Faria Alam

Research Interests

My research focuses on applied machine learning and computational public health, particularly epidemic modeling using AI-driven agents and synthetic populations. I work with spatiotemporal models, graph neural networks, and predictive techniques to forecast disease outbreaks and societal trends, supported by a foundation in decision-support systems, optimization, and supervised learning.

Publications

Faria Alam, Sharad Sharma, Pretom Roy Ovi, K. S. M. Tozammel Hossain, "Simulating Epidemic Response and Communication using AI-powered NPCs in Virtual Reality" in Electronic Imaging, 2026, pp. 190-1 - 190-6, https://doi.org/10.2352/EI.2026.38.13.ERVR-190

Alam, F.; Sang Ko, H.; Lee, H.F.; Yuan, C. Deep Learning Approach for Volume Estimation in Earthmoving Operation. Int. J. Ind. Eng. Manag. 2023, 14, 41–50.

M. Deniz, F. Alam, C. Yuan, H. S. Ko, and H. F. Lee, ‘‘Single image based volume estimation for dump trucks in earthmoving using a machine learning approach,’’ EPiC Ser. Built Environ., vol. 3, pp. 380–388, May 2022.

F. Alam and K. Shahed, “Multicriteria decision making models for performance evaluation and selection of suppliers applying fuzzy TOPSIS and DEA,” International Journal of Applications of Fuzzy Sets and Artificial Intelligence, vol. 10, pp. 227–250, 2020.

Faria Alam, Md. Nazmus Sakib, Purbasha Das and Sayem Ahmed, “An integrated support vector classification approach for performance evaluation and selection of multi-attribute suppliers using CCA & PCA”, pp. 103-111, Paper ID-ICSG2i2019PI-80, International Scientific Conference on Sustainability of Global Garment Industry AUST, Dhaka, Bangladesh, March 5-7, 2019