About Me

I am a last-year PhD candidate in the Visualization & Interface Design Innovation Lab at the the University of California - Davis, USA. Prior to my PhD study, I obtained a computer graphics master degree from the University of Utah and a physics bachelor degree from the Hong Kong University of Science and Technology.

My expertise is rooted in the field of scientific visualization and high-performance computing, utilizing parallel hardware such as CUDA, vectorized CPUs, and supercomputers. I am also proficient in managing large-scale data and automating adaptive workflow scheduling.

My recent research has focused on the application of implicit neural networks for the representation and compression of scientific data, aiming to facilitate more streamlined interactions with large datasets. Looking ahead, I am excited to further integrate implicit neural networks with other cutting-edge machine learning techniques, such as large language models and Generative AI, to advance scientific visualization and enhance computer graphics pipelines.

I am enthusiastic about the potential of these technologies to transform our approach to visual data exploration.