Melanie Mitchell

Computer Science Department
Maseeh College of Engineering and Computer Science
Portland State University

External Professor
Santa Fe Institute

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What's New       Biographical Sketch       Research Interests       Teaching       Research      Books      Publications      Other Links      

What's New:

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 80 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 as one of the ten best science books of 2009, and was longlisted for the Royal Society's 2010 book prize. Melanie originated the Santa Fe Institute's Complexity Explorer project, which offers online courses and other educational resources related to the field of complex systems.

Research Interests:

Artificial intelligence, machine learning, computer vision, cognitive science, complex systems.

Curriculum Vitae


2018-2019 Academic Year

Fall, 2018:

CS 445/545: Machine Learning

CS 570: Machine Learning Seminar
Topic: Fairness and Transparency in Machine Learning

Winter, 2019:

CS 445/545: Machine Learning

CS 570: Machine Learning Seminar
Topic: Transfer Learning

Spring, 2019:

CS 570: Machine Learning Seminar
Topic: Few-Shot Learning


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