Terran Lane, [Former] Associate Professor
Until 2012, I was a professor of computer science at UNM. In July
of 2012, I left my appointment there for a position
interests are: machine learning; scientific data mining;
reinforcement learning, behavior, and control; and artificial
intelligence in general. I'm also interested in computational
neuroscience, bioinformatics, and
Machine learning is simultaneously a pragmatic discipline,
concerned with the analysis of complex data from a variety of fields,
and a theoretical one, concerned with the principles of what is
learnable, how to represent acquired knowledge, how to deal with
complexity/dimensionality, the interactions between learned knowledge
and behavior, how to measure acquired knowledge, and so on. The tools
we use include statistics, algorithms, knowledge representation,
database theory, linear algebra, and (in recent developments)
My personal research interests include behavioral modeling and
learning to act/behave (i.e., reinforcement learning), scalability,
representation, and the tradeoff between stochastic and deterministic
modeling. All of these represent different facets of my overall
interest in scaling learning methods to large, complex spaces and
using them to learn to perform lengthy, complicated tasks and to
generalize over behaviors. While I attempt to understand the core
learning issues involved, I often situate my work in domain studies in
practical (well, ok, semi-practical anyway) problems. Doing so both
elucidates important issues and problems for the learning community
and provides useful techniques to other disciplines.
For information on specific current and past research projects, please
check out the ML Group
Students and Postdocs
I'm fortunate enough to have the chance to work with a number of excellent students here at UNM who're deeply involved in various interesting ML projects including analysis of fMRI (neuroimaging) data, bioinformatics, and reinforcement learning. Check in with each of them to find out what they're up to!
- Blake Anderson (PhD): Collaborative and topological learning.
- Eva Besada-Portas (visiting scholar): Multi-agent reinforcement
learning and control.
- Josh Neil (PhD): Statistical models of network data.
- Diane Oyen (PhD): Analysis of fMRI data; scientific data mining.
- Sergey Plis (Postdoc):
Bayesian MEG/fMRI data fusion.
- Mark Scully (MS): Network inference from neuroimaging data.
- Ben Yackley (PhD): Network inference from neuroimaging data.
Also, check out the long list of illustrious Alumni of the ML lab!
More past classes