Babatunde Anifowose

Dr Babatunde Anifowose holds a Diploma in Data Processing (Computer Science), BSc and MSc degrees from the University of Lagos, Akoka-Nigeria and a PhD in Environmental Science from the University of Birmingham UK (funded through the PTDF: 2007-2010). Babatunde’s current research is contributing to the emerging field of Environmental Data Science, with specific interest in Environmental Impact Assessment (EIA). In 2025, he enrolled onto a 6-month Data Science training course at the Imperial College London with expertise in Machine Learning techniques, Object Oriented Programming, Databases / Relational Databases, SQL/SQLite, Data Visualisation and Python Libraries (e.g., Tableau, NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn, SpaCy, TensorFlow, PyTorch etc.), Datasets and DataFrames, Data Cleaning & Preprocessing, Exploratory Data Analysis, Application of Data Visualisation and Applied Data Analysis. Other technical skills include Supervised learning (Linear & Logistic Regression, Decision Trees, Random Forests etc.), Unsupervised Learning (K-means Clustering, Hierarchical Clustering, PCA etc.), Neural Networks/Recurrent Neural Networks, Learning Algorithms, Sequential Models, Natural Language Processing, Semantic Similarity and NLP Applications, amongst others.

Dr Anifowose is a data enthusiast and a member of SPE’s Data Science and Engineering Analytics Technical Section, Board Member of the Methane Technical Section and the Sustainable Development Technical Section. He uses the GREET model to address climate change issues (e.g. GHG, methane emissions from energy facilities); and has worked on seasonal variation in river flow and its implications for pollution response, as well as hydraulic geometry principle. He is an invited peer-reviewer for a number of Elsevier Journals. He has been privileged to advice on many externally-funded projects such as an IOC’s river pollution case and its hydro-environmental implications; consulted for JICA etc.