Melanie Mitchell is Professor of
Computer Science at Portland State University, and External Professor
and Member of the Science Board at the Santa Fe Institute. She
attended Brown University, where she majored in mathematics and did
research in astronomy, and the University of Michigan, where she
received a Ph.D. in computer science, Her dissertation, in
collaboration with her advisor Douglas Hofstadter, was the development
of Copycat, a computer program that makes analogies. She has held
faculty or professional positions at the University of Michigan, the
Santa Fe Institute, Los Alamos National Laboratory, the OGI School of
Science and Engineering, and Portland State University. She is the
author or editor of five books and over 70 scholarly papers in the
fields of artificial intelligence, cognitive science, and complex
systems. Her most recent
book, Complexity: A Guided Tour (Oxford, 2009),
won the 2010 Phi Beta Kappa Science Book Award. It was also
named by Amazon.com as one of the ten best science books of 2009, and
was longlisted for the Royal Society's 2010 book prize.
- Upcoming courses from Complexity Explorer
- Two new papers from my group:
- Thomure, M. D., Mitchell, M., and Kenyon, G. T. (2013). On the role of shape prototypes in hierarchical models of vision. In Proceedings of the International Joint Conference on Neural Networks (IJCNN).
- Landecker, W., Thomure, M. D., Bettencourt, L. M. A., Mitchell, M., Kenyon, G. T., and Brumby, S. P. (2013). Interpreting individual classifications of hierarchical networks. In Proceedings of the 2013 Conference on Computational Intelligence and Data Mining (CIDM 2013).
Fall, 2013: CS 346U / SYSC 346U: Exploring Complexity in Science and Technology
September 30 - December 13, 2013: Free online course, Introduction to Complexity, offered through the Santa Fe Institute. Register for e-mail announcements at complexityexplorer.org.
Winter, 2014: CS 445/545: Machine Learning
My research interests: Artificial intelligence, machine learning, and
complex systems. Evolutionary computation and artificial life.
Understanding how natural systems perform computation, and how to use
ideas from natural systems to develop new kinds of computational
systems. Cognitive science, particularly computer modeling of
perception and analogy-making, emergent computation and
representation, and philosophical foundations of cognitive science.