Nick Howes, Analytics, Computing, and Technology Manager, brings years of experience in applied science and machine learning to The Water Institute.

During his eight years as an applied scientist at Shell, he worked on a cross-disciplinary team focused prototyping novel methods for subsurface stratigraphic characterization and was appointed a subject-matter expert in the geology of shallow marine and fluvial reservoirs in 2016. He developed a proprietary platform to store geologic and remote sensing records, automate feature engineering, and apply machine learning to assess exploration prospects and reservoirs characterized by significant geologic uncertainty (e.g., below seismic resolution). This technology is validated and assessed at >100 x multiple of its development cost.

Prior to joining The Water Institute, Howes was a Senior Technical Consultant with MathWorks, where he helped organizations scope, develop, and deploy science and engineering solutions, leading projects in areas of artificial intelligence, experiment management, big data, software engineering, and supporting projects in enterprise integration and application development.

Howes’ Machine Learning project portfolio spans energy, utilities, medical, semiconductor and finance industries, and includes applications of image-based and sequence-based deep learning, as well as cross-sectional, geospatial and forecast-based machine learning.

Howes’ geoscience research expertise and interests consider how coastal landscapes respond to forcings on O(1)-O(100) year timescales, including individual events, changes in the frequency and magnitude of these events, relative sea level rise, and how these changes impact people