Terran Lane, [Former] Associate Professor

Computer Science Dept., UNM

Until 2012, I was a professor of computer science at UNM. In July of 2012, I left my appointment there for a position at Google.

My primary (academic) 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 computer/information security/privacy.

Publications list

Full academic vita

My Academic Blog: Ars Experientia


Research

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) topology.

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 public wiki.

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!

Classes

More past classes