Papers
8
Total Citations
155
H-Index
4
About
Yutong Ban is a researcher whose work spans two compelling domains: multi-person tracking in human-robot interaction and AI-driven surgical workflow analysis. With a career that bridges computer vision, probabilistic modeling, and medical AI, Ban has made meaningful contributions to how machines perceive and anticipate human activity. Early in their career, Ban developed variational Bayesian models for multi-person tracking, with their 2016 paper earning 68 citations and establishing a strong foundation in probabilistic approaches to dynamic scene understanding. This work extended naturally into audio-visual fusion, where Ban demonstrated how combining auditory and visual cues enhances tracking robustness in challenging environments — a particularly valuable contribution to human-robot interaction research. More recently, Ban has pivoted toward surgical AI, developing innovative architectures like SUPR-GAN and the Hypergraph-Transformer to anticipate and understand intraoperative surgical events in laparoscopic and robotic procedures. These contributions move beyond passive recognition toward predictive assistance, positioning AI as an active safety partner in the operating room. Collectively amassing over 150 citations, Ban's research reflects a consistent ambition: designing intelligent systems that not only observe complex human activities but anticipate them — whether on a factory floor, in a social space, or inside a surgical suite.
Research Focus
Key Achievements
Top Papers
- 1Tracking Multiple Persons Based on a Variational Bayesian Model68 citations · 2016
- 2
- 3
- 4Tracking a varying number of people with a visually-controlled robotic head18 citations · 2017
- 5Audio-Visual Variational Fusion for Multi-Person Tracking with Robots4 citations · 2019
- 6
- 7
- 8