I recently recieved my Ph.D. in Computer Science Engineering at Ghent University. Fully funded by the AI Flanders strategic program, I conducted my research with the DECIDE team at IDLab, Ghent University-imec under the supervision of Prof. Pieter Simoens and Prof. Sam Leroux.

My doctoral work focuses on developing transferable and privacy-friendly deep learning techniques for real-world audio-visual urban surveillance: bridging the gap between the lab and dynamic street environments. The goal is to create frameworks that are both technically advanced and ethically robust, ensuring responsible AI deployment in smart cities. This has involved designing self-supervised and contrastive learning models for urban monitoring, collaborating with industry partners to validate results with domain-specific datasets, and innovating on privacy protection mechanisms that empower user consent.

My research outcomes include peer-reviewed publications in journals such as IEEE Pervasive Computing, Sensors, and Frontiers in Robotics and AI. Many of these methodologies have been presented at academic and industry events and are now being adopted in practical applications: extending beyond the academic context to immediate societal impact.

Beyond research, I am passionate about mentorship and leadership. I’ve served as a teaching assistant for three semesters in "Applied Machine Learning," guiding students through real-world data-driven projects using tools ranging from Airbnb datasets to Sony Depthsensing. As President of the Taiwanese Student Association in Ghent, I initiated and led a city-wide mentor-mentee program that supported our community throughout the pandemic, further sharpening my cross-cultural collaboration and community-building skills.

Together, these experiences reflect my commitment to advancing AI technology that is not only powerful, but also human-centric, transparent, and positively impactful for diverse urban societies.