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Shariq Mohammed

Postdoctoral Research Fellow

Precision Health Scholar

University of Michigan-Ann Arbor

Biography

I am a postdoctoral research fellow in the Department of Biostatistics and Department of 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 inference, variable selection, geometric functional data analysis and applications in neuro- and cancer-imaging. My current research aims to build statistical methods to address relevant questions in the context of cancer, by integrating data from multiple sources such as radiological-imaging and genomics.

Interests

  • Bayesian Inference
  • Variable Selection
  • Cancer Imaging Genomics
  • Precision Health

Education

  • 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

Experience

 
 
 
 
 

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
 
 
 
 
 

Instructor

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

Research

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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 …

Bayesian variable selection in electroencephalography data

Working on building classification models for high-dimensional electroencephalography (EEG) data. We employ Bayesian variable selection …

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 …

Publications

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

Because of the immense technological advances, very often we encounter data in high dimensions. Any set of measurements taken at multiple time points for multiple subjects leads to data of more than two dimensions (a matrix of covariates for each subject). We present a Bayesian variable‐selection method to identify the active regions in the brain as a response to a certain stimulus. We build binary classification models of subject level responses by using binary regression with Gaussian models on the latent variables.

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

Background and Aim: We aim to build a classifier to distinguish between malaria-infected red blood cells (RBCs) and healthy cells using the two-dimensional (2D) microscopic images of RBCs. We demonstrate the process of cell segmentation and feature extraction from the 2D images. Methods and Materials: We describe an approach to address the problem using mixture discriminant analysis (MDA) on the 2D image profiles of the RBCs. The extracted features are used with Gaussian MDA to distinguish between healthy and malaria infected cells.

A dynamical systems approach to systemic risk in a financial network

The insolvency of a financial entity such as a bank can trigger a sequence of defaults in a network of financial entities interconnected through mutual financial obligations, thus posing a systemic risk to all the financial entities that make up the network. This paper studies the well-known Eisenberg-Noe model for systemic risk from a dynamical systems perspective. In particular, we model the sequence of defaults in the form of a dynamical system, and provide results on its stability and asymptotic behavior.

Teaching

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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.

Contact

  • shariqm at umich dot edu
  • 100 Washtenaw Ave (Palmer Commons Building), Ann Arbor, MI 48103