Dr Andrew Markham
My Google Scholar Citations can be found here.
I am an Associate Professor and I work on sensing systems, with applications from wildlife tracking to indoor robotics to checking that bridges are safe. I work in the cyberphysical systems group. I design novel sensors, investigate new algorithms (increasingly deep and reinforcement learning based) and apply these innovations to solving new problems. Previously I was an EPSRC Postdoctoral Research Fellow, working on the UnderTracker project. I am investigating how to localize people, animals and objects in environments where technologies like GPS fail, such as underground or indoors. Key to this is the use of magneto-inductive tracking and communication. My work is typically cross-disciplinary and I colloborate with colleagues from a wide range of disciplines. I obtained my PhD from the University of Cape Town, South Africa in 2008 researching the design and implementation of a wildlife tracking system, using heterogeneous wireless sensor networks.
Some of my work has neatly been summarized in the following OxfordSparks animation.
PhD in Electrical Engineering, University of Cape Town, South Africa (2008):
"On a wildlife tracking and telemetry system: a wireless network approach"
BSc in Electrical Engineering, First Class Honours, University of Cape Town, South Africa (2004)
RadarLoc: Learning to Relocalize in FMCW Radar
Wei Wang Pedro P. B. de Gusmao Bo Yang Andrew Markham and Niki Trigoni
In IEEE International Conference on Robotics and Automation (ICRA). 2021.
VMLoc: Variational Fusion For Learning−Based Multimodal Camera Localization
Kaichen Zhou Changhao Chen Bing Wang Muhamad Risqi U. Saputra Niki Trigoni and Andrew Markham
In AAAI Conference on Artificial Intelligence (AAAI). 2021.
milliEgo: Single−chip mmWave Radar Aided Egomotion Estimation via Deep Sensor Fusion
C. X. Lu M. R. U. Saputra P. Zhao Y. Almalioglu P. P. B. d. Gusmao C. Chen K. Sun N. Trigoni and A. Markham
In ACM Conference on Embedded Networked Sensor Systems (SenSys). 2020.
- Deep Learning Based Inertial Tracking
- Intuitive Physics
- Mobile and People-centric Systems and Sensing
- Intelligent Resource Constrained Systems
- Machine Learning Systems
- Distributed Sensing and Coordination
- Positioning in GPS-denied Environments
- Software Engineering