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About

Jianxiang Feng is a researcher whose work sits at the intersection of probabilistic machine learning, robotic perception, and assistive robotics. He is perhaps best known for his contribution to "A Survey of Uncertainty in Deep Neural Networks" (2023), a landmark review that has accumulated over 1,100 citations, establishing him as a leading voice in the field of uncertainty quantification for neural networks — a topic of growing importance as AI systems are deployed in safety-critical environments. Feng's research consistently addresses the challenge of making neural networks not only accurate but trustworthy and interpretable. His work on Bayesian approaches — including sparse Gaussian Processes combined with deep networks and Bayesian active learning for sim-to-real transfer — demonstrates a sustained commitment to principled probabilistic frameworks for real-world robotic applications. His introspective perception work further explores how robots can reason about the reliability of their own predictions. Beyond foundational machine learning, Feng has applied these ideas to meaningful human-centered problems, including assistive robotic systems designed to support people with severe motor impairments in performing everyday tasks. This breadth — from theoretical uncertainty estimation to socially impactful robotics — marks Feng as a researcher whose contributions bridge rigorous methodology with genuine real-world application.