Personal Summary:

I typically program in Python and use C++/C to implement AI algorithms in practical tools.

Mathematically, I am familiar with AI-related linear algebra and probability and statistics theory, and have taken courses in image analysis and deep learning.

I am proficient in deep generative models such as GANs, VAEs, and VQVAEs, and have explored various perception algorithms in image detection and segmentation, image restoration, and BEV detection. I can skillfully construct solutions and perform original designs. I am familiar with the entire process of engineering deployment of neural networks, including lightweight network design, model compression, and optimization for deployment. I have independently published academic papers and can rapidly apply advancements from academia to business and execute them engineeringly. I have a rich passion for the research and development of new technologies.

Career

2023.4 - 2024.4

I work at EVAS, where I am primarily responsible for the research and deployment of 2D vision and BEV-based detection networks. I also handle the quantization acceleration of neural networks, which is very common and important in edge-side algorithm deployment. Efficient neural networks are crucial in real-world production environments.

Education

2020.9-203.6

I study at BMEC of USTC. My research focuses on solving MRI image reconstruction problems using neural networks. My main achievements include improving generative models and designing lightweight transformer networks for image reconstruction.