what we study
Research
We study perception, generalization, and explanation in modern AI systems. From robust recognition and segmentation to attribution, saliency, and concept-based explanations, we aim to make AI more reliable and interpretable.
computer-vision
Computer Vision
Detection, segmentation, multi-modal learning, and evaluation on real-world datasets.
interpretabilityInterpretability
Attributions, saliency maps, concept probes, and human-centered evaluation for explanations.
responsible-aiResponsible AI
Robustness, fairness, data quality, and transparent reporting for trustworthy systems.