Andrew Markham
Professor Andrew Markham
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
My Google Scholar Citations can be found here. Visit my personal website for more info about my research.
Research
I am a Professor of Computer Science and I work on sensors, signal processing/machine learning, and systems. My research revolves around making machines better understand the physical world. I work within the Cyber Physical Systems (CPS) theme, and lead a large group of researchers and students. I investigate how to track and localize people, animals and objects, particularly 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. Increasingly, my research has turned to data-driven methods, and physics-informed approaches to learning from noisy sensor data.
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. Within the department, I was a Postdoctoral Fellow from 2008 to 2012, I was appointed as an Associate Professor in 2013, and awarded the title of full Professor in 2021.
Some of my work has neatly been summarized in the following OxfordSparks animation.
Biography
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)
Selected Publications
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PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
Wei Wang‚ Bing Wang‚ Peijun Zhao‚ Changhao Chen‚ Ronald Clark‚ Bo Yang‚ Andrew Markham and Niki Trigoni
In IEEE Sensors Journal. 2021.
Details about PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization | BibTeX data for PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization | Download (pdf) of PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
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3D Motion Capture of an Unmodified Drone with Single−Chip Millimeter Wave Radar
N. Trigoni Z.Peijun C.X. Lu B. Wang and A. Markham
In IEEE International Conference on Robotics and Automation (ICRA). 2021.
Details about 3D Motion Capture of an Unmodified Drone with Single−Chip Millimeter Wave Radar | BibTeX data for 3D Motion Capture of an Unmodified Drone with Single−Chip Millimeter Wave Radar | Download (pdf) of 3D Motion Capture of an Unmodified Drone with Single−Chip Millimeter Wave Radar
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iMag+: An Accurate and Rapidly Deployable Inertial Magneto−Inductive SLAM System
N. Trigoni B. Wei and A. Markham
In IEEE Transactions on Mobile Computing. 2021.
Details about iMag+: An Accurate and Rapidly Deployable Inertial Magneto−Inductive SLAM System | BibTeX data for iMag+: An Accurate and Rapidly Deployable Inertial Magneto−Inductive SLAM System
Activities
- Cyber Physical Systems
- Workplace Autonomy
- 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