Terran Lane, Assistant Professor

Computer Science Dept., UNM

I'm an assistant professor of computer science at UNM. My primary (academic) interests are: machine learning; reinforcement learning, behavior, and control; and artificial intelligence in general. I'm also interested in computer/information security/privacy and bioinformatics.

Publications list


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.
  • Sushmita Roy (PhD): Genomics and bioinformatics.
  • 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

Undergraduate Research Positions

The UNM Machine Learning group has a number of positions open for undergraduate researchers. If you're considering grad school or would just like to get beyond your classes and into the cutting edge of the research world, please get in touch with me.

Embedded Machine Learning Systems
In collaboration with the University of Oklahoma, students will have the opportunity to learn about and work on machine learning in systems ranging from robotics to weather prediction to computer security. Students will have the opportunity to interact with colleagues at OU, participate in the broader research community, and travel to cutting edge research conferences.