Tom_Mitchell - Computer Science

Tom M. Mitchell
Fredkin Professor of Computer Science
Carnegie Mellon University
Thursday December 9, 2004
1170 TMCB, 11:00 AM
Using Machine Learning and Brain Imaging to
Study Cognitive Processes
Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as an
important new method for studying cognitive processes in the human brain. A typical fMRI
experiment captures a sequence of three-dimensional images of brain activity, once per second,
at a spatial resolution of a few millimeters. This talk will present our recent research exploring
the question of how best to analyze fMRI data to build models of human cognitive processes.
We will first describe our recent successes training machine learning classifiers to distinguish
cognitive subprocesses based on observed fMRI images. For example, we have been able to
train classifiers to discriminate whether a person is reading words about tools, or words about
buildings, based on their observed fMRI brain activation. We will then describe our more recent
research on learning more complex models capable of tracking multiple cognitive processes
that overlap in time and space within the brain.
Tom M. Mitchell is the Fredkin Professor of Computer Science at Carnegie Mellon University.
His research lies in the areas of machine learning, artificial intelligence, and cognitive
neuroscience. Mitchell is author of the textbook "Machine Learning," Past President of the
American Association of Artificial Intelligence (AAAI), and a member of the US National
Research Council's Computer Science and Telecommunications Board. In 2002 he received
the Debye Prize from the Edmund Hustinx Foundation for his research in computer science.
Mitchell is the founding director of CMU's Center for Automated Learning and Discovery, an
interdisciplinary research center specializing in statistical machine learning and data mining, and
the first institution to offer a Ph.D. program specifically in this area. Mitchell's recent research
has focused on machine learning approaches to analyzing human brain function based on fMRI
data, and on machine learning for intelligent personal assistants.
Donuts will be provided