Dr Mike Lakoju
Research Associate in Data Science and Cyber Analytics
Yr Ysgol Cyfrifiadureg a Gwybodeg
- Email:
- lakojum@cardiff.ac.uk
- Telephone:
- +44 (0)29 2251 0845
- Location:
- Adeiladau'r Frenhines -Adeilad y De, 5 The Parade, Heol Casnewydd, Caerdydd, CF24 3AA
I am a Data Science and Cyber Analytics Research Associate in the School of Computer Science & Informatics. I am part of the Security, Privacy and Human Factors Research group and I work directly with Dr Pete Burnap. My current research -"Chatty Factories"- is a £1.5m project focused on revolutionising the manufacturing industry by creating a system which allows products securely "talk" directly to the factory floor thereby allowing the possibility of harnessing product use data in real time. My focus in the research involves Operational Technology Security, Machine Learning, Visualisation, Features reduction, Information Technology Security and Security Architecture Modelling.
My PhD research was focused on developing a Big Data Strategy Framework that can help Organisations Identify Value before implementing a Big Data project. I am an alumna of Brunel University London where I got a masters’ degree in Business Systems Integration with SAP Technology. My undergraduate degree was in Physics Electronics from the Federal University of Technology, Minna, Niger State, Nigeria.
With a unique blend, I have more than eleven (12) years of work experience, nine (9) of which were spent in industry. I am a professional member of the British Computer Society and a certified SAP consultant with skills in SAP BI/BW, SAP ABAP and SAP Terp10. I am also certified in SAS Base v9.
I lectured both MSc and Undergraduate students at Brunel University London and Greenwich School of Management London. I was responsible for modules like Data Visualization, Advanced Topics in Business Computing, Data and Information Module Labs, Business Analysis and process modelling, Logistics and Supply Chain Management, Innovation and Risk Management, etc.
My experience also includes but is not limited to multiple full life cycle implementations of leading ERP solutions, Data Management and Business Intelligence, Data Extraction, Transforming & Loading (ETL) and Business Consulting. Over the years, I have consulted as part of the SAP team for notable organizations like the Nigerian Liquified Natural Gas (NLNG) and the Nigerian National Petroleum Corporation (NNPC).
I have particular interest in Big Data, Cyber Security, Machine Learning, Data Visualization, and Artificial Intelligence.
2019
- Burnap, P.et al. 2019. Chatty factories: a vision for the future of product design and manufacture with IoT. Presented at: Living in the Internet of Things (IoT 2019), London, UK, 1-2 May 2019Living in the Internet of Things (IoT 2019). IET pp. 4 (6 pp.)., (10.1049/cp.2019.0129)
- Aldmour, R., Burnap, P. and Lakoju, M. 2019. Risk assessment methods for converged IoT and SCADA systems: review and recommendations. Presented at: Living in the Internet of Things (IoT 2019), London, UK, 1-2 May 2019Living in the Internet of Things (IoT 2019). IET pp. 5-10., (10.1049/cp.2019.0130)
2018
- Lakoju, M. and Serrano, A. 2018. Saving costs with a big data strategy framework. Presented at: IEEE International Conference on Big Data, Boston, MA, 11-14 Dec 20172017 IEEE International Conference on Big Data. IEEE pp. 2340., (10.1109/BigData.2017.8258188)
- Nuhu, K. A.et al. 2018. Investigating user responses to mandatory IT- induced organisational changes: a pre-implementation study. Presented at: European Conference on Information Systems 2018, Portsmouth, 23-28 June 2018.
2017
- Lakoju, M. and Serrano, A. 2017. Framework for aligning big-data strategy with organizational goals. Presented at: 23rd Americas Conference on Information Systems, Boston, MA, August 10-12, 2017AMCIS 2017 Proceedings.
- Lakoju, M. and Serrano, A. 2017. A strategic approach for visualizing the value of big data (SAVV-BIGD) framework. Presented at: 2016 IEEE International Conference on Big Data, Washington, DC, 5-8 Dec 20162016 IEEE International Conference on Big Data. IEEE pp. 1334-1339., (10.1109/BigData.2016.7840739)