ACC Efforts
Speed and Reliability for Nonparametric Bayesian Clustering
Assistant Professor Tamara Broderick, MIT
The goal of this project is to probabilistically cluster extremely large data sets with guarantees on performance so as to rapidly assess unstructured packet capture data. We pre-process data to find a “Bayesian core set”—a small, weighted subset of the data—and then run standard algorithms on the core set rather than the full data.
How Bubbles Burst
Assistant Professor Lydia Bourouiba, MIT, Dr. William Lawrence, MIT LL
The dynamics of bubble formation and collapse to form aerosol droplets play a critical role in disease transmission and air contamination. The project combined theoretical and experimental approaches to examine the fundamental mechanisms that trigger water bubble bursts and to characterize the effects of contaminants on the burst in order to address issues of disease transmission and contaminated aerosol suspension and transport.