Publications

Underlined names are trainees I advised.
† marks equal contribution.
* marks the (co-)corresponding author.

Journal articles

2026

  1. Modeling bounded well-being indices using Bayesian double generalized beta regression with spatial and temporal borrowingJain, A., LaValley, M., Dukes, K.A., and Mohammed, S.Spatial Statistics, 76, 101047 DOIAward: Eastern North American Region (ENAR) Distinguished Student Paper Award (2026)
  2. Characterizing U.S. community well-being indices: a methodological synthesis and measurement systems perspectiveAlperen, J.K., LaValley, M., Ni, P., Tripp, T., Emig, D., Patil, P., Mohammed, S., Jain, A., Rickles, M., Colyer, E., Davis, B., Sullivan, L.M., Acker, M., and Dukes, K.A.Population Health Metrics DOI
  3. Bayesian feature extraction using Gaussian and diffused-gamma priors for high-dimensional spatio-temporal dataFrady, G., Dey, D.K., and Mohammed, S.*Statistics and Applications, 24(2) (in press). Special issue on Recent Advances in Bayesian Statistics and Machine Learning arXiv
  4. Adapting back-calculation methods to estimate the incidence of tuberculosisShapiro, A.N., Mohammed, S., Horsburgh, C.R., Jenkins, H.E., and White, L.F.Epidemiology, 37(2), 220–227 DOI

2025

  1. A national growth mixture modeling analysis of county-level COVID-19 incidence rate trajectories and health inequities during three successive pandemic waves in 2020Dukes, K., Ni, P., Alperen, J., Cesare, N., LaValley, M., Tripp, T., Lane, K., Mohammed, S., Patil, P., Emig, D., Winter, M., Davis, B., Wang, B., Jain, A., Acker, M., and Rickles, M.Scientific Reports, 15(1), 41272 DOI
  2. Later midline shift is associated with better post-hospitalization discharge status after large middle cerebral artery strokeSong, J.J., Stafford, R.A., Pohlmann, J.E., Kim, I.S.Y., Cheekati, M., Dennison, S., Brush, B., Chatzidakis, S., Huang, Q., Smirnakis, S.M., Gilmore, E.J., Mohammed, S., Abdalkader, M., Benjamin, E.J., Dupuis, J., Greer, D.M., and Ong, C.J.Scientific Reports, 15(1), 11738 DOI
  3. Early Pupil Abnormality Frequency Predicts Poor Outcomes and Enhances International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) Model Prognostication in Traumatic Brain InjuryVeerapaneni, D., Sakthiyendran, N.A., Du, Y., Mallinger, L.A., Reinert, A., Kim, S.Y., Nguyen, C., Daneshmand, A., Abdalkader, M., Mohammed, S., Dupuis, J., Sheth, K., Gilmore, E.J., Greer, D., and Ong, C.J.Critical Care Explorations, 7(5), e1257 DOI
  4. Quantitative Pupillometry predicts neurologic deterioration in patients with large middle cerebral artery strokeDu, Y., Pohlmann, J., Chatzidakis, S., Brush, B., Mallinger, L., Stafford, R., Cervantes-Arslanian, A., Benjamin, E., Gilmore, E., Dupuis, J., Greer, D., Smirnakis, S., Mohammed, S., and Ong, C.Annals of Neurology, 97(5), 930–941 DOI
  5. Association Between Tourniquet Use and Patient-Reported Outcomes Following Total Knee Arthroplasty: A Multicenter ComparisonGibbs, B., Sniderman, J., Mohammed, S., Kain, M., Freccero, D., and Abdeen, A., on behalf of the PEPPER InvestigatorsThe Journal of Bone and Joint Surgery, 107(9), 976–984 DOI

2024

  1. Modeling health and well-being measures using ZIP Code spatial neighborhood patternsJain, A., LaValley, M., Dukes, K., Lane, K., Winter, M., Spangler, K.R., Cesare, N., Wang, B., Rickles, M., and Mohammed, S.*Scientific Reports, 14(1), 9180 DOI
  2. Statistical Analysis of Quantitative Cancer Imaging DataMohammed, S., Masotti, M., Osher, N., Acharyya, S., and Baladandayuthapani, V.Statistics and Data Science in Imaging, 1(1), 2405348 DOI

2023

  1. Tumor radiogenomics with Bayesian layered variable selectionMohammed, S.*, Kurtek, S., Bharath, K., Rao, A., and Baladandayuthapani, V.Medical Image Analysis, 90, 102964 DOI
  2. Deep learning for risk-based stratification of cognitively impaired individualsRomano, M.F., Zhou, X., Balachandra, A.R., Jadick, M.F., Qiu, S., Nijhawan, D.A., Joshi, P.S., Mohammad, S., Lee, P.H., Smith, M.J., Paul, A.B., Mian, A.Z., Small, J.E., Chin, S.P., Au, R., and Kolachalama, V.B.iScience, 26(9), 107522 DOI
  3. Bayesian variable selection in double generalized linear Tweedie spatial process modelsHalder, A.†, Mohammed, S.†, and Dey, D.K.New England Journal of Statistics and Data Science, 1(2), 187–199 DOI
  4. A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinantsChekouo, T., Stingo, F.C., Mohammed, S., Rao, A., and Baladandayuthapani, V.Annals of Applied Statistics, 17(4), 3013–3034 DOI
  5. Integrative Bayesian models using post-selective inference: A case study in radiogenomicsPanigrahi, S., Mohammed, S., Rao, A., and Baladandayuthapani, V.Biometrics, 79(3), 1801–1813 DOI

