Mika Campbell
Program: Unspecified
Current advisor:
Undergraduate university: Spelman College
Research summary
I evaluated a new machine learning technique for visualizing and parametrizing interactions. Biologically, this may be useful in determining how a large collection of odorant receptors is segmented in chemical space to provide an organism with a broad yet specific assessment of molecular identity. As an initial use case, I evaluated how this low-dimensional embedding technique performs on standard machine learning data sets that involve relationships between two categories of objects, specifically movies/viewers ratings data sets, with ambitions to explore gene/cells and pre/post-synaptic connections in the future.
Graduate publications