Boosting our machine learning expertise
Right from his childhood bedroom, Egil Möller got an early start on his journey through cyberspace. Since then, he has worked with countless programming languages and abstraction layers. Now he is ready to revolutionize EMerald Geomodelling’s machine learning algorithms for the benefit of both customers and the environment.
As a young company, EMerald Geomodelling continuously seeks expertise to enhance and further develop its unique ideas.
“Möller is a wildcard”, says Andreas Pfaffhuber, CEO of EMerald Geomodelling. “His experience is mainly from trying and failing from a young age. While large companies look for solid university backgrounds, we want experience and engagement. Möller is a perfect fit for our continuous search to learn, understand and improve our solutions.”
Our backbone is AI
Egil started programming at 14 and has continuously worked his way through many programming languages and abstraction levels, from assembly to C, LISP, JS and Python and from low-level Unix utilities to web development to data pipelines to machine learning.
“I’ve always had a passion for using computers as a tool to help people stop doing boring, repetitive tasks”, says Egil Möller. “With AI and machine learning become more sophisticated today, it means that I can help my colleagues to spend less time doing the boring, routine data analysis and get to insights faster.”
He has experience from Global Fishing Watch fishery management and IUU tracing environmental project, developing visualization for flaring from oil refineries, worked on the Etherpad open source collaborative online text editor and wrote the first graphical boot screen for Linux.
Scale up a start-up
The core of Egil’s workflow is to scale up and automate the processing pipeline and machine learning solutions, in addition to making the solutions more maintainable.
“Our goal is to combine and unlock datatypes from different disciplines that usually need domain experts”, says Pfaffhuber. “A lot of value stays hidden if end users don´t have an easy and understandable way to access this information. Egil’s skills will improve our models to become even more reliable and include numerous additional types of information. This will give our clients faster results and better model quality.”
As of today, EMerald Geomodelling’s machine learning algorithms combine geotechnical soundings with geophysical airborne geoscanning data to give important insights on sediment thickness, sediment types, weakness zones, and bedrock lithology. With more input data types and a streamlined pipeline, these existing modelling products will become more accurate and time-efficient, and new ones will be developed.
“What drives me is the knowledge that when we give our clients insights early in a project, we help reduce the cost and environmental impacts of construction”, says Möller. “That’s what motivates me to keep improving our technology.”
Having such a varied skillset and past experience combined with his enthusiasm are sure to help EMerald Geomodelling achieve its goal of reducing risks in infrastructure projects worldwide.
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