Jayasekara (Ravindu) Jayasekara

PhD candidate in Civil Engineering specializing in uncertainty quantification, resilience, and catastrophe modeling, with a robust foundation in advanced analytical techniques. Expertise includes surrogate modeling, machine learning methodologies (such as CNN and YOLO), and active learning for optimization and fragility modeling, facilitating the resolution of complex engineering challenges. Proven experience in conducting large-scale physical experiments enhances capabilities in risk assessment and disaster mitigation strategies. Seeking opportunities in catastrophe modeling, risk analysis, or risk engineering to contribute to data-driven advancements in infrastructure resilience.

• Graduate Student Supplements (GRASS) grant 2026 ($10,000) – awarded by the National Science Foundation and the Louisiana Board of Regents
• Journal of Marine Science and Engineering Travel Award 2024

• Jayasekara, J.R., Kameshwar, S., 2024. “Non-Deterministic Kriging for Probabilistic Systems with Mixed Continuous and Discrete Input Variables”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 10, 04024058 [link]
• Jayasekara, J. R., & Kameshwar, S. 2025. An active learning strategy for sequential designs of probabilistic systems with continuous and discrete input variables. Engineering Optimization, 1–20, [link]
• Jayasekara, J.R., Koh, M.J., Doyle, K., Kameshwar, S., Park, H., Cox, D., Lomonaco, P. 2026. Experimental investigation of mechanisms leading to tsunami-driven debris damming at elevated column structures, Coastal Engineering, 104952 [link]