We develop computer vision methods that explicitly account for topological properties such as connectivity, continuity, and the preservation of meaningful structures. Our work focuses on topology-aware learning for curvilinear and network-like structures in both 2D and 3D, with applications including vessels, neuronal structures, road networks, and other connected visual patterns.
Topology
Topology-Aware Computer Vision
Other research areas
02 · Trust
Uncertainty Estimation & Trustworthy AI
We develop methods for estimating predictive uncertainty and improving the reliability and trustworthiness of deep learning models. Our research focuses on calibrated confidence, uncertainty-aware prediction, robustness, and identifying unreliable outputs, particularly in settings where dependable model behavior is critical.
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03 · 3D
3D Shape & Implicit Representations
We study neural representations for modeling, understanding, and manipulating complex 3D shapes. Our work includes implicit representations and part-based approaches for shape reconstruction, generation, parametrization, editing, and optimization, with an emphasis on representing geometry in flexible and controllable ways.
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04 · Motion Forecasting
Trajectory & Motion Forecasting
We develop methods for predicting the future trajectories and motion of agents in dynamic environments. Our work spans human trajectory forecasting and autonomous-driving scenarios, with a focus on multimodal prediction, interactions between agents, and modeling plausible future behavior in complex scenes.
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