Shariq Mohammed
Profiles

Shariq Mohammed

Assistant Professor, Biostatistics - Boston University School of Public Health

(617) 358-2518
shariqm@bu.edu
Crosstown Center
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Biography

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.

Education

  • University of Connecticut, PhD Field of Study: Statistics
  • University of Connecticut, MS Field of Study: Statistics
  • Chennai Mathematical Institute, MSc Field of Study: Applied Mathematics
  • Indian Statistical Institute, BA Field of Study: Mathematics

Websites

Classes Taught

  • SPHBS755

Publications

  • Published on 10/30/2023

    Thierry Chekouo, Francesco C. Stingo, Shariq Mohammed, Arvind Rao, Veerabhadran Baladandayuthapani . A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants. Annals of Applied Statistics. 2023; 4(17):3013-3034.

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  • Published on 9/12/2023

    Mohammed S, Kurtek S, Bharath K, Rao A, Baladandayuthapani V. Tumor radiogenomics in gliomas with Bayesian layered variable selection. Med Image Anal. 2023 Dec; 90:102964. PMID: 37797481.

     Read At: PubMed
  • Published on 8/2/2023

    Romano MF, Zhou X, Balachandra AR, Jadick MF, Qiu S, Nijhawan DA, Joshi PS, Mohammad S, Lee PH, Smith MJ, Paul AB, Mian AZ, Small JE, Chin SP, Au R, Kolachalama VB. Deep learning for risk-based stratification of cognitively impaired individuals. iScience. 2023 Sep 15; 26(9):107522. PMID: 37646016.

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  • Published on 7/1/2023

    Warner E, Lee J, Krishnan S, Wang N, Mohammed S, Srinivasan A, Bapuraj J, Rao A. Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI. Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul; 2023:1-4. PMID: 38083692.

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  • Published on 6/19/2023

    Halder, A., Mohammed, S., Dey, D.K. Bayesian variable selection in double generalized linear Tweedie spatial process models. New England Journal of Statistics and Data Science. 2023; 2(1):187-199.

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  • Published on 10/1/2022

    Halder, A., Mohammed, S., Chen, K. and Dey D.K. 2022 Proceedings of International E-Conference on Mathematical and Statistical Sciences: A Selçuk Meeting. Spatial risk estimation in Tweedie double generalized linear models. 2022; 62-91.

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  • Published on 9/12/2022

    Bhattachayya, R., Banerjee, S., Mohammed, S. and Baladandayuthapani, V. Spatial network-based modeling of COVID-19 dynamics: Early pandemic spread in India. Journal of the Indian Statistical Association. 2022.
  • Published on 9/1/2022

    Sevcan Turk, Nicholas C. Wang, Omer Kitis, Shariq Mohammed, Tianwen Ma, Remy Lobo, John Kim, Sandra Camelo-Piragua, Timothy D. Johnson, Michelle M. Kim, Larry Junck, Toshio Moritani, Ashok Srinivasan, Arvind Rao, Jayapalli R.Bapuraj. Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomas. Neuroscience Informatics. 2022; 2(3).

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  • Published on 8/31/2022

    Panigrahi S, Mohammed S, Rao A, Baladandayuthapani V. Integrative Bayesian models using Post-selective inference: A case study in radiogenomics. Biometrics. 2023 Sep; 79(3):1801-1813. PMID: 35973786.

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  • Published on 5/31/2022

    Qin A, Lima F, Bell S, Kalemkerian GP, Schneider BJ, Ramnath N, Lew M, Krishnan S, Mohammed S, Rao A, Frankel TL. Cellular engagement and interaction in the tumor microenvironment predict non-response to PD-1/PD-L1 inhibitors in metastatic non-small cell lung cancer. Sci Rep. 2022 May 31; 12(1):9054. PMID: 35641540.

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