Research
In this Section:
- Anomaly Detection for Computer Security
- Learning and Planning in Large Stochastic Environments
- Reading List: MDPs and POMDPs for very large state spaces
- Alumni of the UNM ML Lab
- Bayesian Computational Neuroscience
- Machine Learning Reading Group
New! Most of my group's current and previous projects are linked from our Research Wiki.
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.
Bioinformatics Reading Group Web Site