🎓 Master of Science in Computer Vision (Soongsil University, South Korea)
💼 AI Researcher at DeltaX, a startup focused on cutting-edge vision-based AI systems
🌏 Passionate about bringing intelligence to the real world using deep learning and computer vision

💡 Key areas of interest:
3D Object Detection, Point Cloud Processing, Scene Understanding, Panoptic Segmentation, Point Cloud Reconstruction

• Working on real-time AI perception systems for autonomous platforms and smart infrastructure
• Designing and optimizing high-performance deep learning pipelines for vision-based tasks
• Leveraging frameworks like PyTorch, TensorRT, OpenCV, MMDetection3D, MMDet, Detection2, OpenPCDet, and others for production-ready AI
• Focused on both image-based and LiDAR-based solutions, with deployment on embedded devices (Jetson Orin, Xavier)

• Conducted research on 3D object detection and multi-object tracking for autonomous driving applications
• Explored architectures involving transformers, sparse convolution, and panoptic segmentation
• Wrote and published multiple peer-reviewed papers on top-ranking benchmark results (KITTI, etc.)

• Developed deep learning models for visual inspection and damage detection
• Participated in the end-to-end system deployment including cloud integration and model scaling

• Simulated robotic motion planning and trajectory optimization on quadruped robots
• Utilized tools such as ROS, Gazebo, and MATLAB for robotics experiments

• Built real-time object recognition systems using deep learning for edge platforms
• Worked on projects involving facial recognition and vehicle detection with lightweight neural networks

• Languages: Python, C++, Matlab, C#
• Frameworks: PyTorch, TensorFlow, Keras, Detectron2, OpenPCDet, Ultralytics
• Libraries: OpenCV, Open3D, TorchVision, NumPy, Matplotlib, Mayavi
• Edge & Acceleration: TensorRT, ONNX, CUDA, OpenVINO
• Tools: Git, Docker, VSCode, Jupyter, PyCharm
• OS: Ubuntu, Jetson Linux

• 🏅 TSSTDet: Transformation-based 3D Object Detection via Spatial Shape Transformer — IEEE Sensors Journal (Q1) – First Author
• 🏅 3ONet: 3D Detection for Occluded Objects — IEEE Sensors Journal (Q1) – First Author
• 🧠 AFMtrack: Attention-Based Feature Matching for MOT — IEEE Access (Q1) – Second Author
• 📦 Shape-Aware 3D Detection — KICS Conference – First Author
• 🧩 ESSDet: Enhancing Spatial Shape for 3D Detection — ICOIN Conference – First Author
• 🧠 CAMTrack: a combined appearance-motion method for multiple-object tracking — Machine Vision & Applications Journal (Q2) – Second Author

• 💻 C++ – SoloLearn
• 🐍 Python Core – SoloLearn
• 📊 Machine Learning – SoloLearn
• 🤖 Machine Learning with Python – IBM (Coursera)
• 🚗 Introduction to Self-Driving Cars – University of Toronto (Coursera)
• 🧠 Neural Networks and Deep Learning – DeepLearning.AI
• 🔍 Deep Neural Networks with PyTorch – IBM (Coursera)
• 👁️ Visual Perception for Self-Driving Cars – University of Toronto (Coursera)

• ✉️ [email protected]
• 🌐 LinkedIn
• 📚 Google Scholar

“Driven by curiosity and guided by data, I build efficient, scalable, and real-time computer vision systems to empower intelligent machines.”