Shih Yu Chang
Papers
2
Total Citations
31
H-Index
2
About
Dr. Shih Yu Chang is a leading researcher at the intersection of advanced signal processing, control theory, and machine learning. His work is distinguished by the innovative fusion of classical estimation algorithms with modern computational intelligence. Dr. Chang’s most notable contribution is the development of the **Tensor Kalman Filter**, a groundbreaking extension of the traditional Kalman filter designed to handle high-dimensional, multi-modal data. This work, published in 2022 and garnering 20 citations, provides a robust framework for state estimation in complex systems, with applications ranging from environmental science to financial analysis and robotics. Demonstrating a practical command of control systems, Dr. Chang also pioneered the application of **Reinforcement Learning** to achieve self-balancing control in two-wheeled robots. His 2016 paper on this topic, with 11 citations, details a successful implementation on the BeagleBone Black platform, showcasing how AI can manage inherently unstable, non-linear dynamics. Through these contributions, Dr. Chang bridges theoretical rigor with tangible engineering solutions, offering powerful tools for researchers and engineers tackling estimation and control challenges in an increasingly data-rich world.
Research Focus
Key Achievements
Top Papers
- 1Tensor Kalman Filter and Its Applications20 citations · 2022
- 2Using Reinforcement Learning to Achieve Two Wheeled Self Balancing Control11 citations · 2016