Dr Habeeb Balogun

Dr Habeeb Balogun is a lecturer in data science at the University of Westminster. He has expertise in machine learning, natural language processing, data engineering and applied AI, with applications in healthcare, the built environment, energy and air quality. He has published over 20 peer-reviewed outputs, including first-author work in Scientific Reports, and has served as Principal Investigator or Co-Investigator on Innovate UK-funded projects with a combined worth of over £1.1 million (a detailed list of projects can be found below). His research philosophy is to bridge academic knowledge and industry practice by turning research ideas into production-ready solutions that solve real-world problems and improve productivity, safety and sustainability.

At Westminster, he is Module Leader for Machine Learning in Practice (MSc Applied AI) and Data Engineering (BSc Data Science) and leads a module delivered in partnership with IIT Sri Lanka, with assessments designed around real industry recruitment tasks. Prior to Westminster, he was a machine learning engineer at BDTI, where he developed and deployed AI-powered solutions across seven Innovate UK-funded projects in air quality, construction, energy and the circular economy and mentored junior research staff. He has also worked as a machine learning researcher at University College London on the early detection of pancreatic cancer.

Dr Habeeb holds a PhD in Data Science from the University of Hertfordshire and an MSc in Data Science from the University of Salford. He is a Fellow of the Higher Education Academy (FHEA). He serves on the Editorial Board of Scientific Reports (Nature Portfolio) and as Guest Editor of the Nature Portfolio collection on Knowledge-assisted Data Mining and is a member of the EPSRC Peer Review College. He also reviews for international journals, including Reliability Engineering & System Safety, Neurocomputing, Engineering, Construction and Architectural Management, Atmospheric Pollution Research and Energies.

He welcomes enquiries from prospective PhD and MSc students seeking supervision and from academic and industry partners interested in applied and responsible AI.

Research grants

LINK - Creating a digital direct connection between owners and buyers of salvaged construction material (Circular Economy), Innovate UK (ref. 10034201), September 2022 - March 2024. Grant Value: £392,765, Role: Principal Investigator. link

HS3 - Holistic Principal Tunnel-Sewer Survey System using Unmanned Aerial Vehicle and Artificial Intelligence + Big Data, Innovate UK, Industrial Strategy Challenge Fund (ref. 10004446), March 2021 - March 2022. Grant Value: £383,248, Role: Co-Investigator. link

Powerbox - Locally made Solar Home System for affordable mini off-grid, Innovate UK (ref. 10106535), March 2024 - March 2025. Grant Value: £233,559, Role: Co-Investigator. link

E-TRACS - Embedding Traceability in Manufacturing Construction Steel to Aid Reuse, Innovate UK, June 2023 - November 2023. Grant Value: £65,526, Role: Co-Investigator.

Aircons - Artificial Intelligence Reviewer of construction contract for subcontractor, Innovate UK (ref. 10081661), August 2023 - February 2024. Grant Value: £49,496, Role: Co-Investigator. link

PASS - Pollution Avoidance Support System using GIS, Machine Learning and Big Data, Innovate UK (ref. 10009455), November 2021 - April 2023. Grant Value: £393,712, Role: Researcher. link

Air-PoT - Clustered Blockchain Platform for Air Pollution Data Aggregation and Dissemination: A Big Data and Artificial Intelligence Approach to Air Pollution Tracking, Innovate UK (ref. 78362), September 2020 - June 2021. Grant Value: £427,754, Role: Researcher. link

PANC-CYS-GAN - A Multimodal Longitudinal Generative Adversarial Network (GAN) to Discriminate High-risk Cysts for the Early Detection of Pancreatic Cancer, Cancer Research UK, Pancreatic Cancer UK and EPSRC, 2022. Role: Researcher. panc-cys-gan.github.io

My research is in applied AI: using machine learning, deep learning and natural language processing to solve practical problems across domains. Current areas of application include:

- Built environment and construction, including deconstruction, material reuse and the circular economy 

- Environment and air quality, including pollution forecasting from IoT sensor data -

- Health, including patient-record data quality and early disease detection 

- Energy, including building energy performance and affordable off-grid systems 

- Legal and commercial documents, including automated contract review 

- Productionising AI: data engineering and deploying models into real use

I am open to applying these methods in new domains with academic, industry and public-sector partners.

For details of all my research outputs, visit my WestminsterResearch profile.