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[Colloquium] Graph and Structure Inference for Scientific Data Mining

January 19, 2012

Watch Colloquium: 

M4V file (503 MB)

  • Date: Thursday, January 19, 2012 
  • Time: 11:00 am — 12:15 pm 
  • Place: Mechanical Engineering 218

Terran Lane 
UNM Department of Computer Science

Many modern scientific phenomena are best described in terms of graphs. From social networks to brain activity networks to genetic networks to information networks, attention is increasingly shifting to data that describe or originate in graph structures. But because of nonlinearities and statistical dependencies in graphical data, most “traditional” statistical methods are not well suited to such data. Coupled with the explosion of raw data, stemming from revolutions inscientific measurement equipment, domain scientists are facing steep challenges in statistical inference and data mining.

In this talk, I will describe work that my group has been doing on the identification of graph structure from indirect data. This problem is very familiar to the machine learning community, where it is known to be both computationally and statistically challenging, but has received substantially less attention in a number of scientific communities, where it is of substantial practical interest. I will examine an approach to graph structure inference that roots into the topology of graph structure space. By imposing metric structure on this otherwise unstructured set, we can develop fast, efficient, accurate inference mechanisms. I will explain our approach and illustrate the core idea and variants with examples drawn from neuroscience and genomics and introduce recent results on malware identification.

 

Bio: Terran Lane is an associate professor of computer science at UNM. His personal research interests include behavioral modeling and learning to act/behave (reinforcement learning), scalability, representation, and the tradeoff between stochastic and deterministic modeling. All of these represent different facets of his 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 he attempts to understand the core learning issues involved, he often situates his work in domain studies in practical problems. Doing so both elucidates important issues and problems for the learning community and provides useful techniques to other disciplines.