Curriculum
Curriculum
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.
