Jintong Han is a PhD candidate in the Department of the Built Environment at the National University of Singapore (NUS), specializing in building fault detection, diagnosis, and prognosis. She develops data-driven methods for building systems, with particular expertise in Bayesian neural networks for uncertainty-aware inference. Her work delivers calibrated, reliable FDDP algorithms and explores generative models to synthesize fault data. She holds an MSc in System and Project Management and a BSc in Traffic Engineering. With interdisciplinary training that bridges controls, data science, and building engineering, she excels at cross disciplinary integration and collaboration. She is passionate about developing advanced machine learning algorithms and applying them to integrated solutions for real world building systems.