To Transform Complex, Multi-Source Data
Into Predictive Insight
to Reveal the Dynamics of Complex Systems
and Supports Better Decisions Under Risk

I am a transportation system modeler and data scientist with a PhD in Engineering Science (Transportation) and more than a decade of experience.  My work sits at the intersection of data science, statistical modeling, and machine learning, with a strong emphasis on understanding, modeling, and improving complex systems using real-world data, primarily in the context of transportation systems modeling and analysis. 

My expertise spans large-scale data fusion and advanced data science methodologies, including machine and deep learning, Bayesian inference, time-series and state-space modeling, and optimization, combined with traffic flow theory, driving behavior analysis, safety, and risk modeling. I have designed end-to-end analytical pipelines that integrate heterogeneous data sources, ranging from vehicle trajectories, loop detectors, and video streams to survey and textual data, to extract actionable insights for complex real-world systems such as traffic operations, safety assessment, and decision support. 

Across my career, I have worked on both policy-oriented and research-driven projects, collaborating with universities, public authorities, and industry partners in international settings. I value methodological rigor, interpretability, and reproducibility, and I am particularly interested in applying data-driven approaches to real-world challenges where system behavior, uncertainty, and human decision-making play a central role. My broader goal is to contribute analytical and computational tools that improve safety, efficiency, and sustainability in complex socio-technical systems.