Dr. Laura Brown
Assistant Professor, Computer Science
research is centered broadly on the application and design of
methods in artificial intelligence and machine learning. This
work spans from the theoretical design of algorithms for
feature selection and learning Bayesian networks, to the
application of methods across domains including clinical
healthcare, biomedicine, power distribution networks, electric
microgrids, and computer systems research.
Dr. Brown's group is investigating parallel machine learning algorithms on wide variety of datasets. This project seeks to better characterize and understand the algorithms. The evaluation looks beyond the standard machine learning metrics of model quality, such as, accuracy, AUC, precision, recall, etc. and incorporates metrics parallel computing community, such as, speed-up.
Recently, Dr. Brown and Dr. Zhenlin Wang have been examining the use of machine learning methods in computer systems research. Specifically, the work looks to better understand, model, and predict performance of applications in heterogeneous (different computer architectures and hardware configurations) data centers. Preliminary work has verified that machine learning methods can be used to create a decision model to select memory virtualization approaches. The current research aims to model co-run interference, when two applications are run on a single chip, and predict this interference for new hardware settings.
For more information, please visit Dr. Brown's website.