I lead a group at Janelia Research Campus. I apply methods from physics to the study of complex biological systems. To read more about the problems that I work on, check out the links above.
email: hermundstada at janelia dot hhmi dot org
LOW-DIMENSIONAL PROJECTIONS
CAN OBSCURE IMPORTANT FEATURES
OF HIGH-DIMENSIONAL DATA
WE DEVELOP TOOLS
FOR PROBING FEATURES
OF LOW-DIMENSIONAL
PROJECTIONS
NEURAL RESPONSES
TO ODOR MIXTURES
HAVE COMPLEX STRUCTURE
WE DEVELOP TOOLS
FOR INFERRING HIERARCHICAL
CLUSTERS OF COMBINATORIAL
MIXTURES
Much of our work involves deciphering patterns in high-dimensional distributions. We would often like to understand features of these distributions in low-dimensional subspaces, which depends on the specific projection used to reduce dimensionality. Furthermore, if the distributions have a high density of data points, direct visualization of such low-dimensional projections can be misleading because many points are overlapping, and a disproportionate amount of "visual weight" is dedicated to outliers.
To aid in the quantitative analysis of high-dimensional datasets, we are developing tools to better visualize the underlying features of these distributions. These techniques can be used to resolve density variations in low-dimensional projections, and they can be used to easily compare across different projections.
In some cases, we are interested in understanding categorical relationships in our datasets. For example, given recordings of olfactory cells responding to different odor mixtures, we are interested in understanding how relationships between mixtures are represented in neural firing patterns. Is the mixture A+B+C most similar to A+B, A+C, or B+C? And how do these submixtures relate to the original components A, B, and C? We are developing hierarchical clustering techniques for understanding such relationships across different categorical scales.