Research Assistant/Database Manager
Mahmoud has an MSc from University College London (UCL) on a fully funded Chevening scholarship. His dissertation focused on building an algorithm to extract the optimal frames for 3D models out of videos. He was elected as a class representative and eventually graduated with Distinction.
With skills in data analysis and programming, which were strengthened during his time at UCL, and a background in spatiotemporal analytics, big data mining, geology and remote sensing, Mahmoud is uniquely placed to work with MAEASaM on database and web development. This includes implementing new features in Arches and analysing the collected data, using machine learning methods to examine the archaeological data and reveal undiscovered patterns and stories.
Mahmoud is passionate about applying machine learning and data science techniques to sift through the data and extract meaning. His interest in computer vision and 3D modelling flows from this, particularly his work on web development (especially in Arches and GIS) and remote sensing process automation.
I’m fired up by driving meaning and use out of data. In other words, I want to bring data to life and make it easy to understand the how, where and why. Most of my data-related work has either spatial or temporal aspects or a combination of these. It’s my goal to understand the significance of these two factors along with hidden patterns within these data, then present data-driven insights in a visually appealing and approachable way.
When he is not typing code, Mahmoud can be found listening to and recording audiobooks as well as beach running and swimming. He is an active member of the Future Leaders Connect network.
Read more about Mahmoud on LinkedIn, Orcid, GitHub and Twitter.