Nicholas Wayand, Ph.D.
Senior Applied Scientist @ Amazon
I'm an Earth scientist specializing in machine learning and environmental modeling. I build predictive systems that transform weather data, satellite imagery, and sensor networks into operational decision-making tools, quantifying risk and uncertainty from messy, real-world data at scale. My work bridges climate science, hydrology, and applied ML to solve problems in energy systems, transportation, and environmental forecasting. Having worked in both industry and academia, I like connecting ideas across different fields.
Developing models to optimize electric vehicle charging networks that account for temperature impacts on battery performance and grid capacity under changing climate conditions.
Building models for supply chain risk assessment, logistics optimization, and demand forecasting under uncertainty. Combining computer vision, weather models, and real-time sensor data to predict road surface conditions and mitigate operational disruptions across transportation networks.
Integrating physics-based hydrological models with machine learning techniques to enhance snowmelt predictions and water resource forecasting in complex terrain.
Developed ML model to estimate road conditions from vehicle camera images. Presented at the 104th AMS Annual Meeting.
Framework for operational snowmelt forecasting over the Canadian Rockies combining weather forecasts, triangular-mesh hydrological model, and snow data assimilation.
Platform for Arctic sea ice forecast skill assessment. Provides real-time visualization comparing 25+ operational forecast models with 33% improvement in prediction skill.