Research Interest

Research Areas

My research focuses on developing statistical and machine learning methods for analyzing high-dimensional biomedical data, with applications to multi-omics integration, medical imaging, and longitudinal studies. I work on functional data analysis, supervised tensor decomposition, and joint modeling approaches to address complex problems such as cancer detection from histology images, dynamic prediction of dementia risk, and biomarker discovery in microbiome and single-cell RNA sequencing studies. A central goal of my work is to create reproducible, interpretable methods that advance translational research and precision medicine.

Manuscript in Progress

  • Alam, M.S., and Luo, S. (2025+). Dynamic Prediction of Dementia Risk in Alzheimer’s Disease Integrating Longitudinal High-dimensional Multi-omics Data. (under preparation)

  • Alam, M.S., Choi, D., and Luo, S. (2025+). Generalized Multivariate Functional Mixed Model for Joint Modeling of Item Response and Time to Event Data: A Dynamic Prediction Framework (under preparation)

  • Alam, M.S., and Luo, S. (2025+). Joint modeling of high-dimensional longitudinal and time-to-event data using supervised low-rank tensor decomposition. (under review at Biostatistics)

  • Alam, M.S., Choi, D., Koner, S., and Luo, S. (2025+). Dynamic prediction using functional latent trait joint models for multivariate longitudinal outcomes: An application to Parkinson’s disease. (accepted in Statistics in Medicine)

  • Alam, M. S. and Staicu, A. M. Classification using repeated and spatially indexed multivariate functional data: an application to prostate cancer identification on H & E stained histopathology image. (under preparation for Journal of Royal Statistical Society Series C)

  • Alam, M. S., Staicu, A. M., and Pixu, S. Supervised low-rank approximation of high-dimensional multivariate functional data via tensor decomposition. (under review in Annals of Applied Statistics.)

Published papers

  • Guo, Y., Zou, H., Alam, M.S., and Luo, S. (2025). Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer’s Disease. Statistics in Medicine. ().

  • Alam, M. S., and Staicu, A. M. \((2024)\). Modeling longitudinal skewed functional data. Biometrics, 80(4).

  • Lipi N., Alam, M. S., and Hossain, S. S. \((2020)\). A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data. Austrian Journal of Statistics, \(50(4), 36-52\).

  • Alam, M. S., and Paul S. \((2020)\). A Comparative Analysis of Clustering Algorithms to Identify the Homogeneous Rainfall Gauge Stations of Bangladesh. Journal of Applied Statistics, \(47(8)\), \(1460-1481\).

  • Alam, M. S., Hossain, S. S., and Sheela, F. F. \((2019)\). Spatial Smoothing of Low Birth Weight Rate in Bangladesh using Bayesian Hierarchical Model. Journal of Applied Statistics, \(46(10)\), \(18870-1885\).

  • Hossain S. S., and Alam, M. S., \((2017)\). MISSPECIFICATION EFFECT IN BOOTSTRAP VARIANCE ESTIMATION FOR ESTIMATORS OF THE POPULATION MEAN, Far East Journal of Theoretical Statistics, \(53(1)\), \(1-14\).

  • Ahmed M.K., Alam, M. S., Yousuf, A. H. M., and Islam, M. M. \((2016)\). A long-term trend in precipitation of different spatial regions of Bangladesh and its teleconnections with El Ni\(\tilde{n}\)o/Southern Oscillation and Indian Ocean Dipole. Theoretical and Applied Climatology, \(129 (1-2)\), \(473-486\).

  • Alam, M. S., and Hossain, S. S. \((2016)\). A Geostatistical Approach to Predict the Average Annual Rainfall of Bangladesh. Journal of Data Science, \(14(1)\), \(149-165\).