I am a third-year Ph.D. student in Computer Science at RPI. I am currently working on social choice (rank aggregation) problems with Professor Lirong Xia.
Zhibing Zhao is a Ph.D. student at the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). He received his master’s degree from the University of Connecticut and his bachelor’s degree from Tsinghua University, both in Electrical Engineering. His research focuses on efficient learning (statistical inference) of ranking models, in particular, the Random Utility Models (including the Plackett-Luce model) and their mixtures. His strength lies in variance characterization and complexity analysis of inference algorithms, and designing new algorithms that are both statistically and computationally efficient.
Rank aggregation problems have a wide range of applications, including presidential elections, yelp rating, college ranking, and so on. People (agents) have diverse preferences over the alternatives (presidential candidates, restaurants, etc.) even though there is a ground truth. Ranking models are used to model this uncertainty. My research focuses on efficient estimation of ground truth parameters of ranking models, mainly from the computational perspective. Specifically, I am interested in identifiability of a class of ranking models, the convergence rate of an algorithm, and the statistical accuracy.
I received my Master's degree in Electrical Engineering at University of Connecticut in 2014. I worked on ocean power generator design and real-time simulations.
I obtained my Bachelar's degree in Electrical Engineering from Tsinghua University. I worked on a cascade bi-directional DC/DC converter for microgrid applications.