Dr Andrew Markham
Jan 2015: Our paper on novel magneto-inductive localization of underground animals has been published in Methods in Ecology and Evolution!
Apr 2014: We received the best paper award at IPSN 2014 for the paper "Lightweight map matching for indoor localisation using conditional random fields"!
Feb 2014: We received the best poster award at EWSN 2014 for the poster "A Case for Magneto-Inductive Indoor Localization"!
I am a University Lecturer (Associate Professor) in Software Engineering, looking at sensing and communication in extreme and challenging application. 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. This has been applied to monitoring animals in their underground habitats, allowing for the first time detailed reconstruction of animal trajectories in their underground burrows.
I also worked on the WildSensing project, which used wireless sensor nodes to monitor badger behaviour. My work is typically cross-disciplinary, and one interesting avenue of research was automatically evolving code for distributed computing. This used a computational analog of a biological process, termed a discrete Gene Regulatory Network (dGRN). 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.
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)
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Chris Xiaoxuan Lu‚ Xuan Kan‚ Bowen Du‚ Changhao Chen‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni and John Stankovic
In The Web Conference (WWW). 2019.
MotionTransformer: Transferring Neural Inertial Tracking Between Domains
Changhao Chen‚ Yishu Miao‚ Chris Xiaoxuan Lu‚ Linhai Xie‚ Phil Blunsom‚ Andrew Markham and Niki Trigoni
In The Thirty−Third AAAI Conference on Artificial Intelligence (AAAI−19). 2019.
Dense 3D Object Reconstruction from a Single Depth View
Bo Yang‚ Stefano Rosa‚ Andrew Markham‚ Niki Trigoni and Hongkai Wen
In IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018.
- 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