Zhou Lan

Instructor in Medicine; Investigator

Harvard Medical School, Department of Radiology
Center for Clinical Investigation, Brigham and Women's Hospital

Contact

Office Phone: 617-732-6467
Medical Research Building 208C
75 Francis Street, Boston MA 02115
Email: zhou.joe.lan@gmail.com, zlan@bwh.harvard.edu

Overview

Zhou (Joe) Lan is an early-career biostatistics investigator at Brigham and Women’s Hospital and Harvard Medical School. He works closely with investigators in the Department of Radiology and serves as a core member of Dr. Lauren J. O’Donnell’s lab within Laboratory of Mathematics in Imaging. Zhou earned his Ph.D. in Statistics from North Carolina State University under supervision of Dr. Brian J Reich, where he was honored with the Paige Plagge Award. His research focuses on the development of statistical methodologies for MRI data, with expertise in spatial statistics, Bayesian computing, matrix-variate analysis, and high-dimensional data analysis applied to neuroimaging. His work has been published in statistical journals (such as Technometrics, Biometrics, Bayesian Analysis, and Journal of the Royal Statistical Society: Series A), neuroimaging journals (Imaging Neuroscience, NeuroImage: Clinical), and data science and informatics journals (Data Science in Science, JAMIA Open).

Zhou is enthusiastic about collaboration. Please feel free to reach out if you’re interested in a conversation or potential collaboration.

Research

Innovative Statistical Methodologies for Diffusion MRI:

The structural connections within the brain’s white matter are essential for its function. Diffusion MRI tractography allows for the in-vivo reconstruction of white matter fiber pathways. Diffusion MRI poses significant challenges due to their high dimensionality, spatial correlation, and complex signal structures. My research focuses on developing and applying cutting-edge statistical and computational methods to unlock the full potential of such data. The motivating data includes HCP Young Adult - -Connectome, Adolescent Brain Cognitive Development (ABCD) Study, and Alzheimer's Disease Neuroimaging Initiative (ADNI). The key collaborators and mentors in this research avenue includes Lauren J. O'Donnell from Brigham and Women's Hospital at Harvard Medical School, Brian J Reich from Department of Statistics, North Carolina State University, Arkaprava Roy from Department of Biostatistics, University of Florida, and Zhengwu Zhang from Department of Statistics and Operations Research, UNC Chapel Hill.

Robust Statistical Methodologies and Theoretical Frameworks for Matrix-Valued Data:

I focus on developing robust statistical methodologies and theoretical frameworks tailored to the complexities of real-world biomedical and imaging data. My work emphasizes extending theoretical foundations to accommodate challenges such as high dimensionality, spatial correlation, and intricate dependency structures, ensuring the developed methods are both robust and generalizable. The key collaborators and mentors in this research avenue includes Arkaprava Roy from Department of Biostatistics, University of Florida.

Innovative Statistical Methodologies for Magnetic Resonance Spectroscopy (MRS):

MRS is a critical tool for exploring the neurometabolic underpinnings of brain function, providing unique insights into the biochemical composition of tissues. My research has focused on developing and applying advanced methodologies to enhance MRS data processing, analysis, and interpretation, particularly in the context of neurological disorders such as functional neurological disorders (FND). The key collaborators and mentors in this research avenue includes Alexander Lin from Center for Clinical Spectroscopy, Department of Radiology, Harvard Medical School, Brigham and Women's Hospital.

Epidemiological/Clinical Studies:

One house-keeping component of my research is epidemiological/clinical studies. This includes addressing challenges in data quality, integration, and predictive modeling to improve public health.

  • Lan, Z. and Turchin, A.*, 2023. Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis. JAMIA open, 6(4), p.ooad111.
  • Lan, Z. and Bao, L.*, 2024. Multivariate spatial modelling for predicting missing HIV prevalence rates among key populations. Journal of the Royal Statistical Society Series A: Statistics in Society, 187(2), pp.321-337.

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