{
  "id": 727111,
  "title": "KAIST student receives best paper award at international symposium",
  "url": "https://urgent.news/2026/08/13/kaist-student-receives-best-paper-award-at-international-symposium",
  "topic": "world",
  "section": "World",
  "published": "2026-08-13T03:47:03.000Z",
  "source": {
    "name": "The Korea Times",
    "slug": "the-korea-times",
    "url": "https://www.koreatimes.co.kr/business/tech-science/20260813/kaist-student-receives-best-paper-award-at-international-symposium"
  },
  "original_language": "en",
  "account": "Lee Jung-hyun, a graduate student at KAIST's TERA Lab, was awarded the Best Student Paper — Honorable Mention at the 2026 IEEE International Symposium on Electromagnetic Compatibility, Signal & Power Integrity. The symposium took place from August 3-7 in Dallas, Texas. For his achievement, Lee presented the paper titled \"Physics-Aware Tensor Learning for Data-Efficient Multi-Port Power Distribution Network Analysis.\" The research proposed a novel tensor-based method for efficiently storing and analyzing vast amounts of power distribution network (PDN) data in advanced semiconductor packages like high-bandwidth memory (HBM) and chiplet systems. By employing Tucker decomposition, a mathematical technique, the team achieved an impressive compression ratio of approximately 695 to 1 during testing on a 256-port PDN, while maintaining the desired reconstruction accuracy. Lee's interest in tensors began during his studies.",
  "summary": "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…",
  "key_points": [
    "Lee Jung-hyun, KAIST graduate student, wins Best Student Paper - Honorable Mention",
    "IEEE International Symposium on Electromagnetic Compatibility, Signal & Power Integrity",
    "Proposed physics-aware tensor learning for data-efficient PDN analysis"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "The Korea Times",
        "title": "KAIST student receives best paper award at international symposium",
        "url": "https://urgent.news/2026/08/13/kaist-student-receives-best-paper-award-at-international-symposium-727985",
        "published": "2026-08-13T03:57:03.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}