Ming-Hsuan Yang

Ming-Hsuan Yang is a computer scientist, academic, and author. He is a professor at the University of California, Merced, and a research scientist at Google DeepMind.

Yang's work is focused on computer vision, machine learning, artificial intelligence, and robotics. Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), and American Association for the Advancement of Science (AAAS).

Education and career Yang received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign.

Yang worked as a senior research scientist at the Honda Research Institute in Mountain View, California. He previously chaired the IEEE International Conference on Computer Vision (ICCV) and the Asian Conference on Computer Vision (ACCV).

Research Much of Yang's research has explored intelligent systems such as AI, machine learning, computer vision, and robotics. In a paper published in 2013, Yang assessed online object tracking algorithms through large-scale experiments, identifying methods, benchmarking performance, and highlighting key factors influencing tracking accuracy across different scenarios. He also presented a graph-based manifold ranking approach for saliency detection, integrating foreground and background cues, and benchmark dataset evaluation.

Yang has been named a highly cited researcher from 2018 to 2025.

Awards and honors 1999 – Ray Ozzie Fellowship, The Grainger College of Engineering 2009 – Google Faculty Award, Google 2012 – Faculty Early Career Development (CAREER) Award, NSF 2014 – Distinguished Research Award, UC Merced 2017 – Nvidia Pioneer Research Award 2018 – Best Paper Honorable Mention, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2018 – Nvidia Pioneer Research Award 2018 – Best Student Paper Honorable Mention, Asian Conference on Computer Vision (ACCV) 2019 – Fellow, IEEE 2024 – Best Paper Award, International Conference on Machine Learning (ICML) 2025 – Fellow, AAAI 2026 – Fellow, AAAS