Mr.SeungHyun Lee | Plastic Pollution | Best Researcher Award

Researcher | KOREA RESEARCH INSTITUTE OF SHIPS& OCEAN ENGINEERING | South Korea

SeungHyun Lee is a Principal Engineer at the Korea Research Institute of Shipbuilding and Ocean Engineering (KRISO), specifically in the Maritime Safety and Environment Research Center. With over 30 years of experience in marine safety and environmental research, he has made significant contributions to reducing marine plastic waste and enhancing maritime safety.

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Education 🎓

SeungHyun Lee received his Ph.D. from Hanbat University in 2017. His academic journey has equipped him with extensive knowledge and skills in marine safety and environmental research.

Experience 🏢

SeungHyun Lee has been a dedicated researcher and Principal Engineer at KRISO for three decades. His work primarily focuses on marine safety and environmental preservation, contributing to significant projects aimed at reducing marine plastics and improving maritime safety.

Research Interests 🔬 

Research Interests: Marine safety and environmental research, with a specific focus on developing estimation models for marine plastic waste inflow and innovative methods for marine garbage collection.

Awards 🏆

SeungHyun Lee is a nominee for the Best Researcher Award, recognizing his outstanding contributions to marine safety and environmental research.

Publications 📚

1. Estimating the Temporal Impacts of Nearshore Fisheries on Coastal Ocean-Sourced Waste Accumulation in South Korea Using Stepwise Regression
   Published in SUSTAINABILITY
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   Cited by numerous articles in marine pollution research.
2. Estimating the Amount of Submerged Marine Debris Based on Fishing Vessels Using Multiple Regression Model
   Published in SUSTAINABILITY
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   Frequently cited in studies on marine debris assessment.
3. Assessment of Marine Debris on Hard-to-Reach Places Using Unmanned Aerial Vehicles and Segmentation Models Based on a Deep Learning Approach
   Published in SUSTAINABILITY
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   A key reference in UAV-based marine debris research.
4. A Comparative Study of Deep Learning-Based Network Model and Conventional Method to Assess Beach Debris Standing-Stock
   Published in MARINE POLLUTION BULLETIN
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   Widely cited in comparative studies of marine debris assessment methods.