Chair and Associate Professor, Computer and Data Sciences
I have an active research portfolio focusing on three primary areas right now. I am a founding member of the Food Justice Research and Action Cluster (FJRAC). We actively tackle local food insecurity and promote food justice by engaging with community partners and applying our expertise in problem solving efforts in conjunction with their own. The cluster has published papers, run Food Justice Summit events, and exchanged data and resources with community partners.
Federated Machine Learning (FML) is a data modeling paradigm in which models are fit to datasets from multiple sources without the need for those sources to exchange their data, thus protecting the privacy of the subjects in the data. This technique is state of the art and being employed in the finance, education, and especially healthcare arenas. I work on studying and improving the technique with regard to its use in healthcare.
Finally, I am engaged in a multiyear initiative to tackle traffic congestion alleviation using genetic algorithms trained on massive traffic datasets. Fitting language models to the datasets allows for the mapping of roadmap featuresets to traffic patterns, thus determining through automated means how a particular urban traffic pattern will contribute to local traffic.