Jiahua Chen current holds a Canada Research Chair, Tier I, in the department of statistics at University of British Columbia.

His research interests cover many specialized areas of statistics including empirical likelihood, variable selection, finite mixture models, hidden Markov model, sample survey and statistical genetics.

His work on EM-test in the context of finite mixture models are particularly relevant to statistical genetics. His most recent paper in this respect is to test the presence of imprinting when the imprint information is contained in a mixture model.

He developed an extended Bayesian information criterion used for variable selection when the number of variables in a regression setting is comparable or even far exceeds the sample size. It has found applications in genomic data analysis and in graphic models.

He proposed to introduce a pseudo observation into the empirical likelihood. The resulting inference retains much of the original properties of the empirical likelihood. At the same time, the technique completely solves the empty set problem which can be an obstacle in some specialized applications.

JIahua Chen is interested in all scientific problems that can be summarized by mathematical language. He relies on his instinct ion based on logic and his broad mathematics knowledge in his research adventures.