Mark Kon

Mark Kon works in quantum probability and information, bioinformatics, machine and statistical learning, mathematical physics, mathematical and computational neuroscience, complexity theory, and wavelets.  His current research focuses on two areas. 

The first is on questions in quantum probability, quantum computation and quantum information.  Quantum computation promises to solve some long-standing optimization problems arising in statistics and computational biology, including protein folding, RNA structure, and DNA transcriptional activity.  Quantum probability is related also to questions having applications in statistical mechanics.  These include questions related to dependence/independence (entanglement) of quantum random variables, and to ultimately to more general approaches to quantum computing methods themselves. 

A second area of study by Kon and his co-workers is in applications of machine learning to bioinformatics and computational biology, in areas ranging from inference of gene regulatory networks to identification and classification of cancers based on gene variation, single nucleotide polymorphisms, microRNA, and other biomarkers. Bioinformatic and transcription informatics applications of statistical and machine learning in fact have led to methodological and theoretical improvements in the statistical approaches themselves, which have become important in several aspects of these research projects.  These areas connect also with statistical complexity theory, neural networks, and Bayesian inference, where similar issues are prominent.  In this work Kon and his co-workers focus on connections between the above statistical approaches, and more generally on formulating more unified methodologies.  One unifying goal is to provide a general machine learning approach and algorithm set for the analysis of gene regulatory interactions and transcriptional control.