Thomas Johnson is an Academic Associate in the Department of Computer Science within the School of Science and Technology. In this role he splits his time between teaching as a part-time lecturer and working towards a part-time PhD in Pervasive Computing and Data Science.
Thomas is involved in teaching the following modules:
- Degree Apprenticeship:
- Mobile Platform Applications
- Database Eng & Internet Application Programming
- Project Concept and Planning
- Level 4
- Essential Skills (Web)
- Systems Analysis and Design
- Foundation of Comp & Tech (Python Programming)
- Level 5
- Internet Applications Development
He also supervises project work of Level 6 BSc students.
Thomas was awarded a BSc. (Hons) Information and Communications Technology in 2016 and a MSc Computing Systems in 2017 both from Nottingham Trent University. He went on to be awarded in 2018 a PGCE in Primary Education within the Wider Curriculum with Qualified Teaching Status (QTS) from the University of Derby.
In 2019, he returned to Nottingham Trent University as an Academic Associate in the Department of Computer Science.
PhD project title: Multimodel Sensor Fusion Approach to study the impact of Environment on Wellbeing.
Director of Studies: Professor. Eiman Kanjo
2nd Supervisor: Professor. Saeid Sanei
Thomas is part of the Smart Sensing Lab team. His research focuses on how stressors within the environment (such as: particulates, noise, gases) can have an impact towards mental wellbeing.
Invited talk at Clean Air Research Futures Group (CARFG): "Making it personal - will small / portable sensors transform air pollution management and research?” (October 2021)
IEEE International Smart Cities Conference: Sensor Fusion and The City: Visualisation and Aggregation of Environmental & Wellbeing Data (September 2021).
A full list of publications can be found at: https://www.researchgate.net/profile/Thomas-Johnson-43
- Johnson T., Kanjo E. (2021) Sensor Fusion and The City: Visualisation and Aggregation of Environmental & Wellbeing Data. Proceedings of the 2021 IEEE International Smart Cities Conference.
- Johnson T., Kanjo E., & Woodward, K. (2021) DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network. (Preprint)
- Woodward, K., Kanjo, E., Anderez, D. O., Anwar, A., Johnson, T., & Hunt, J. (2020). DigitalPPE: Low cost wearable that acts as a social distancingreminder and contact tracer: Poster abstract. SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems. https://doi.org/10.1145/3384419.3430600
- Johnson T, Kanjo E, Woodward, K. (2020) Sensor Data and the City: Urban Visualisation and Aggregation of Well-Being Data. (Preprint)
Johnson T., Kanjo E., & Woodward, K. (2021) Real-time Environmental Changes Impacts Mental Wellbeing. (Published February 2021)