident Intensity Index (EIII)
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Data Science Intern @ Codveda Technologies (ID: CV/A1/61250)
Six-level analytics sprint: EDA, clustering, NLP, interactive Tableau dashboards
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Junior Project Manager @ TS Academy
Leading Aurelius-1 IIoT deployment capstone project
- 🎓 B.Eng. Mechanical Engineering | University of Nigeria, Nsukka
- 🛢️ Society of Petroleum Engineers (SPE) | Active Member
- 📊 TechCrush AI/ML Bootcamp | Complrted
- 💼 TS Academy Project Management Capstone | Completed
- 📊 University of Tokyo GCI | In Progress
- 🎓 Aspire Leadership Program HARVARD | Completed
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URTeC 4495058 | Physics-Informed Neural Networks for Estimated Ultimate Recovery Prediction in Unconventional Wells
📄 Status: Published | Repository: GitHub -
SPE-NAICE2026-1017 | AI-Enhanced Bradley Curve Analysis: Predicting HSE Incidents in Oil & Gas Operations
📄 Status: Submitted | Novel Contribution: Environmental Incident Intensity Index (EIII)
Repository: GitHub -
SPE-NAICE2026 | Virtual Pipeline Technology Assessment for Niger Delta Stranded Gas Monetization
📄 Status: Submitted | Focus: CNG vs. mini-LNG TAC/NPV framework
- AAPG ICE 2026 (Jakarta, December 2026) | Abstract in development
class Tosa:
def __init__(self):
self.role = "Data Scientist & AI/ML Researcher"
self.education = "Mechanical Engineering @ UNN"
self.focus_areas = [
"Physics-Informed Neural Networks",
"Predictive Analytics in Energy Sector",
"Time Series Forecasting",
"NLP & Sentiment Analysis",
"Interactive Data Visualization"
]
self.current_learning = ["Deep Learning", "MLOps", "Advanced UI/UX"]
self.hobbies = ["Abstract Art", "Music", "Design", "Trading (Deriv)"]
def say_hi(self):
print("Let's build something amazing together!")
me = Tosa()
me.say_hi()
