Sarah Tam

I am a civil engineer with graduate training from the Technical University of Munich and a bachelor’s in Civil and Environmental Engineering from HKUST, focused on traffic engineering, hydraulic engineering and computational methods. I blend engineering fundamentals with programming and machine learning to solve transport and water-related problems.

My thesis and research apply computer-vision and deep-learning methods to real-world mobility questions: I fine-tuned YOLO models, implemented pose estimation, used TensorFlow bidirectional LSTMs for pedaling detection, and processed skeletal data in Python to infer cyclist intent. I have also modeled wave behavior and conducted sensitivity analyses for meta-pipeline designs using MATLAB and Ansys Fluent.

In industry I contributed to BIM clash detection and constructability analysis with Navisworks, produced conceptual temporary works drawings in SketchUp, mapped lighting in AutoCAD and delivered sustainability recommendations that achieved energy savings. I seek roles where I can combine traffic or hydraulic engineering with computation and AI to deliver safer, more efficient infrastructure.