Welcome to the Home of the Numerical Intelligent Systems Laboratory

The Numerical Intelligent Systems Laboratory focuses on performing cutting-edge research into fundamental problems in artificial intelligence and machine learning from a numerical computation perspective. We are exploring problems in advanced knowledge representation, inference, and learning as it applies to system-level problems such as system monitoring and control, equipment health management, and precision agriculture. Techniques explored include probabilistic and Bayesian methods, evolutionary methods, and particle-based methods. We are also exploring problems in deep learning and explainable AI.

Funded Projects

• Continuous Time Bayesian Networks for risk-based prognostics and health management (PHM)
• Life prediction in grocery story produce using hyper-spectral imaging and deep learning
• Machine Learning and Topological Data Analysis for Prostate Cancer Diagnosis
• Optimal wavelength selection for multi-spectral image classification
• Optimized experimental design in on-farm precision experimentation
• Optimal work plan management for facility maintenance

Graduate Student Projects

• Compressed Convolutional Networks for Multi-Spectral Image Classification
• Evolutionary Design of Fertilizer Application in Precision Agriculture
• Evolutionary Wavelength Selection for Multi-Spectral Image Classification
• Fuzzy Spectral Clustering and Association Rule Analysis in Large Social Networks
• Transfer Learning for Wind Vector Determination in Hurricanes
• Uncertainty Estimation in Neural Networks Ensembles
• Yield and Protein Prediction for Winter Wheat using Ensembles of Neural Networks