Maks Ovsjanikov

Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France, and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs.

• Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes
• Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery

Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching.

Scientific Honors include:

• ERC Consolidator Grant (VEGA Project, 2023)
• ERC Starting Grant (2017)
• ACM SIGGRAPH 2023 Test-of-Time Award
• Best Paper Awards at 3DV 2021 and 3DV 2022

Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.