Fei Jiang, PhD, MS
Associate Professor
Epidemiology & Biostatistics
School of Medicine

Dr. Jiang is an Associate Professor in biostatistics in the University of California, San Francisco. She has been an assistant professor in statistics for three years in the University of Hong Kong.

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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. In addition, 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. Furthermore, she has involved in establishing Master of Data Science and Artificial Intelligence program in the University of Hong Kong. As a researcher with solid statistical background, she is able to provide rigorous data driven tools to analyze wide range of biomedical data.

Awards

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  • Stroke and Vascular Neurology High Impact Paper Award, Stroke and Vascular Neurology Journal, 2018
  • ENAR Distinguished Student Paper Award, ENAR, 2013
  • Rice Graduate Student Fellowship, Rice University, 2010-2012

Education & Training

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  • Postdoctoral Fellow Biostatistics Harvard University 06/2016
  • Ph.D. Statistics Rice University 01/2014
  • M.S. Biostatistics The University of Texas at Houston 07/2010

Interests

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  • cardiovascular disease
  • brain functional connectivity
  • digital health
  • high dimensional data analysis
  • stroke
  • functional data analysis
  • brain mechanisms for speech
  • medical device signal processing
  • machine learning
  • observational study
  • Huntington's disease
  • EMR
  • measurement error
  • EHR
  • source localization
  • change point problem
  • neuroimage

Websites

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Grants and Projects

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  • Development of Dynamic Resting State Functional Connectivity Machine Learning Framework for Dementia, NIH, 2022-2027
  • Development of MEG/EEG platform for epilepsy surgery planning., Research resource program at UCSF, 2023-2024
  • The effect of acupuncture for insomnia in breast cancer patients undergoing chemotherapy: a randomized, sham-controlled trial, Research Grants Council of Hong Kong, 2019-2022
  • The response-aided clustering, multi-scale Bayesian change point detection, and error contaminated functional quantile regression with the applications to the BOSS (blood pressure and clinical outcome in TIA or ischemic stroke) data., Research Grants Council of Hong Kong, 2018-2021
  • Investigations on the censored quantile regression with time dependent covariates, B-spline measurement error estimation, and reflected non-local priors. Role: Principal Investigator, Research Grants Council of Hong Kong, 2017-2020
  • Combined Electroacupuncture and Auricular Acupuncture for Postoperative Pain after Abdominal Surgery for Gynecological Diseases: A Randomized Sham-controlled Trial, Research Grants Council of Hong Kong, 2016-2019

Publications (38)

Top publication keywords:
Bayes TheoremNeurodegenerative DiseasesOut-of-Hospital Cardiac ArrestMagnetoencephalographyFunctional NeuroimagingSample SizeUrologyZebrafishAlzheimer DiseaseResearch DesignAngiotensin-Converting Enzyme InhibitorsBrainCardiopulmonary ResuscitationMagnetic Resonance ImagingContraceptives, Oral

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