Contributions to Science & Highlights
Dr. Jiang is an Associate Professor in biostatistics at the University of California, San Francisco. Her research interest lies in machine learning methods, high dimensional models, functional data analysis and their applications in analyzing neurological, image, genetics data, and in designing adaptive randomization clinical trials. Her efforts yield high quality statistical and computational publications, which focus on addressing practical problems in the medical domain. She has taught both undergraduate and graduate courses on the foundations of statistics and their applications in medical diagnosis, medical decision making, classification, prognostic prediction, and clinical trials. As a researcher with solid statistical background, she is able to provide rigorous data driven tools to analyze wide range of biomedical data.
Keywords: high dimensional data analysis, functional data analysis, machine learning, measurement error, stroke, EMR, EHR, Huntington's disease, cardiovascular disease, neuroimage, source localization, change point problem, medical device signal processing, digital health, observational study, brain functional connectivity, brain mechanisms for speech.
Research Focus: Statistical tensor regression models for intracranial aneurysm growth prediction, development of MEG/EEG platform for epilepsy surgery planning, dynamic resting state functional connectivity machine learning framework for dementia.