2022

  1. Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomasTurk, S., Wang, N.C., Kitis, O., Mohammed, S., Ma, T., Lobo, R., Kim, J., Camelo-Piragua, S., Johnson, T.D., Kim, M.M., Junck, L., Moritani, T., Srinivasan, A., Rao, A., and Bapuraj, J.R.Neuroscience Informatics, 2(3), 100088 DOI
  2. Cellular engagement and interaction in the tumor microenvironment predict non-response to PD-1/PD-L1 inhibitors in metastatic non-small cell lung cancerQin, A., Lima, F., Bell, S., Kalemkerian, G.P., Schneider, B.J., Ramnath, N., Lew, M., Krishnan, S., Mohammed, S., Rao, A., and Frankel, T.L.Scientific Reports, 12(1), 9054 DOI
  3. Spatial network-based modeling of COVID-19 dynamics: Early pandemic spread in IndiaBhattacharyya, R., Banerjee, S., Mohammed, S., and Baladandayuthapani, V.Journal of the Indian Statistical Association, 60(1), 1–42 PDF
  4. GaWRDenMap: A quantitative framework to study the local variation in cell-cell interactions in pancreatic disease subtypesKrishnan, S.N.†, Mohammed, S.†, Frankel, T.L., and Rao, A.Scientific Reports, 12(1), 3708 DOI

2021

  1. Quantifying T2-FLAIR mismatch using geographically weighted regression and predicting molecular status in lower-grade gliomasMohammed, S., Ravikumar, V., Warner, E., Patel, S.H., Bakas, S., Rao, A., and Jain, R.American Journal of Neuroradiology, 43(1), 33–39 DOINominated: 2021 Lucien Levy Best Research Article Award, AJNR
  2. Spatial Tweedie exponential dispersion models: An application to insurance rate-makingHalder, A., Mohammed, S., Chen, K., and Dey, D.K.Scandinavian Actuarial Journal, 2021(10), 1017–1036 DOI
  3. RADIOHEAD: Radiogenomic analysis incorporating tumor heterogeneity in imaging through densitiesMohammed, S., Bharath, K., Kurtek, S., Rao, A., and Baladandayuthapani, V.Annals of Applied Statistics, 15(4), 1808–1830 DOI
  4. Scalable spatio-temporal Bayesian analysis of high-dimensional electroencephalography dataMohammed, S.* and Dey, D.K.Canadian Journal of Statistics, 49(1), 107–128 DOI

2020

  1. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learningLee, J., Wang, N., Turk, S., Mohammed, S., Lobo, R., Kim, J., Liao, E., Camelo-Piragua, S., Kim, M., Junck, L., Bapuraj, J., Srinivasan, A., and Rao, A.Scientific Reports, 10(1), 20331 DOI
  2. Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomographyMohammed, S.*, Li, T., Chen, X.D., Warner, E., Shankar, A., Abalem, M.F., Jayasundera, T., Gardner, T.W., and Rao, A.Scientific Reports, 10(1), 15937 DOI
  3. A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomasChekouo, T., Mohammed, S.*, and Rao, A.NeuroImage: Clinical, 28, 102437 DOI
  4. Classification of high-dimensional electroencephalography data with location selection using structured spike-and-slab priorMohammed, S.*, Dey, D.K., and Zhang, Y.Statistical Analysis and Data Mining: The ASA Data Science Journal, 13(5), 465–481 DOIInvited: Statistical Analysis and Data Mining (SADM) Best Paper Session, Joint Statistical Meetings (JSM) 2022
  5. Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: Data science call to armsRay, D., Salvatore, M., Bhattacharyya, R., Wang, L., Du, J., Mohammed, S., Purkayastha, S., Halder, A., Rix, A., Barker, D., Kleinsasser, M., Zhou, Y., Bose, D., Song, P., Banerjee, M., Baladandayuthapani, V., Ghosh, P., and Mukherjee, B.Harvard Data Science Review, Special Issue 1 DOI

2019

  1. Bayesian variable selection using spike‐and‐slab priors with application to high dimensional electroencephalography data by local modellingMohammed, S.*, Dey, D.K., and Zhang, Y.Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(5), 1305–1326 DOI
  2. Assessing malaria using neutral-zone classifiers with mixture discriminant analysis on 2D images of red blood cellsMohammed, S. and Dey, D.K.Journal of Biostatistics and Epidemiology, 5(1), 1–11 DOI

Book chapter

2020

  1. Biomedical applications of geometric functional data analysisMatuk, J., Mohammed, S., Kurtek, S., and Bharath, K.In Handbook of Variational Methods for Nonlinear Geometric Data, 675–701. Springer, Cham DOI

Conference proceedings

2023

  1. Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRIWarner, E., Lee, J., Krishnan, S., Wang, N., Mohammed, S., Srinivasan, A., Bapuraj, J., and Rao, A.In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4 DOI

2022

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

2016

  1. A dynamical systems approach to systemic risk in a financial networkBhat, S.P., Kumar, M.U., and Mohammed, S.In 2016 Indian Control Conference (ICC), 377–384. IEEE DOI