Mark Kon

Mark Kon is Professor of Mathematics and Statistics at Boston University, and is affiliated with Quantum Information Group, the Bioinformatics Program and the Computational Neuroscience Program.

He received a PhD in Mathematics from MIT, and has Bachelor’s degrees in Mathematics, Physics, and Psychology from Cornell. He has had appointments at Columbia University as Assistant and Associate Professor (Computer Science, Mathematics), and at Harvard and MIT as a Visiting Professor.  He has also served as his department’s director of graduate studies. He has approximately 100 publications in mathematical quantum physics, statistics, machine learning, neural networks, and complexity, including one book. He is on the editorial board of Neural Networks, and has been on the organizing committee of the World Congress on Neural Networks twice. He has had research grants and contracts from the American Fulbright Commission, National Science Foundation, National Institutes of Health, and the U.S. Air Force. He has given over 120 talks in 15 countries.

Professor 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 Dr. 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 Dr. 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.