· Awards and Honors · 3 min read
Prof. Jianshe Feng Selected for Measurement Science and Technology "Emerging Leaders 2026" Special Issue
Prof. Jianshe Feng has been selected for the "Emerging Leaders 2026" special issue of the internationally authoritative journal Measurement Science and Technology, in recognition of his outstanding contributions to multimodal spatiotemporal data modeling and industrial AI, demonstrating his growing academic influence as an emerging leader in the field.
Exciting news from AI Cube Lab! Prof. Jianshe Feng, director of the AI Cube Lab, has been selected for the “Emerging Leaders” special issue of the internationally authoritative journal Measurement Science and Technology (published by IOP Publishing, Impact Factor 3.4), joining the ranks of the world’s most outstanding early-career researchers in measurement and metrology in 2026.
About the Emerging Leaders Special Issue
Measurement Science and Technology annually identifies and compiles a curated selection of exceptional early-career researchers in the field of measurement and metrology into its “Emerging Leaders” special issue. Selection criteria are stringent: nominees must be recognized as leading researchers in the field and must have received their doctoral degree within the past ten years (excluding career interruptions). Inclusion in this special issue represents a strong endorsement from the academic community of a researcher’s innovative capacity and scholarly potential.
About Prof. Jianshe Feng
Prof. Jianshe Feng received his B.Eng. and M.Eng. in Mechanical Engineering from Tongji University (2012) and Zhejiang University (2015), respectively, and earned his Ph.D. in Mechanical Engineering from the University of Cincinnati, USA, in 2020. He is currently an Associate Professor at the School of Advanced Manufacturing, Sun Yat-sen University, and has been selected for the Shenzhen Overseas High-Level Talent Program (Peacock Plan).
His research focuses on multimodal spatiotemporal data modeling and industrial AI, with key applications in industrial process monitoring, fault prognostics, and process optimization. He is a young scholar with significant international influence in the domestic industrial AI community.
Featured Publication
The paper selected to represent Prof. Feng in the Emerging Leaders 2026 issue is:
Physically-informed Bayesian feature optimization for semi-supervised industrial anomaly detection Jianshe Feng et al, Measurement Science and Technology, 37, 116103 (2026)
This paper integrates physical prior knowledge with Bayesian feature optimization to propose a semi-supervised learning framework for industrial anomaly detection. The approach achieves significant improvements in detection performance under conditions of scarce labeled data, providing important methodological support for quality monitoring and health management in intelligent manufacturing.
Academic Significance
Measurement Science and Technology is one of the oldest and most influential journals in the field of measurement published by IOP Publishing, and its “Emerging Leaders” special issue enjoys a distinguished reputation in the international measurement and metrology community. Prof. Feng’s selection is not only a strong affirmation of his individual academic achievements, but also further highlights the international competitiveness of the AI Cube Advanced Manufacturing Lab at Sun Yat-sen University in the areas of industrial intelligent sensing and condition monitoring.
For members of the laboratory, this recognition vividly demonstrates the lab’s deep academic foundations at the frontier of industrial AI, and serves as an inspiration for students to strive for research excellence and aspire to world-class scholarship.
