Samuel J. Wang
Papers
1
Total Citations
17
H-Index
1
About
Samuel J. Wang is a leading researcher at the intersection of robotics, computer vision, and numerical optimization. His most impactful work centers on developing tools that accelerate and simplify the creation of high-performance robotic systems. Wang is best known as the creator of SymForce, a groundbreaking library for symbolic computation and code generation in robotics. This work, published in 2022 and already garnering 17 citations, addresses a critical bottleneck in the field: the trade-off between the flexibility of symbolic math and the speed of hand-tuned code. SymForce enables researchers to rapidly develop complex algorithms for tasks like visual odometry, motion planning, and control, then automatically generate blazing-fast, optimized code. This contribution is a game-changer for the robotics community, dramatically reducing development time while ensuring real-time performance. Wang’s research is essential reading for anyone building robust, efficient autonomy systems.
Research Focus
Key Achievements
Top Papers
- 1SymForce: Symbolic Computation and Code Generation for Robotics17 citations · 2022