Highly driven Electronic Information Engineering graduate with a strong specialization in Artificial Intelligence, Deep Learning, and Computer Vision, eager to apply advanced algorithmic and system design skills to impactful AI/ML Engineer roles. Proven ability to lead complex projects from concept to publication, developing intelligent object detection systems and pioneering multi-scale medical image segmentation networks. Successfully delivered quantifiable results, including significant contributions to Q1 journal publications and provincial-level competition wins, demonstrating a commitment to innovation and technical excellence.
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Bachelor
Electronic Information Engineering
Grade: 3.20/5.00
Courses
Signals and Systems
AI Probability
Digital Signal Processing
Analog Electronic Technology
Awarded By
National University Embedded Chip and System Design Competition Committee
Led a team to design and implement an innovative embedded system solution, recognized with a regional third prize in a national-level competition.
Awarded By
Shandong Province University Electronic Information Technology Application Competition Committee
Spearheaded a project demonstrating excellence in electronic information technology application, earning provincial recognition for technical skill and problem-solving capabilities.
Awarded By
Huawei "Kunpeng Cup" Competition Committee
Led an innovation and entrepreneurship project focused on new kinetic energy software, recognized at the provincial level for its potential and innovative approach.
Python.
PyTorch, TensorFlow.
Anaconda, PyCharm, ITK-SNAP, Origin, Raspberry Pi.
Deep Learning, Computer Vision, Medical Image Processing, Neural Networks, Convolutional Neural Networks (CNN), YOLOv3, Self-Attention Mechanisms, Transfer Learning, Image Segmentation, Data Annotation, Embedded Systems.
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Summary
Developed a lightweight and efficient 3D medical segmentation network, the Multi-scale Dense Dilated Transformer, to address challenges in capturing multi-scale features and fine details in complex 3D medical images. This project focused on optimizing computational efficiency while enhancing segmentation accuracy.
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Summary
Designed and implemented an innovative cross-convolutional transformer architecture to enhance automated multi-organ segmentation in diverse medical images. This research focused on combining global self-attention and local convolutional modeling to improve segmentation accuracy and efficiency.
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Summary
Collaborated with leading medical institutions to construct a large-scale, high-quality medical image segmentation dataset specifically for cardiac structures. This project involved meticulous manual annotation to create a foundational resource for advanced AI research in cardiology.
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Summary
Led the full lifecycle development of an intelligent fruit and vegetable pricing device, leveraging deep learning for automated identification and real-time pricing. This project involved comprehensive system design, model training, hardware integration, and data management to optimize retail operations.