Alex Andonian

Papers

Total Citations

277

H-Index

About

Alex Andonian is a researcher at the forefront of computer vision and embodied AI, with a focus on enabling machines to perceive and interact with complex 3D environments. His most influential work introduces "Cross-View Semantic Segmentation," a novel visual task that allows robots to sense their surroundings by integrating multiple camera perspectives into a unified, semantically labeled spatial map. This foundational paper has garnered over 277 citations, underscoring its impact on autonomous navigation and scene understanding. Andonian’s contributions extend to multimodal learning and generative models, where he has advanced techniques for aligning vision and language representations. Notably, his research bridges the gap between 2D perception and 3D reasoning, providing critical tools for robotics and augmented reality. With a knack for tackling challenging, real-world sensing problems, Andonian continues to shape how AI systems build coherent spatial awareness from fragmented visual data—a key step toward truly autonomous agents.