About Shuhao Jiao Shuhao Jiao Postdoctoral Research Fellow, Statistics Biostatistics Shuhao Jiao is a Postdoctoral Fellow of the KAUST Biostatistics Research Group. Before joining KAUST, Dr. Jiao received his Ph.D. in Statistics in 2019 from University of California, Davis. He is interested in statistical learning of complicated neuroimage data. He addresses these problems using novel statistical theory and methods in functional data analysis and machine learning. Education 2019: Ph.D., Department of Statistics, University of California, Davis. 2014: BSc (Mathematics & Statistics), Shandong University, Jinan, China. Research Interest Functional data analysis, Time series Articles Related News August 2021 Beating the curse of dimensionality 1 min read · Wed, Aug 18 2021 News Environmental Statistics big data statistics By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the
Beating the curse of dimensionality 1 min read · Wed, Aug 18 2021 News Environmental Statistics big data statistics By scanning past data for both partial and complete matches to current observations, a KAUST-led research team has developed a prediction scheme that can more reliably forecast the future trajectory of environmental parameters. The collection of data at regular intervals over time is common in many fields but particularly so in environmental, transportation and biological research. Such data are used to monitor and record the current state and also to help predict what might come in the future. A typical approach is to look for previous patterns or trajectories in the data that match the
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