Roly Hudson
Director at Lacunae
Dublin, Dublin 1, Ireland
Summary
👤
Senior🎓
Top SchoolRoly Hudson is a Director and founder of Lacunae who blends deep expertise in software engineering with a PhD in architecture to deliver computational design tools for the built environment. With over a decade of experience across academia and industry, he leads interdisciplinary teams to build web-integrated CAD/BIM workflows that marry geometric, geospatial and climatic datasets. His work has automated tasks from masterplan management and stadium view analysis to whole-building geometry control and bespoke façade fabrication. Roly applies agile, model-driven development to create plugins, custom software and training that embed advanced geometry processing into everyday BIM practice. He splits time between consulting and academic collaboration, having developed curricula and won funded research partnerships that translate research into production-ready tools. Based in Dublin, he is known for turning abstract design spaces into efficient manufacturing and construction procedures that broaden practical design possibilities.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Software Engineering, (part-time), Master of Science - MS, Software Engineering, (part-time) at University of LiverpoolDoctor of Philosophy - PhD, Architecture, Doctor of Philosophy - PhD, Architecture at University of Bath
Spanish
Github Skills (66)
object-model10
engine10
rhinoceros10
geometry10
scripting10
adapter10
machine-learning10
manipulation10
gis10
openstreetmap9
rhino9
utility9
grasshopper8
python7
azure7
Programming languages (5)
Github contributions (5)
BHoM/OpenStreetMap_Toolkit
Nov 2019 - Feb 2022
Tools for working with OpenStreetMap
Contributions:3 reviews, 134 commits, 41 PRs in 2 years 3 months
adaptertoolkitosmbhomgis
rolyhudson/climacolombia
Jul 2018 - Jul 2019
This is the code and documention repository for Roland Hudson's dissertation project for a Master of Science in Software Engineering at the University of Liverpool. The code defines a distributed, machine-learning application that classifies Colombian climate data and provides decision support to environmental designers seeking to understand the spatial and temporal use of low-energy design strategies.
Contributions:2 PRs, 79 pushes, 4 branches in 1 year
climatespatialuniversity-of-liverpoolengineeringscience
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