- A short essay: How can the study of complexity transform our understanding of the world?
- Current and upcoming free 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).
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
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. Melanie directs the Santa Fe Institute's
project, which offers online courses and other
educational resources related to the field of complex systems.
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.