His research focus is the intersection among air pollution, data science, and computer science. He is especially interested in leveraging machine learning models and emerging and low-cost air quality measurement techniques including low-cost air quality sensors, mobile air quality sampling, and micro-satellite imagery to give accurate estimation of high-resolution spatial maps of air quality, as well as the identification of hot-spot sources at a neighborhood level to target mitigation strategies, with a focus on PM2.5. His teaching interests at Duke Kunshan include air pollution, atmospheric chemistry, environmental science, environmental chemistry, and data analytics.

He has had papers published in leading academic journals such as Science of Remote Sensing, Transactions on Machine Learning Research, and Atmospheric Measurement Techniques. His 2018 work “Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments” is listed as a highly cited paper in Web of Science and has been cited 327 times so far.

He earned his BSc degree in Environment and Sustainable Development at The Hong Kong Polytechnic University in 2016. Subsequently, he obtained an MS and a PhD in Civil and Environmental Engineering from Duke University in 2018 and 2021, respectively.

Prior to joining DKU, he spent 2.5 years working in Southern California. Initially, he worked at the California Air Resources Board (CARB), a state agency of the government of California that aims to reduce air pollution. He later transitioned to Aclima, a start-up specializing in mobile air quality monitoring across the US.