I have been working in the fields of artificial intelligence, machine learning, and pattern recognition as a graduate student and university teacher for over three decades. From the beginning, my basic guideline has been to use a metric-based machine learning approach.
The fundamental research idea is that intelligent learning can be simulated as the process of finding a right metric space by a computational agent. Meaningful patterns in a complex, even chaotic, data space can be discovered by minimizing some combinations of distance functions over the space.
Ideas from this have been published in the following journals:
Artificial Intelligence
Bioinformatics
IEEE Transactions on Systems, Man, and Cybernetics
ACM's APL Quote Quad
Pattern Recognition