For over 25 years the MaxEnt workshops have explored the use of Bayesian and Maximum Entropy methods in scientific and engineering applications. All aspects of probabilistic inference such as Techniques, Applications, and Foundations, are of interest. With the rapid growth of computing power, computational techniques such as Markov chain Monte Carlo sampling are of great interest, as are approximate inferential methods. Application areas include, but are not limited to: Astronomy and Astrophysics, Genetics, Geophysics, Medical Imaging, Material Science, Nanoscience, Source Separation, Particle Physics, Quantum Mechanics, Plasma Physics, Chemistry, Earth Science, Climate Studies, Engineering and Robotics. Foundational issues involving probability theory and information theory, and inference and inquiry are also of keen interest as there are yet many open questions.

MaxEnt Workshops Inc. was incorporated in 2006 to enable the organization of scientific workshops focused on the topics of Bayesian inference and maximum entropy methods for science and engineering. The organization and running of the such workshops is not performed by MaxEnt Workshops Inc., but rather by the Local Organizing Committee for each particular meeting. These Local Organizing Committees are typcially selected during previous meetings by the Advisory Committees and often the participants themselves. The purpose of MaxEnt Workshops Inc. is to facilitate the funding of such meetings and to support young scientists from the U.S. and abroad who wish to participate in these exciting new research directions.

Please consider a donation.
Your funds will go toward supporting our scientific workshops and giving the opportunity to young scientists from the U.S. and abroad to present their work and learn about the most recent advances in statistical scientific data analysis and signal processing.

Officer Biographies

Kevin H. Knuth
Kevin Knuth was born in Fond du Lac, Wisconsin, USA in 1965.  He received his B.S. in science from the University of Wisconsin Oshkosh, his M.S. in Physics from the University of Montana, and his Ph.D. in physics with a minor in mathematics at the University of Minnesota in 1995. He has held postdoctoral positions studying neuroscience at Louisiana State University Medical Center in New Orleans and at Albert Einstein College of Medicine and the City University of New York in New York City.  He was an Instructor at thw Weill Medical College of Cornell University in 1999 and from 1999-2001 was a Research Scientist at the Center for Advanced Brain Imaging in Orangeburg NY. From 2001 through 2005 he was a research scientist at NASA Ames Research Center in the Intelligent Systems Division developing machine learning techniques and their applications in the Autonomous Systems and Robotics Area.  Currently he is an Assistant Professor in Physics at the University at Albany, State University of New York.  He has 12 years experience designing and implementing machine learning algorithms for data analysis applied to physical systems in fields including: Earth Science (climate and clouds), Neuroscience (EEG analysis), and Astrophysics (planetary nebula modeling and characterization). His scientific interests include: probability theory, astrophysics, complex systems, earth science, and brain dynamics.  For recreation, he enjoys hiking, birdwatching and poking around tidal pools.