|
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), at Tufts University
as Assistant Professor, and at MIT as a graduate instructor. He has
served as departmental director of graduate studies at Boston University,
and he is currently affiliated with the Bioinformatics Graduate
Program. He has approximately 80 publications
in mathematics and statistics, computational biology, neural network
theory, and mathematical physics, including one book. His recent
research and applications interests involve machine learning, computational
biology, statistics and probability, neural networks, and complexity.
His recent 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 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 recent 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 machine learning,
bioinformatics, mathematical neural network theory, complexity theory,
statistical learning theory, wavelets, and mathematical physics. His
current research focuses on learning as a statistical phenomenon in which
an intelligent system learns to combine a priori information with current
data to form a model of an input-output function to be learned.
Bioinformatic and transcription informatic
applications of such approaches are important in several aspects of this
research. This area naturally
connects to complexity theory, neural networks, and Bayesian inference,
areas in which similar issues are prominent. He and his co-workers focus on
connections between these approaches, and more generally on formulation of
an approach which unifies them. One major goal of this project is to
provide a normative index in which learning algorithms arising from various
approaches can be compared in a single setting.
Online Publications
Abstracts (in progress):
Number of visitors since October 14, 2003
|
