Numerical Intelligent Systems Laboratory

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