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)
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.
Learning with training wheels: Speeding up training with a simple controller for deep reinforcement learning
N Trigoni L Xie S Wang S Rosa AC Markham
In IEEE Intl Conference on Robotics and Automation (ICRA). 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