The principal aim of robot perception is to infer useful information from the environment to enable precise, reliable, robust and safe robot execution. Perception is universal, and serves the core to recent 3D vision technologies, e.g., from virtual reality (VR) and augmented reality (AR) equipments to endoscopic surgical devices.

My work is to develop novel theoretical tools to solve perception in challenging scenarios. In specific, I have been conducting research on:

• perception in nonrigid scenes (e.g., in surgical devices),
• techniques to solve derived optimization instances,
• geometric methods and cameras (e.g., the rolling-shutter camera).

My research effort has resulted in several first-author publications on top rank journals in robotics, vision, and artificial intelligence, including T-RO 2021, T-PAMI 2022, IJCV 2022, and IJRR 2023.

Research interests: I enjoy fundamental research a lot aiming for permanent results that can live longer than ourselves. Orthogonally, I appreciate applied research that can improve human well-beings in the forthcoming years. I am broadly interested in geometric methods, optimization techniques, applied graph theory, sensor fusion, and machine learning technologies.

Aside from research, I enjoy traveling, visiting museums, and trying local food and drinks. I am a big fan of medieval buildings. I like hiking after daily work.