He received a PhD in Mathematics from MIT, and Bachelor's degrees in Mathematics, Physics, and Psychology from Cornell University.  He has had appointments at Columbia University as Assistant and Associate Professor (Computer Science, Mathematics), as well as at Harvard and at MIT.  He has served as departmental director of graduate studies at Boston University, and he is currently affiliated with the Bioinformatics Graduate Program.  He has published approximately 100 articles in mathematics and statistics, mathematical physics, computational biology, and computational neuroscience, including two books.  His recent research and applications interests involve quantum probability and information, statistics, machine learning, computational biology, computational neuroscience, and complexity.  He has recently pursued research in quantum computation and information, and his current work in machine learning has investigated complexities of designs for learning machines and neural networks which improve, sometimes significantly, on those for standard architectures.  Application areas of the latter include bioinformatics and genetic transcription informatics.  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, and the U.S. Air Force.  He has given approximately 100 lectures in 15 countries.  Among organizational roles, he has been a co-organizer for MIT summer analysis seminars in Vermont, and the organizer of a mini-symposium on Computational Complexity Theory in Chamonix, France. 

Research:  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.

 Online Publications

Abstracts (in progress):  

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