Research on 3D Computer Vision, Multi-Sensor Fusion, and Prompt LearningAdvised by Prof. Sungeun HongKey projects:- Developed RGB-D to Point Cloud Dataset Converter (https://github.com/parkie0517/NYUDepthV2_PointCloud_Converter)- Created Semantic KITTI Multi-Frame Dataset Generator (https://github.com/parkie0517/SemanticKITTI-MultiFrameGeneration)Gained hands-on experience in advanced computer vision techniques and dataset creation for autonomous driving applications.

Research focused on 3D Object Recognition and Backdoor AttackAdvised by Prof. Seok-Hwan ChoiKey accomplishments:- First author of "Investigating the Vulnerability of 3D Point Cloud Classifiers to Backdoor Attacks Leveraging Transfer Learning" (Journal of Korean Institute of Intelligent Systems, Mar. 2024)- Received Outstanding Paper Award at the Korean Institute of Intelligent Systems 2023 Fall ConferenceDeveloped expertise in 3D computer vision, deep learning security, and transfer learning techniques.

Research focused on Computer Vision and LiDAR PerceptionKey areas of work:- Explored advanced techniques in LiDAR data processing and interpretation- Contributed to projects integrating computer vision algorithms- Gained hands-on experience with state-of-the-art perception systems for autonomous vehiclesDeveloped practical skills in sensor fusion, point cloud processing, and deep learning applications for 3D scene understanding.

Development of Non-destructive Sugar Meter Using Hybrid Machine LearningKey Projects:- Non-destructive Sugar Meter: Developed a machine learning model to estimate sugar content in agricultural products. Utilized hybrid machine learning techniques to combine traditional machine learning with deep learning methods.- Apple Dataset Creation: Created a dataset containing multi-view RGB images and sugar levels of apples.Gained hands-on experience in hybrid machine learning models and non-destructive testing methods. This project was part of the IITP Korean Software Square Fall 2022 Program, funded by the IITP.