• Ann M. Hermundstad

    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

High dimensional data

We are designing computational tools to analyze and visualize high-dimensional datasets

+ LOW-DIMENSIONAL PROJECTIONS

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.

+ HIERARCHICAL CLUSTERING

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.