I am a postdoc in the AMPLab at UC Berkeley where I am continuing work on large-scale systems for machine learning as well as the GraphLab project. As a graduate student I worked with Carlos Guestrin in the Machine Learning Department at Carnegie Mellon University (CMU). My research addresses the challenges of designing and building large-scale machine learning algorithms and systems. In particular, my thesis focuses on large-scale structured machine learning using probabilistic graphical models that are capable of reasoning about billions of related random variables. The resulting algorithms and systems have achieved state-of-the-art performance in tasks ranging from predicting ad preferences in social networks to solving complex protein modeling tasks. As part of my thesis work we created GraphLab , a framework that dramatically simplifies the design and implementation of high-performance large-scale machine learning systems.
I am a recipient of the ATT Labs Graduate Research Fellowship and the National Science Foundation Graduate Research Fellowship. Some of my work is also supported by the ONR Young Investigator Program grant N00014-08-1-0752, the ARO under MURI W911NF0810242, and the ONR PECASE-N00014-10-1-0672.
I completed my BS in computer science at the California Institute of Technology (Caltech) and my MS in Machine Learning at Carnegie Mellon University. As part of my Masters work I developed non-parametric Bayesian models to estimate wireless signal quality in sensor networks.