Staff & Faculty Directory
Surani Mathara Arachchige Dona
Assistant Professor; Computer Science, College of Engineering & Computing Sciences
Education Credentials: Ph.D.
Expertise: Machine Learning, Statistical Learning, Bayesian Inference, NLP, Deep Learning, High Dimensional Data Analysis, Computational Statistics
Joined New York Tech: 2025
Surani Matharaarachchi is an assistant professor of data science at New York Institute of Technology-Vancouver, in British Columbia (Canada). She holds a Ph.D. in Statistics and Data Science from the University of Manitoba, where she received the award for Outstanding Research by a Ph.D. Student from the Department of Statistics. She also earned her M.Sc. in Statistics from the University of Manitoba and her B.Sc. in Statistics from the University of Sri Jayewardenepura, Sri Lanka.
Her research sits at the intersection of machine learning and statistical modeling, with emphasis on class-imbalanced learning, Bayesian methods, and robust, interpretable predictive modeling for complex, high-dimensional data. She is particularly interested in methodologies that support reliable decision-making in domains such as cybersecurity/IoT anomaly detection, health data analytics, and education analytics.
At New York Institute of Technology-Vancouver, she teaches statistics and programming for data science, integrating hands-on learning through Python/R-based labs and applied projects that connect core theory to real-world impact.
- Matharaarachchi S., Turgeon M., Domaratzki M., Muthukumarana S. (2026). “Sequential Bayesian Estimation of the F1 Score Using the Dirichlet-Multinomial Model.” International Journal of Data Science and Analytics. https://rdcu.be/eSU8X
- Matharaarachchi S., Domaratzki M, Muthukumarana S. (2024). “Enhancing SMOTE for Imbalanced Data with Abnormal Minority Instances.” Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2024.100597
- Matharaarachchi S., Domaratzki M., Muthukumarana S. (2022). “Minimizing features while maintaining performance in data classification problems.” PeerJ Computer Science 8:e1081. https://doi.org/10.7717/peerj-cs.1081
- Matharaarachchi S., Domaratzki M., Katz A., Muthukumarana S. (2022). “Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets.” JMIR Form Res. https://doi.org/10.2196/37984
- Matharaarachchi S., Domaratzki M., Marasinghe C., Muthukumarana S., and Tennakoon V. (2022). “Modeling and Feature Assessment of the Sleep Quality among Chronic Kidney Disease Patients.” Sleep Epidemiology. https://doi.org/10.1016/j.sleepe.2022.100041
- Matharaarachchi, S., M. Domaratzki, and S. Muthukumarana (2021). “Assessing feature selection method performance with class imbalance data.” Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2021.100170
- DTSC 502 Fundamentals of Probability and Statistics for Data Science
- DTSC 610 Programming for Data Science
- DTSC 620 Statistics for Data Science