Computer Vision Lab
We develop intelligent vision systems with a focus on controllability, interpretability, and reliability. Our research spans constrained learning, uncertainty quantification, 3D geometry, generative models, and medical imaging applications.
Research Areas
Constrained Learning
Developing algorithms that incorporate domain-specific constraints to guide model predictions and improve reliability.
Interpretability & Explainability
Creating methods to understand and explain AI decisions, enhancing transparency and trustworthiness.
Uncertainty Quantification
Quantifying and reducing uncertainty in predictions to build more robust and reliable systems.
3D Vision & Geometry
Advancing 3D reconstruction, mesh generation, and geometric understanding from images.
Generative Models
Developing controllable and efficient generative models for images, videos, and 3D content.
Medical Imaging
Applying computer vision and deep learning to medical image analysis and diagnosis.