Full Professor in Mathematical Statistics with focus on Statistical Learning and Inference for Spatiotemporal Data, including fundamental research and applications in AI.
I am leading the research group on statistical learning and inference for spatiotemporal data.
We work on tackling theoretical data science problems and developing statistical learning methods for solving real-life problems, which originate from various application areas, including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry and hydrology, radiation oncology, spatial ecology, sports science, and transportation.
Regarding the statistical learning and inference studied: statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modelling, nonparametric density/intensity estimation and smoothing techniques, statistical inference for hidden Markov models and random fields, summary statistics for point processes, and wavelet theory applied to signal and image analys.
In terms of data analysis tools: intelligent data sampling using compressive sensing, large-scale environmental data model, multimodal image processing, tree growth models, and general modelling of biological populations in space and time.
I have been teaching mathematical statistics at all levels, from basic education to postgraduate education. The courses taught have been subjected to different target groups, which are students with majors varying from mathematics, statistics, to biology, engineering, and forestry. The languages that I used in teaching have been English, Swedish, or Chinese.
In latest years, I have primarily taught doctoral courses in Mathematical Statistics and Data Science.