Lee Jung-hyun, a graduate student at the Korea Advanced Institute of Science & Technology (KAIST) TERA Lab, received the Best Student Paper — Honorable Mention at the 2026 Institute of Electrical and Electronic Engineers International Symposium on Electromagnetic Compatibility, Signal & Power Integrity, the semiconductor research group said Thursday. During the symposium held from Aug. 3-7 in Dallas, Texas, Lee received the award for his paper, “Physics-Aware Tensor Learning for Data-Efficient Multi-Port Power Distribution Network Analysis.” The paper proposed a new tensor-based method for efficiently storing and analyzing massive amounts of power distribution network (PDN) data generated in advanced semiconductor packages such as high-bandwidth memory (HBM) and chiplet systems. Using a mathematical technique called Tucker decomposition to extract representative patterns from the data, the research team achieved a compression ratio of about 695 to 1 In a test involving a 256-port PDN, while maintaining the targeted reconstruction accuracy. “I became interested in tensors while st