Komo MaiResearch Publications Teaching

Research Interests: Computational Neuroscience and Unsupervised learning

One of the most thoroughly studied neural systems is the stomatogastric ganglion (STG) of the lobster. This system is often modeled as a ring of coupled, nonlinear oscillators, and interesting phase relationships can occur, depending on biophysical parameters. These oscillator ring models are fairly powerful and have been used to model many phenomena in neuroscience.

S. Still and G. LeMasson.

Unsupervised learning is at the heart of neural information processing systems. It also constitutes one of the most freequently used data analysis methods with applications in a variety of fields ranging from bioinformatics to computer vision to ecology, geophysics, and astronomy.

One of the most challenging problems in cluster analysis is to determine the number of clusters we can resolve from a given data set. It is related to controlling the complexity of a model, a crucial task in machine learning. We derived a complexity control term within an information theoretical framework. This allows us to determine the maximal number of clusters which can be resolved given the size of the data set.

S. Still and W. Bialek.

This approach allows us to re-visited geometric clustering algorithms, including the widely used k-means clustering algorithm, which can be understood in terms of this framework. The approach suggests a quenched annealing method that allows one to improve the k-means algorithm, dramatically increasing the likelyhood of finding the global optimum.

S. Still and W. Bialek.

Student Projects (499, 699) and thesis projects:

Theory:

1. Learning in neural networks

I am looking for a student with an interest in learning theory and neuroscience.

2. Coupled oscillator networks

Applications of coupled oscillator models to computational neuroscience. Possible subjects: STG, movement control, navigation. On the Manoa campus, Dr. Ann Castelfranco and Prof. Dr. Dan Hartline, at PBRC, are both experts in STG modeling. Students interested in this subject can profit from collaboration with Dr. Castelfranco and from interfacing to the mathbio program.

3. Foundations of unsupervised learning

This problem
addresses open problems at the theoretical foundation of unsupervised
learning.

Applications:

1. Astronomy

I am looking for a student who is interested in applying these methods to astronomical data.

2. Computer vision

Image segmentation is an important step
in computer vision. This project deals with new approaches to
segmentation.

3. Bioinformatics

Cluster analysis is frequently used to
analyze gene expression array data. However, existing methods have
crucial shortcomings. In this project, we will address some of them.

4. Movement primitives

Much work has gone into attempts of
trying to decompose human movements such as to be able to better model
them. This has applications for example in animation. We will take a
new approach to the problem in this project.