Resume

Education

• B.S. in Automotive Engineering, Hefei University of Technology, 2015~2019
• M.S. in Computer Science, Hefei University of Technology, 2020~2023 (expected)

Research Interesting

• Machine Learning
• Deep Model Compression and Acceleration
• Low-bit Quantization
• Robotics

Publications

• Yan Luo#, Yangcheng Gao#, et al. “Long-Range Zero-Shot Generative Deep Network Quantization,” submitted to CVPR, 2023.

• Zhao Zhang, Yangcheng Gao, et al. “SelectQ: Calibration Data Selection for Post-Training Quantization,” submitted to CVPR, 2023.

• …, Huan Zheng, Yangcheng Gao, et al. “MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results,” Mobile Intelligent Photography and Imaging Workshop 2022 (ECCV MIPI), Tel-Aviv, Israel, Oct 2022.

• Yangcheng Gao, Zhao Zhang, et al. “ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization,” submitted to IEEE TNNLS, 2022.

• Yangcheng Gao, Zhao Zhang, et al. “Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization,” IEEE ICDM 2022.

• Yangcheng Gao, Zhao Zhang, et al. “Fast and Effective Data-Free Deep Neural Network Compression by Dictionary Pair-Driven Reconstruction,” submitted to KAIS, 2022.

• Yangcheng Gao, Zhao Zhang, et al. “Dictionary Pair-based Data-Free Fast Deep Neural Network Compression,” IEEE ICDM 2021. (Invited for KAIS journal publication as Best-ranked paper)

Work Experience

• Spring 2022: Shanghai AI Laboratory Research Intern

  • Duties included: Model Quantization Theory Research
  • Supervisor: Dong Wang

Skills

• Python
• C++
• Machine Learning Tools
  • PyTorch
  • NumPy
  • TensorRT & ncnn (for model deployment)