Please ensure Javascript is enabled for purposes of website accessibility Dr. Tharindu P. De Alwis | Faculty | University of West Florida
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Dr. Tharindu P. De Alwis

Biography

Dr. Tharindu De Alwis, an Assistant Professor in the Department of Mathematics and Statistics, has a Ph. D in Mathematics concentration in Statistics from Southern Illinois University Carbondale. Before joining UWF, Dr. De Alwis worked as a post-doctoral scholar in the Department of Mathematical Sciences at Worcester Polytechnical Institute in Worcester, Massachusetts.

His research focuses on Dimension reduction in multivariate time series and spatial-temporal observations. Dimensionality reduction is a fundamental technique in the field of data analysis and machine learning, aimed at reducing the complexity of high-dimensional datasets while preserving essential information. His contribution involves developing novel methods and applications for dimensional reduction, addressing both theoretical and practical challenges. Additionally, his academic interests extend to deep learning and machine learning methods, including artificial neural networks (ANNs) and convolution neural networks (CNNs) models.

Dr. De Alwis has published articles in peer-reviewed journals such as Statistical Methods & Applications and Reliability Engineering & System Safety. His work has also been featured in the Comprehensive R Archive Network (CRAN). Additionally, he has presented his research at multiple national academic conferences across the USA.

Degrees & Institutions

  • Ph.D., Mathematics, Department of Mathematics and Statistics, Southern Illinois University Carbondale
  • M.S., Mathematics, Department of Mathematics and Statistics, Southern Illinois University Carbondale
  • B.Sc., Statistics and Operations Research, University of Peradeniya (Sri Lanka)

Research

High-dimensional Multivariate Statistics, Envelope Methods, Dimension Reduction, Neural Network, Spatial-Temporal Methods, Sufficient Dimension Reduction (SDR), Deep Learning and Machine Learning for Time Series and Spatial-Temporal Data Analysis.

Current Courses

  • STA 6856 Time Series Analysis
  • STA 6507 Nonparametric Statistics
  • MAC1114 Trigonometry

Classes Taught

  • STA4051 Nonparametric Statistics
  • STA2023 Elements of Statistics
  • STA4173 Biostatistics
  • MAS3105 Linear Algebra
  • MAC2233 Calc with Business Applications
  • MAC1147 Precalculus with Trigonometry
  • MAP2302 Differential Equations

Special Interests

Dr. De Alwis enjoys playing cricket and guitar.

Publications

  1. Samadi S. Y. & De Alwis T. P. (2026). Envelope Matrix Autoregressive Models. Journal
    of Business & Economic Statistics, 44(2), 397–412.
    https://doi.org/10.1080/07350015.2025.2537404.

  2. De Alwis, T. P., Samadi, S. Y. & Ashofteh, A. (2026). Convolution transformation for
    sufficient dimension reduction in time series. Japanese Journal of Statistics and Data
    Science. https://doi.org/10.1007/s42081-026-00364-y.

  3. Yu E., Cao G., Khalil Y. F., Zou J., & De Alwis T. P. (2026). Enhancing FRACAS Through
    Natural Language Processing: An AI-Driven Approach to System Reliability Analysis.
    Journal of Research in Engineering and Computer Sciences.
    https://doi.org/10.63002/jrecs.404.1603.

  4. Samadi S. Y. & De Alwis T. P. (2025). Fourier methods for efficient sufficient dimension
    reduction in time series. Canadian Journal of Statistics e70027.
    https://doi.org/10.1002/cjs.70027.

  5. De Alwis T. P., S. Asadollahfardi G., Samadi Y., Fazeli R., & Yahyaei B. (2025). Artificial
    neural network modeling of self-compacting concrete mixed and cured with seawater for
    compressive strength and chloride penetration prediction. Journal of Structural Integrity
    and Maintenance, 10(4). https://doi.org/10.1080/24705314.2025.2558425.

  6. De Alwis T. P., & Samadi S. Y. (2024). Stacking-Based Deep Neural Network for
    Nonlinear Time Series Analysis. Journal of Statistical Methods and Applications (SMAP).
    https://doi.org/10.1007/s10260-024-00746-0.

  7. Grabill N., Wang S., Olayinka H., De Alwis T. P., Khalil Y. F, & Zou J., (2024). AI-
    augmented Reliability Predictions using Failure Modes, Effects, and Criticality Analysis
    for Industrial Applications. Journal of Reliability Engineering and System Safety.
    https://doi.org/10.1016/j.ress.2024.110308.

  8. De Alwis T. P. and Samadi S. Y. (2024). sdrt: An R Package to Estimate SDR in Time
    Series (2024). The Comprehensive R Archive Network (CRAN).
    https://CRAN.R-project.org/package=sdrt.

  9. De Alwis T. P., Samadi S. Y., and Weng J. (2021). itdr: An R Package of Integral
    Transformation Methods to Estimate SDR in Regression. The Comprehensive R Archive
    Network (CRAN). https://CRAN.R-project.org/package=itdr.