Developing lightweight machine learning models optimized for edge devices with limited computational resources.
Learn MoreResearching specialized hardware architectures for efficient AI inference at the edge with minimal power consumption.
Learn MoreExploring privacy-preserving distributed learning techniques that keep data on devices while improving models.
Learn MoreIEEE Conference on Computer Vision and Pattern Recognition
Read PaperACM International Conference on Embedded Software
Read PaperNeural Information Processing Systems
Read PaperIEEE Internet of Things Journal
Read PaperPhD in Computer Vision with 15+ years of experience in AI research.
Specialized in neural network optimization and edge deployment.
Expert in AI accelerator design and low-power computing.
Focuses on federated learning and privacy-preserving AI.
We're always interested in exploring partnerships with academic institutions and industry researchers.