Summary
Mengjie Zhao is a sciencepreneur and PhD-trained researcher focused on predictive maintenance for large-scale industrial systems, with seven years of experience bridging computational mechanics, CFD, and machine learning. She applies temporal graph neural networks and multi-modal sensor fusion to detect early faults in chemical plants and critical infrastructure, turning research prototypes into deployable PdM solutions. As co-founder of KIIO and a researcher at EPFL, she combines hands-on system design with product-oriented thinking to reduce downtime and deliver actionable insights. Her background spans numerical simulation, virtual sensing, and condition monitoring, giving her a rare ability to translate complex physical models into scalable IIoT time-series architectures. Based in Lausanne, she leverages deep domain expertise from TUM, ETH Zürich, and EPFL to tackle industrial-scale reliability challenges. Beyond academic publications, she actively commercializes GNN-driven maintenance tech, demonstrating a pragmatic focus on real-world impact.
7 years of coding experience
5 years of employment as a software developer
Maser of Science with honours Computational Mechanics, Maser of Science with honours Computational Mechanics at Technical University of Munich
Doctor of Philosophy - PhD Intelligent Maintenance and Operations Systems, Doctor of Philosophy - PhD Intelligent Maintenance and Operations Systems at EPFL
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at ETH Zürich
German, Chinese, English, French