CV

Education

• B.S. in Electronic Information Engineering, Beihang University, 2020
• M.Phil. in Computer and Information Engineering, The Chinese University of Hong Kong, Shenzhen, 2022
• Ph.D focusing studies in artificial intelligence in remote sensing (AI4RS), Helmholtz-Zentrum Dresden-Rossendorf / Technical University of Munich, 2026 (expected)

Research experience

• Summer 2018: Research Assistant
  • Tsinghua University
  • Duties included: Conducting Deep Learning Experiments
  • Supervisor: Dr. Yuezhi Zhou
• Summer 2019: Intern
  • Nokia R&D Center Beijing
  • Duties included: Development of Automatic Docker Deployment on Servers
  • Supervisor: Baochen Guo

Skills

• Programming Languages: Python
• Deep Learning Architectures
  • PyTorch
  • TensorFlow
• Hardware Management and Maintainance

Publications

Style transformation-based change detection using adversarial learning with object boundary constraints

A hybrid model-based and data-driven approach for cloud removal in satellite imagery using multi-scale distortion-aware networks

Cloud removal in optical remote sensing imagery using multiscale distortion-aware networks

Multilevel Deformable Attention-Aggregated Networks for Change Detection in Bitemporal Remote Sensing Imagery

Federated Deep Learning With Prototype Matching for Object Extraction From Very-High-Resolution Remote Sensing Images

Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning

Weakly Supervised Local-Global Anchor Guidance Network for Landslide Extraction With Image-Level Annotations

AI Security for Geoscience and Remote Sensing: Challenges and future trends

Txt2Img-MHN: Remote sensing image generation from text using modern Hopfield networks

Responsible AI for Earth Observation

MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification

MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery

Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models

Teaching

Teaching Assistant at the CUHK Shenzhen