Shariq Mohammed

Postdoctoral Research Fellow

Precision Health Scholar

University of Michigan-Ann Arbor


I am a postdoctoral research fellow in the Departments of Biostatistics and Computational Medicine and Bioinformatics at The University of Michigan-Ann Arbor. I am also a Precision Health Scholar at the University of Michigan Precision Health. I am jointly mentored by Dr. Veerabhadran Baladandayuthapani and Dr. Arvind Rao. I obtained my PhD in Statistics from University of Connecticut under the supervision of Dr. Dipak Dey and Dr. Yuping Zhang.

My research interests include Bayesian modeling, variable selection, geometric functional data analysis and applications to biomedical imaging data. My current research is focused on building statistical methods to address relevant questions in the context of cancer, by integrating data from multiple sources such as radiological-imaging and genomics.


  • Bayesian Modeling
  • Variable Selection
  • Cancer Imaging Genomics
  • Medical Imaging Analyses
  • Precision Health


  • PhD in Statistics, 2018

    University of Connecticut

  • MS in Statistics, 2017

    University of Connecticut

  • MSc in Applications of Mathematics, 2014

    Chennai Mathematical Institute

  • B.Math.(Hons.), 2012

    Indian Statistical Institute



Precision Health Scholar

University of Michigan Precison Health

Sep 2019 – Present Ann Arbor, MI

Postdoctoral Research Fellow

University of Michigan

Sep 2018 – Present Ann Arbor, MI


University of Connecticut

Jun 2017 – Dec 2017 Storrs, CT

Research Assistant

The Travelers Companies and University of Connecticut

Aug 2016 – Aug 2018 Hartford/Storrs, CT



Bayesian 2D functional analysis in magnetic resonance imaging for gliomas

We are working on building modeling approaches for the gray-level co-occurrence matrices obtained from magnetic resonance imaging …

Applications of geometric functional data analysis in medical imaging

We are working on approaches to build classification algorithms for imaging data from Ophthalmology to assess retinal changes in …

Radiogenomic analysis in gliomas

We are building Bayesian variable selection approaches to identify associations between molecular characteristics and imaging …

Spatial modeling of large insurance claims and occurrence data

We are working on building efficient spatial risk-quantification models in insurance claims data. We are also building graphical …



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

The Canadian Journal of Statistics (2020) (Just Accepted). Abstract: We present a scalable Bayesian modeling approach for identifying …

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

Statistical Analysis and Data Mining: The ASA Data Science Journal (2020). Abstract: With the advent of modern technologies, it is …

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

Abstract: Identifying direct links between gene pathways and clinical endpoints for highly fatal diseases such as cancer is a …

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), 68(5), (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, 5(1), (2019). Abstract: Background and Aim: We aim to build a classifier to distinguish …

Spatial Tweedie exponential dispersion models

Abstract: This paper proposes a general modeling framework that allows for uncertainty quantification at the individual covariate level …

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 …


Regional Contact Networks and the Pandemic Spread of COVID-19 in India

Predictions to enable State-level Surge Preparedness in moving from Containment to Mitigation.

The medium.com article can be found here. To explore the results under different scenarios for the spread of infection, we have developed an R Shiny app which can be accessed here.

Recent Talks

  • Understanding COVID-19 Dynamics via Individual-level Temporal and Network Modeling: Lessons from India and China (joint presentation with Dr. Zhenke Wu, Rupam Bhattacharyya and Dr. Veerabhadran Baladandayuthapani)
    • MIDAS COVID-19 Special Seminar Series, University of Michigan-Ann Arbor. June 2020
  • Integrative Statistical Modeling Approaches for Imaging-Genomics Data
    • Precision Health Seminar (Pharmacy 217), University of Michigan-Ann Arbor. March 2020
    • Tools and Technology Seminar, University of Michigan-Ann Arbor. March 2020

See a list of all the talks here.



Computational Biotatistics and Survival Analysis

At Tata Memorial Centre Advanced Centre for Treatment, Research and Education in Cancer, India.

Data Visualization with R Shiny

As part of Statistical Consulting Services at University of Connecticut.

STAT 3025 - Statistical Methods (Calculus Level II)

During fall session in the Department of Statistics at University of Connecticut.

STAT 3025 - Statistical Methods (Calculus Level II)

Five-week course during summer session in the Department of Statistics at University of Connecticut.


  • shariqm at umich dot edu
  • 100 Washtenaw Ave (Palmer Commons Building), Ann Arbor, MI 48109