I'm a PhD student in Computer Science at the University of Southampton. I started my career with an undergraduate and masters in Archaeology and while writing my dissertation I noted a gap within the research of automated methods for the detection of archaeological sites. Looking beyond the research within archaeology I found that automation can be done for very challenging tasks and so I decided to pursuit a PhD into the most advanced method out there; Deep Learning.
The critique by archaeologists towards automation has slowed down research into this field. It is my mission to get automation back on the research agenda and to get computer scientists into the loop. It can be said that archaeologists are faced with one of the most challenging tasks of geolocated object extraction as archaeological sites are some of the most overwitten objects in the landscape. I believe that together with computer scientists we can create robust methods for detection and move both fields forward.
Thus far, I've presented my views and research at conferences directed at archaeology, computer applications and remote sensing across the globe. Videos and slides of conference presentations and interviews can be found below.
Thesis: supervised by Dr. J. Hare: "New Approaches to Archaeology using Deep Learning with Remote Sensor Data"
Funding: Research Studentship is fully supported via an ECS DTP and Ordnance Survey Studentship award.
Le Wagon is an intensive 12 weeks full-time training school in web application development for front-end and back-end coding using mainly Ruby and Ruby on Rails. Amsterdam, The Netherlands.
Modules: “Advanced GIS and Spatial Technologies for Archaeological Landscapes”, “GIS for Environmental Management”, “3D Remote Sensing”, “Semantic Web Technologies” and “Digital Image Processing”.
Thesis: distinction (82), supervised by Dr. D. Wheatley: “An archaeological reaction to the remote sensing data explosion. Reviewing the research on semi-automated pattern recognition and assessing the potential to integrate artificial intelligence”.
Funding: £800 from the sponsored MSc programme of the Ordinance Survey, United Kingdom
Bachelor specialisation: Computer Applications, Classical Archaeology (average 7.1).
Thesis: 7.5 (equivalent to 2:1), supervised by Dr. H. Kamermans: “From survey to excavation planning through extensive data analysis in GIS”.
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