converging machine

Resisting singularity with Descartes - Cogito Ergo Sum.

converging_machine.png

UCSF Department of Radiology and Biomedical Imaging

China Basin, 3rd Floor.

San Francisco, California. 94107

I am a computational scientist specializing in AI-driven medical imaging analysis at UCSF’s Yang Lab. My research focuses on developing advanced machine learning solutions that transform multimodal radiological data into precise diagnostic tools.

Research Focus

  • Data-centric AI approaches for medical imaging
  • Machine learning algorithms for diagnostic enhancement
  • Multimodal radiological data integration

Current Work At Yang Lab - UCSF, I develop novel deep learning architectures that push the boundaries of automated medical image analysis, working at the cutting edge of AI applications in radiology.

My work aims to bridge the gap between clinical needs and technological innovation, creating practical AI solutions that enhance radiological workflows and improve patient care.

Explore my repositories or reach out to discuss potential collaborations.

Mehmet Can Yavuz, PhD - Post-doctoral Scholar personal webpage.

selected publications

  1. policy_grad.png
    Policy Gradient-Driven Noise Mask
    Mehmet Can Yavuz, and Yang Yang
    arXiv preprint arXiv:2406.14568, 2024
    Bib
  2. crossdconv.png
    Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operation
    Mehmet Can Yavuz, and Yang Yang
    arXiv preprint arXiv:2411.02441, 2024
    Bib
  3. vcl.png
    Self-Supervised Variational Contrastive Learning with Applications to Face Understanding
    Mehmet Can Yavuz, and Berrin Yanikoglu
    In 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), 2024
    Bib