Chris develops and implements machine learning solutions to enhance Fathom’s flood and climate modeling capabilities, bridging the gap between numerical scientific methods and modern AI approaches.
Chris completed his PhD in high energy physics at the Compound Muon Solenoid (CMS) experiment at CERN before transitioning into software engineering and machine learning. His career has spanned medical diagnosis using Bayesian inference, vehicle condition assessment with computer vision modeling, patient support systems using large language models and much more, always focusing on creating real-world impact through machine learning applications.
At Fathom, Chris is making the most of the opportunity to advance scientific progress through machine learning while addressing crucial real-world challenges in flood modeling. Working with world leaders in flood and climate modelling is a huge motivation for him, as it provides an opportunity to share and combine complementary backgrounds and experiences to be greater than the sum of our parts.
In his spare time, Chris can be found outdoors getting back to nature, either mountain biking, climbing or hiking.
