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About Candidate
Education
1.Research Focus: Computer vision, privacy-preserving object detection, few-shot learning 2.Key Technologies: Vision-language foundation models, knowledge distillation, prompt engineering 3.Relevant Coursework: Statistical Machine Learning, Mathematics of Deep Learning, Parallel Computing, Cyberinfrastructure
1.B.Sc Thesis: Multi-source Non-negative Matrix Factorization for Visual Recognition. 2. M.Sc Thesis: Multi-source Multi-task Learning for Medical Image Analysis 3. Relevant Coursework: Distributed Systems, Embedded System Design, Data Structure, Database SQL
Experiences
1.Developed privacy-preserving object detection systems using vision-language foundation models 2.Implemented knowledge distillation techniques for efficient model deployment 3. Created novel prompt engineering methods for zero-shot domain adaptation 4. Designed and implemented large-scale deep learning training pipelines 5. Published research in top-tier conferences (AAAI, ECCV) 6.Presented research findings at international conferences and workshops
1.Developed deep learning solutions for medical image classification 2. Implemented efficient model architectures for resource-constrained environments 3. Collaborated with international research teams on multi-modal learning projects
Skills
Python
PyTorch
Machine Learning
Deep Learning
SQL
Computer Vision
Scikit-learn
