Kookmin University (President Jeong Seung Ryul) announced on the 23rd that it held an award ceremony for the '2023 Second Half Graduate Student Thesis-based IP Creation Support Program Competition' at the Kookmin University Industry-Academia Cooperation Center.
The contest, which is designed to intellectualize the excellent research achievements of graduate students in the leading fields of innovation in the 4th Industrial Revolution and further enhance national competitiveness through technology commercialization, was held for the 6th time under the auspices of the LINC 3.0 project group and sponsored by the Kookmin University Industry-Academia Cooperation Center.
Jeong Jin-myung (Graduate School of Business and IT - Advisor Namkyu Kim), who won the grand prize for his paper, "A Language Model Fine Tuning Methodology Based on Offsite Tuning for Privacy Protection," said, "I am grateful to Professor Namkyu Kim for his full support of my research." He added, "I will continue my follow-up research to produce more practical and innovative results.
Juen jae-seung (Graduate School of Automotive Engineering, Advisor Yujin Woo) won the first prize for "Research on Point Cloud Segmentation and Classification Network Based on Multi-Head Attention Using Location Information," and Park Sang-Hoon (Graduate School of Automotive Engineering, Advisor Yujin Woo) won the second prize for "Improving Generalization Performance of Image-based Reinforcement Learning through Strong Data Augmentation and Contrastive Learning," which was well received by the judges.
"We will do our best to support graduate students' creative ideas not only in their theses but also in practical applications, from intellectual property rights to start-ups," said Lee In-hyung, head of Kookmin University's Industry-Academia Cooperation Center. "We will spare no effort to create an environment where graduate students can focus on research and become competitive on their own."
This content is translated from Korean to English using the AI translation service DeepL and may contain translation errors such as jargon/pronouns.
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