About Me

I am an Applied Research Scientist at the NVIDIA Toronto AI Lab, where I construct and render neural representations at scale. I received my PhD from the Visualization & Interface Design Innovation Lab at the University of California, Davis, USA. Prior to my PhD, I obtained a master’s degree in computer graphics from the University of Utah and a bachelor’s degree in physics from the Hong Kong University of Science and Technology.

My expertise lies in 3D rendering, 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 real-time rendering and scientific data compression, 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.