Corinne Teeter

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email: first initial then last name at gmail

My general scientific interests are very diverse and can basically be summarized as a desire to learn and use theoretical/computational techniques to understand complex systems. My doctoral work has focused on neuroscience however my interests are much more broad. Many fields such as economics and ecology where there are interesting questions concerning the behavioral prediction of complicated systems are interesting to me.

I am currently a Ph.D. Student in Computational Neuroscience at the University of California, San Diego and the Salk Institute for Biological Studies. I will be graduating this winter. My thesis work is focused on quantitatively characterizing neural arbor shape in terms of probability density functions. Because fitting a function to a neural arbor is hard, I measure the moments of the arbors directly from 3 dimensionally reconstructed neurons (A probability density function can be described by it's moments). Investigation of neural arbor moments reveal that arbors are statistically self similar to one another. Statistical self similarity reveals that neural arbors density functions have the same basic shape. It also reveals the parameters that are needed to characterize the probability density function of an arbor. I investigate different pre-classified types of arbors in terms of these parameters and show that there are differences between different types of arbors but there is a lot of overlap between them.

My thesis work has involved techniques from neuroscience, physics (derivation of scaling relationship in arbors), biostatistics & machine learning (data analysis, probability density estimation, cluster analysis), data mining (extracting information from a large data base of neurons), signal processing (spike train analysis) and experimental electrophysiology (measuring ion channel conductances).

As far as projects go I am pretty flexible. I would generally be interested in exploring an astronomy, economics, or ecology problem. In terms of a very specific problem I would be interested in thinking about how to fit functions to high dimensional data--such as high dimensional maximum likelihood estimation (MLE), or comparisons between high dimensional data sets.

I look forward to meeting you all!