STATISTICS WELCOMES NEW FACULTY
Two new faculty members will join the Statistics faculty this fall. Please join us in welcoming Dr. Raymond Ka Wai Wong and Dr. Debdeep Pati to the Department of Statistics Faculty.
Raymond Ka Wai Wong received a MS degree in Statistics in 2010 from the Chinese University of Hong Kong and a PhD in Statistics in 2014 from the University of California at Davis. He is the recipient of a Best Student Paper award from the International Indian Statistical Association and a Los Alamos Statistical Sciences Conference Grant.
Raymond’s research is mostly problem-driven and has its roots in both scientific and engineering applications. These problems arise from astronomy, brain imaging, computer experiment and recommender systems. Many of them involve modern data complications such as big data size, high dimensionality and manifold structures. He broadly tackles them with nonparametric and semi-parametric modeling, combined with efficient computational techniques.
Raymond Wong is a member of the American Statistical Association, the Institute of Mathematical Statistics and the International Chinese Statistical Association.
Debdeep Pati received a MS in Statistics in 2010 and PhD in Statistics in 2012 both from Duke University under the direction of David Dunson. Prior to that he earned a MS in Statistics with a specialization in Mathematical Statistics and Probability in 2008 from the Indian Statistical Institute, Kolkata. He received an honorable mention for the Leonard J. Savage Award for outstanding dissertation in Bayesian statistical theory and methods in 2013 from the International Society for Bayesian Analysis and a Distinguished Student Paper Award from the International Biometric Society (ENAR). He is an Associate Editor of Sankhya, Series A (Mathematical Statistics and Probability).
Debdeep’s research involves developing Bayesian methods for complex objects including high-dimensional sparse vectors, matrices, shapes of non-Euclidean objects and large graphs. He is also interested in studying Bayesian model selection consistency when the marginal likelihood is analytically intractable. Modeling the distributions of objects contained within images motivated some of his collaborative work, e.g., in applications of tumor tracking in targeted radiation therapy. More recently, he has become interested in building models for discovering communities in large networks and to predict cognition from connectomics data.
Debdeep Pati is a member of the American Statistical Association, International Biometric Society (Eastern North American Region), International Society for Bayesian Analysis, Institute of Mathematical Statistics as well as the International Indian Statistical Association.