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Research

I recognize that interdisciplinary research offers greater opportunities for scientific and technological breakthroughs since the world is diverse and statistics/biostatistics alone can not address all problems in isolation.

My research interest lies in the intersection of machine learning and statistics. My general focus is to apply machine learning techniques to improve traditional statistical methodologies/overcomes the existing bottleneck of statistical procedures and introduce statistical ideas in machine learning. Currently, I’m focused on developing non computationally intensive matrix factorisation techniques and apply these techniques to solve practical problems. Additionally, I’m also interested in protecting each individual’s privacy when analyzing sample data, especially the differential privacy technique where statistical random errors are added when sharing information about a dataset. I have actively collaborated with professors in both statistics and electrical engineering as well as industry, such as the Huawei company in Canada and Hong Kong.

My research area also includes Reinforcement Learning, High Dimensional Data, Bridging Study, Functional Data Analysis, Biostatistics, Semiparametric Modelling, Causal Inference, Rank Estimation.