One of the primary focus of our lab is to investigate an alternative computational paradigm that involves "humans-in-the-loop" for large-scale analytics problems. These problems arise at different stages in a traditional data science pipeline (e.g., data cleaning, query answering, ad-hoc data exploration, or predictive modeling), as well as from emerging applications.

We study optimization opportunities that come across because of this unique man-machine collaboration and address data management and computational challenges to enable large-scale analytics with humans-in-the-loop.  Our focus domains are social networks, healthcare, climate science, retail and business, and spatial data.

The research projects at BDaL are funded by the National Science Foundation, Office of Naval Research, National Institute of Health, and Microsoft Research.