Research on symbolic and numerical approaches to machine learning, first order logic, intelligent document processing, spatial data mining, human-computer interaction.

Research on learning first-order classification rules, first-order concept descriptions, genetic algorithms, neural networks, computational learning theory.

Research on inductive logic programming for natural language processing and for knowledge discovery in databases.

Applications of soft computing (fuzzy systems, neural networks, and genetic algorithms) in machine learning. Manuscripts and MATLAB codes related to fuzzy clustering and classification, and visualization and analysis of high-dimensional data.

Techniques include inductive logic programming, model based reasoning, evolutionary computing, neural networks, multivariate statistics. Applications to drig design, protein secondary structure prediction, functional genomics, etc.

An internationally recognized node of machine learning researchers at the University of Alberta

Focuses on theory of logic and learning, and applied intelligent systems. Methodolgies range from traditional knowledge-based systems and neural networks to machine learning, agents, and evolutionary computation.

An on-line handwriting recognition engine based upon statistical dynamic time warping (SDTW) and support vector machines with a Gaussian DTW kernel (SVM-GDTW).

Research on Machine Learning in Robotics, Factory Automation, and Assistance Systems.

Analysis of functional genomics data, Construction of data-dependent metrics for focusing data analysis on relevant or important aspects of the data.