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Biographical Sketch
What's New
Courses
Research
Books
Publications
Other Links
Biographical Sketch:
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.
Curriculum Vitae
What's New:
- A very sketchy bit of info on the Complexity Explorer Project here. I will add to this page as the project progresses.
- New on
my blog: "Blog on hold till April" (sad but true)
Teaching
Recent Courses
Winter, 2012: CS 445/545 Machine Learning
Fall, 2011:
- CS 346U Exploring Complexity in Science and Technology (University Studies)
- CS 441/541
Artificial Intelligence
- Spring, 2010:
- CS 446/546 Advanced Topics in Machine Learning
- CS 510 Information Retrieval on the Internet (co-teaching with Prof. Maier)
Winter, 2010: CS 445/545 Machine Learning
Fall, 2009: CS 346U
Exploring Complexity in Science and Technology
(CS Department and University Studies)
Winter, 2009: CS 445/545 Machine Learning
Fall, 2008: CS 441/541 Artificial Intelligence
Spring 2008: CS 410/510
Nonstandard Computation
Research
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.
Other Links
