Artificial Intelligence & Machine Learning Analytics Research Group
Applying AI and machine learning for forecasting, image analysis, and decision support across industry and agriculture.
This group applies artificial intelligence and machine learning to problems where data is noisy, incomplete, or highly uncertain — environmental sensor readings, medical images, seasonally volatile commodity prices. The goal is turning large volumes of raw data into forecasts and decisions people can actually act on.
Research spans methods for filling in missing readings from river-telemetry sensor networks, forecasting urban air quality and fine particulate pollution, analyzing medical images to screen for retinal disease, and designing cleaner energy production processes using multi-stage machine learning. The toolkit draws on deep neural networks, time series analysis, and hyperspectral image processing.
What stands out is how quickly the same underlying techniques move across domains — from environmental monitoring to agriculture, from agriculture to public health — connecting the group's work to government agencies, agricultural producers, and healthcare institutions alike.