Papers
9
Total Citations
59
H-Index
5
About
Yu Nishiyama is a researcher whose work bridges the frontiers of Bayesian inference and robotic medical ultrasound. His key research areas include nonparametric filtering, kernel-based probabilistic methods, and the development of autonomous ultrasound systems for healthcare. Nishiyama made significant contributions to kernel Bayesian inference, notably through the "Kernel Monte Carlo Filter" (2015, 16 citations) and "Monte Carlo Filtering Using Kernel Embedding of Distributions" (2014, 12 citations), which advanced state-space modeling by enabling filtering without explicit observation models. He also introduced the "Model-based kernel sum rule" (2020), further unifying kernel methods with probabilistic graphical models. In medical robotics, Nishiyama has pioneered compact and autonomous ultrasound systems, including a portable robot for home healthcare (2019) and a bed-type diagnostic robot (2019). His recent work on rib region detection (2023) and visual servoing for kidney imaging (2023) demonstrates a commitment to making ultrasound diagnostics more accessible and less operator-dependent. With a growing citation record and a portfolio that spans theoretical foundations to practical deployment, Nishiyama is a notable figure in both machine learning and medical robotics.
Research Focus
Key Achievements
Top Papers
- 1Filtering with State-Observation Examples via Kernel Monte Carlo Filter16 citations · 2015
- 2Monte Carlo Filtering Using Kernel Embedding of Distributions12 citations · 2014
- 3
- 4A VS ultrasound diagnostic system with kidney image evaluation functions8 citations · 2022
- 5Development of compact portable ultrasound robot for home healthcare6 citations · 2019
- 6Development of bed-type ultrasound diagnosis and therapeutic robot3 citations · 2019
- 7
- 8
- 9