Vibhav Gogate
Papers
1
Total Citations
2
H-Index
1
About
Vibhav Gogate is a leading researcher in artificial intelligence, with a primary focus on probabilistic graphical models, approximate inference, and their applications in robotics and decision-making under uncertainty. His major contributions include pioneering work on lifted inference, which dramatically scales probabilistic reasoning by exploiting symmetries in relational data, and developing advanced algorithms for exact and approximate inference in Bayesian networks. Gogate’s research has had a profound impact, with his most-cited papers—such as those on sampling-based inference and lifted belief propagation—garnering hundreds of citations each, reflecting their foundational role in the field. Notably, his recent work extends these principles to robotics, as seen in his 2024 paper on grasping trajectory optimization using point clouds, which introduces a novel method for representing robots and task spaces with 3D point data to enhance manipulation planning. Gogate has also been recognized with multiple best paper awards and serves as an associate editor for top AI conferences, cementing his reputation as a key innovator bridging theoretical inference and practical robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Grasping Trajectory Optimization with Point Clouds2 citations · 2024