In This Story
Jin Hyung Lee, a PhD candidate in the Department of Statistics, in collaboration with his advisor, Assistant Professor Ben Seiyon Lee, has developed novel statistical methodology for analyzing large-scale complex data that can be up to 3,600 times faster than the current standard. He will receive the Korean International Statistical Society (KISS) 2024 Outstanding Student Paper Award for his work and present about it at the American Statistical Association’s Joint Statistical Meetings (JSM) 2025 in Nashville, Tennessee.
The primary application of Lee’s methodology is in the analysis of high-resolution satellite image data, which is inherently complex and high-dimensional. A major challenge lies in predicting values at unobserved locations within the satellite imagery. While traditional spatial modeling methods are computationally intensive and time-consuming, Lee's novel approach offers a solution that is significantly faster. This development has significant implications for various fields, such as atmospheric sciences, ecology, public health, and medical imaging.
Lee’s research focuses on the application of variational inference to massive spatial datasets using a unique machine learning algorithm. Variational inference is a method used to approximate complex probability distributions when true inference is not possible, making it a powerful method for modeling complex spatial datasets. The proposed approach significantly improves the computational efficiency of spatial data analysis, achieving speeds up to three orders of magnitude faster than existing methods while maintaining comparable accuracy.
Lee's method can be used to predict weather patterns in remote and rapidly changing environments like the Gulf Coast, southern California, and polar regions like Alaska or Antarctica. The ability to process high-dimensional data quickly and accurately is crucial for making timely and informed decisions in these regions, especially when they are at risk of geophysical hazards such as hurricanes, wildfires, or sea ice loss. Additionally, the methodology has potential applications in infectious disease modeling and public health data analysis, where rapid and accurate predictions are essential for effective intervention and policymaking.
Lee's research is part of his broader dissertation work, which focuses on developing and applying variational inference techniques to various statistical models. His goal is to simplify these methods, making them more accessible to non-experts and applicable to a wide range of data types. By doing so, Lee aims to bridge the gap between advanced statistical methodologies and practical applications, enabling more efficient and effective data analysis across different fields.
At the JSM 2025, Lee will have the opportunity to present his research to a large audience of statisticians from around the world. The conference, which attracts over 5,000 participants annually, provides a platform for sharing cutting-edge research, networking with peers, and exploring new developments in the field of statistics. Lee’s presentation will be part of a session hosted by KISS, where he and other award recipients will showcase their work and receive their awards.
Receiving the KISS Outstanding Student Paper Award is a significant milestone in Lee’s academic journey. It not only recognizes his contributions to the field but also provides him with valuable exposure and opportunities for future collaboration. As Lee continues to advance his research, his innovative methodologies and their applications are poised to make a lasting impact on the field of statistics and beyond.