Dr Babatunde Anifowose CEnv., MSPE, MEI, SFHEA holds a Diploma in Data Processing (Computer Science), BSc and MSc degrees from the University of Lagos, Akoka-Nigeria and a PhD in Geography and Environmental Science from the University of Birmingham UK (funded by the Nigerian Government through the PTDF: 2007-2010). Babatunde’s current research is contributing to the emerging field of Environmental Data Science. 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 the Japan International Cooperation Agency (JICA) Research Institute; and served as specialist adviser/reviewer to African Development Bank’s  projects titled “Enhancing the Performance of African National Oil Companies”; and the “Dasgupta Review on Economics of Biodiversity: Consultation and Presentation of the Interim Report” whose Advisory Panel is Chaired by Her Majesty’s Exchequer Secretary to the UK Treasury. He is a professional member of the British Hydrological Society (with the Institution of Civil Engineers), the Society of Petroleum Engineers, and has been a friend of the Transportation Research Board (TRB) of the US National Academies of Science’s committee on Transportation of Hazardous Materials – AT040 since 2008. Dr Anifowose is a Senior Fellow of the UK Higher Education Academy and a member of the UK Engineering and Physical Sciences Research Council’s (EPSRC) Peer Review College. He is former Chair of SPE’s Engineering Professionalism Committee (2021-2022) and the current Chair of the Methane Technical Section (2025-2028).

1. Innovative application of AI/Machine Learning techniques in environmental problem-solving e.g., NLP, semantic similarity, LSTM/RNN, prediction etc. usng Python programming 

2. Sustainability in the Energy Industry - Principles of Sustainability 

3. Environmental Impact Assessment (EIA) of complex projects (e.g., oil and gas, renewables), and Project Performance Evaluation 

4. River systems, pollution response modelling and water resource management in an EIA context

5. Economic modelling of energy systems; Decarbonisation and energy system transitions.

I previously did and have expertise in: Oil spill modelling; Flow description for oil spill trajectory simulation; Hydraulic Geometry Principle; Oil and Gas Transportation; Oil and Gas EIA/EIS Quality; Environmental GIS and Remote Sensing; Projects, Facilities and Construction phase interaction with Environmental receptors

To shape the emerging field of Environmental Data Science (EDS) as one of the early callers, offering novel solutions to contemporary environmental issues by leveraging the ongoing advances in Data Science. 

To be a global leader in EDS