Imperial College London


Faculty of Natural SciencesDepartment of Physics

Senior Teaching Fellow



+44 (0)20 7594 5240mark.richards Website




314Blackett LaboratorySouth Kensington Campus





Publication Type

11 results found

Kandiko Howson CB, Smith A, Durk J, Fox MFJ, Tymms V, Richards Met al., 2023, STRENGTHENING LEARNING COMMUNITIES: Belonging in a UK physics department, Academic Belonging in Higher Education: Fostering Student Connection, Competence, and Confidence, Pages: 80-94, ISBN: 9781642675283

After noticing significant achievement gaps between certain demographic groups in a large UK-based, research-intensive physics department, in 2020 institutional funding was secured to conduct research through the Strengthening Learning Communities project. The broad aims are to investigate the extent of attainment gaps between demographic groups, plus those students with protected characteristics; research the causes of any such gaps; and implement targeted strategies in pedagogical and pastoral practices within the institution to improve the university experience for all students and ensure parity for all. This chapter details five student-led or student partnership-based projects that raise awareness, offer interventions, and work toward increasing students’ academic sense of belonging and addressing inequalities in outcomes. This collaborative partnership represents a significant institutional commitment to tackling awarding gaps across student groups, supporting research and practice to improve belonging across a diverse student body, and investing in a growing community of physics education research, a nascent field in the UK.

Book chapter

Wade J, Rabey IM, Smith A, Martin SA, Okenyi M, Ohene Y, Richards MDet al., 2022, Lessons from a UK research school for Black physicists and engineers, Nature Reviews Materials, Vol: 7, Pages: 927-928, ISSN: 2058-8437

Journal article

North R, Cohen J, Wilkins S, Richards M, Hoose N, Polak J, Bell M, Blythe P, Sharif B, Neasham J, Suresh V, Galatioto F, Hill G, Mead I, Jones R, Beresford A, Chen H, Ropkins K, Goodman P, Oates C, Tate J, Ballijepalli Net al., 2009, Field deployments of the environmental monitoring, Traffic Engineering and Control, Vol: 50, Pages: 484-488, ISSN: 0041-0683

This paper describes how the technologies developed in the MESSAGE project have been deployed in a series of real-world experiments to examine the relationship between transport and air pollution. Three different sensor systems have been developed to allow deployment on infrastructure, people and vehicles for both short and long term studies. This paper describes the field trials conducted using each of these systems in turn. The initial conclusions regarding the coordinated use of these sensors in the management of transport and air pollution are presented.

Journal article

Ma Y, Richards M, Ghanem M, Guo Y, Hassard Jet al., 2008, Air pollution monitoring and mining based on sensor grid in London, Sensors, Vol: 8, Pages: 3601-3623, ISSN: 1424-8220

In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a twolayer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

Journal article

Hoose N, North R, Cohen J, Richards Met al., 2008, The use of mobile sensors and GRID computing for managing the environmental impact of traffic., 7th European Congress on ITS

Conference paper

Richards M, North R, Ghanem M, 2008, Environmental Sensor Grids - The Next Generation, Better Air Quality (BAQ)

Conference paper

North R, Richards M, Hassard J, Cohen J, Hoose N, Polak Pet al., 2008, A Mobile Environmental Sensing System to Manage Transportation and Urban Air Quality., IEEE International Symposium on Circuits and Systems (ISCAS)

Conference paper

North R, Richards M, Cohen J, Hoose N, Hassard J, Polak Jet al., 2008, A mobile environmental sensing system to manage transportation and urban air quality, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 1994-+, ISSN: 0271-4302

Conference paper

Richards M, Ghanem M, Osmond MA, Guo YK, Hassard Jet al., 2006, Grid based analysis of air pollution data, Proceedings of the 4th European conference on ecological modelling, 4th international workshop on environmental applications of data mining (ECEM/EAML 2004), Publisher: Elsevier, Pages: 274-286, ISSN: 0304-3800

Conference paper

Ghanem M, Guo Y, Hassard J, Osmond M, Richards Met al., 2004, Sensor Grids for air pollution monitoring, 3rd UK e-Science All-hands Conference AHM 2004, Nottingham, UK, Publisher: EPSRC, Pages: 106-113

Conference paper

Richards M, Ghanem M, Guo Y, Osmond M, Hassard Het al., Grid-based Analysis of Air Pollution Data, Ecological Modelling, Vol: 194, Pages: 274-286

n this paper, we present a distributed infrastructure based on Grid computing technology and data integration and mining tools to discuss the main informatics challenges that arise when a high-throughput sensor network is constructed to address real-time urban air pollution monitoring and mapping. We present the background to urban air pollution monitoring and modelling and describe the high-throughput sensors developed within this project to tackle the problem. We present a solution that addresses the informatics challenges based on the integration of distributed sensors, Grid technologies, data integration, data mining and GIS systems. We also present a case study for examining the effectiveness of visual and automated methods developed for the analysis of generated data sets.

Journal article

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