Maren completed his BSc in computer science from the University of Jos in 2008. He obtained his MSc in Computer Forensics and cybersecurity from the University of Greenwich London in 2015. Maren is now in Cranfield University pursuing his PhD in the Integrated Vehicle Health Management (IVHM) centre working on data analytics for predictive maintenance in aerospace. Maren has worked as a research assistant, machine learning analyst, digital forensic and cybersecurity engineer at several companies including the School of Water, Energy and Environment (SWEE) Cranfield University and Nigerian Security and Civil Defence Corps. As a machine learning analyst, Maren applied machine learning to infer individual loads from aggregate energy consumption data. He was also responsible for researching and deploying machine learning and deep learning models as scalable prototypes for user evaluation. His other roles include building and maintaining data pipelines and products using Python and data analysis tools including MySQL, Tableau, Jupyter and dash. His current research interests involve big data analytics, machine learning and predictive maintenance techniques in the aerospace industry.
Imbalance learning for industrial application
application of reinforcement learning for vehicle maintenance
Articles In Journals
- Dangut MD, Skaf Z & Jennions I (2020) Rare failure prediction using an integrated auto-encoder and bidirectional gated recurrent unit network, IFAC-PapersOnLine, 53 (3) 276-282.
- Dangut MD, Skaf Z & Jennions IK (2020) An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset, ISA Transactions, Available online 11 May 2020.
- Dangut MD, Jennions I & Skaf Z (2020) Rescaled-LSTM for predicting aircraft component replacement under imbalanced dataset constraint. In: 2020 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, United Arab Emirates, 4-6 February 2020.
- Maren DD, Zakwan S & Ian KJ (2020) Aircraft predictive maintenance modeling using a hybrid imbalance learning approach. In: TESConf 2020 - 9th International Conference on Through-life Engineering Services, Online, 3-4 November 2020.