Research interests include computer vision, computer graphics, machine learning, medical imaging, animation and simulation, image-based rendering, and physics-based modeling.
Focuses on explaining visual data through appropriate physical and statistical models for applications in computer vision, computer graphics, and medical image analysis.
Central research theme is modeling the interaction between 3D shape and illumination for shape and motion estimation, object recognition, and augmented reality.
Interested in statistical modeling of non-rigid 3D shape deformation and accurate matching of 3D data.
Extensive work on human modeling, especially faces, including facial appearance under variable illumination and expression modeling for biometrics and human-computer interaction.
Collaborates with psychologists using multimodal data (e.g., eye-tracking, fMRI) to study human behavior and applies machine learning to brain image analysis based on neuropsychological findings.
Research funded by NSF, NIH, DoE, DoJ, FRA, New York State, and Adobe Corp.