Research

My research focuses on leveraging the power of AI to advance the understanding and development of materials with desirable properties. Through the application of machine learning, natural language processing, and optimization I have developed methods for predicting material properties, discovering new chemical reactions, and automating the development of various cementitious composites, including ordinary concrete, ultra-high-performance concrete, and strain-hardening cementitious composites. I have also developed a method based on computer vision and image processing to characterize and classify nanomaterials.

My research revolves around two core aspects: optimal sensor deployment and sensor data analysis. I strategically position sensors using optimization techniques to collect valuable data throughout structures, ensuring effective monitoring of structures. By analyzing data collected from distributed fiber optic sensors, I extract crucial parameters to detect potential damage and excessive loads, thus enhancing our understanding of structural health. These advancements contribute to improved maintenance strategies and the creation of safer and more resilient structures.

My research centers around two areas: optimization design of high-strength yet lightweight concrete composite modules, with a particular focus on robotic integration and accelerated construction methodologies. Concurrently, I employ and develop a blend of optimization algorithms that are inspired by natural phenomena and game theory to design resilient structures. These algorithms are aimed at significantly enhancing computational efficiency, especially vital in the optimization of large-scale and complex structural designs.