Shariq Mohammed

Assistant Professor of Biostatistics

Rafik B. Hariri Junior Faculty Fellow

Boston University


I am an Assistant Professor in the Department of Biostatistics at Boston University (BU) School of Public Health. I am also a Rafik B. Hariri Junior Faculty Fellow at the Rafik B. Hariri Institute for Computing and Computational Science & Engineering at BU.

I was a postdoctoral research fellow in the Departments of Biostatistics, and Computational Medicine and Bioinformatics, and a Precision Health Scholar at The University of Michigan-Ann Arbor. I obtained my PhD in Statistics from University of Connecticut.

My research interests include Bayesian modeling, variable selection, geometric functional data analysis, spatial statistics and applications to complex-structured biomedical data. My current research is focused on building statistical methods to address relevant questions in different disease contexts, by integrating complex-structured data (imaging, spatial-genomic, geospatial and digital data) from multiple platforms.


  • Bayesian Modeling
  • Variable Selection
  • Functional Data Analysis
  • Spatial Statistics
  • Medical Imaging Analysis
  • Digital Data
  • Cancer- and Neuro-Imaging


  • Ph.D. in Statistics, 2018

    University of Connecticut

  • M.S. in Statistics, 2017

    University of Connecticut

  • M.Sc. in Applications of Mathematics, 2014

    Chennai Mathematical Institute

  • B.Math.(Hons.), 2012

    Indian Statistical Institute



Imaging and complex-structured data applications

We develop statistical models to:

  1. identify associations between complex-structured data (imaging, spatial-genomic, geospatial, digital) from multi-platform data sources,

  2. predict clinical characteristics of interest by integrating biomarkers generated from complex-structured data, and

  3. understand the biological relevance and implications of our findings.

Our methods are developed in the context of various diseases such as cancer and neurodegenerative (Alzheimer’s) diseases.

Statistical methods for the analysis of complex-structured biomedical data

We develop innovative statistical methodology that leverages structural and biological information in the data. To address the challenges of handling complex-structured data, we develop and employ methods at the interface of hierarchical Bayesian modeling, variable selection, geometric/functional data analysis and spatial statistics.



Integrative Bayesian models using post-selective inference: a case study in radiogenomics

Biometrics (Just Accepted) Abstract: Identifying direct links between gene pathways and clinical endpoints for highly fatal diseases …

Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India

Journal of the Indian Statistical Association (Just Accepted) Abstract: Modeling the dynamics of COVID-19 pandemic spread is a …

GaWRDenMap: A quantitative framework to study the local variation in cell-cell interactions in pancreatic disease subtypes

Scientific Reports (2022) Abstract: Spatial pattern modelling concepts are being increasingly used in capturing disease heterogeneity. …

Tumor radiogenomics with Bayesian layered variable selection

Abstract: We propose a statistical framework to integrate radiological magnetic resonance imaging (MRI) and genomic data to identify …

Spatial Tweedie exponential dispersion models: An application to insurance rate-making

Scandinavian Actuarial Journal (2021). Abstract: In this paper we propose a statistical modeling framework that contributes to …

RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imagine through densities

Annals of Applied Statistics (2021). Abstract: Recent technological advancements have enabled detailed investigation of associations …

Scalable spatio-temporal Bayesian analysis of high-dimensional electroencephalography data

Canadian Journal of Statistics (2021). Abstract: We present a scalable Bayesian modeling approach for identifying brain regions that …

A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas

NeuroImage: Clinical (2020). Abstract: In cancer radiomics, textural features evaluated from image intensity-derived gray-level …

Classification of high-dimensional electroencephalography data with location selection using structured spike-and-slab prior

Invited for Statistical Analysis and Data Mining Best Paper Session at Joint Statistical Meetings 2022. JSM 2022 Program Statistical …

Biomedical applications of geometric functional data analysis

Handbook of Variational Methods for Nonlinear Geometric Data - Springer (2020). Abstract: In this chapter, we describe several …

Spatial risk estimation in Tweedie compound Poisson double generalized linear models

Abstract: Tweedie exponential dispersion family constitutes a fairly rich sub-class of the celebrated exponential family. In …

Bayesian variable selection using spike-and-slab priors with application to high dimensional electroencephalography data by local modelling

Journal of the Royal Statistical Society: Series C (Applied Statistics) (2019). Abstract: Because of the immense technological …

Assessing malaria using neutral zone classifiers with mixture discriminant analysis on 2D images of red blood cells

Journal of Biostatistics and Epidemiology (2019). Abstract: Background and Aim: We aim to build a classifier to distinguish between …

A dynamical systems approach to systemic risk in a financial network

In 2016 Indian Control Conference (ICC). Abstract: The insolvency of a financial entity such as a bank can trigger a sequence of …


  • shariqm at bu dot edu
  • Crosstown Center (CT-303), 801 Massachusetts Ave, Boston, MA 02118