astronomy

Ming-Hsuan Yang

Ming-Hsuan Yang is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Ming-Hsuan Yang rather than just read about it. In short: 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.

Key takeaways

Ming-Hsuan Yang belongs to astronomy; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Ming-Hsuan Yang to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Ming-Hsuan Yang from memory before moving on to harder problems.

Reference excerpt

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. He is a fellow of the Institute of Electrical and Electronics Engineers (IEEE), 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 joined UC Merced in 2008. Since 2018, he has been a research scientist at Google DeepMind. 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 2010 – Distinguished Early Career Research Award, UC Merced 2012 – Faculty Early Career Development (CAREER) Award, NSF 2014 – Distinguished Research Award, UC Merced 2017 – Best Paper Honorable Mention, ACM Symposium on User Interface Software and Technology (UIST) 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 2021 – Fellow, ACM 2023 – Longuet-Higgins Prize, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2024 – Best Paper Award, International Conference on Machine Learning (ICML) 2025 – Fellow, AAAI 2025 – Test-of-Time Award, IEEE Winter Conference on Applications of Computer Vision (WACV) 2026 – Fellow, AAAS

Bibliography

Books Yang, Ming-Hsuan; Ahuja, Narendra (2012). Face Detection and Gesture Recognition for Human-Computer Interaction. Kluwer Academic Publishers. ISBN 9781461514237.

Selected articles Yang, M.-H.; Kriegman, D. J.; Ahuja, N. (2002). "Detecting faces in images: A survey". IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (1): 34–58. doi:10.1109/34.982883. Ross, D. A.; Lim, J.; Lin, R. S.; Yang, M.-H. (2008). "Incremental learning for robust visual tracking". International Journal of Computer Vision. 77 (1–3): 125–141. doi:10.1007/s11263-007-0075-7. Wu, Y.; Lim, J.; Yang, M.-H. (2013). "Online object tracking: A benchmark". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2411–2418. doi:10.1109/CVPR.2013.312. Lai, W. S.; Huang, J. B.; Ahuja, N.; Yang, M.-H. (2017). "Deep Laplacian pyramid networks for fast and accurate super-resolution". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 624–632. doi:10.1109/CVPR.2017.618. Gao, S. H.; Cheng, M. M.; Zhao, K.; Zhang, X. Y.; Yang, M.-H.; Torr, P. (2019). "Res2Net: A new multi-scale backbone architecture". IEEE Transactions on Pattern Analysis and Machine Intelligence. 43 (2): 652–662. arXiv:1904.01169. doi:10.1109/TPAMI.2019.2938758. PMID 31484108.

References

Worked examples

Example 1 — a first encounter with Ming-Hsuan Yang

Start with the simplest possible case. Write down what Ming-Hsuan Yang claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Ming-Hsuan Yang before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Ming-Hsuan Yang ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Ming-Hsuan Yang

In research: Ming-Hsuan Yang appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Ming-Hsuan Yang in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Ming-Hsuan Yang is common in secondary-school and first-year university syllabi. It links to neighbouring topics DeepMind people, Fellows of the Association for Computing Machinery, Fellows of the Association for the Advancement of Artificial Intelligence, so understanding it makes those chapters shorter.
In everyday life: Look for Ming-Hsuan Yang outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Ming-Hsuan Yang” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Ming-Hsuan Yang in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Ming-Hsuan Yang means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Ming-Hsuan Yang out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Ming-Hsuan Yang in simple terms?

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.

Why does Ming-Hsuan Yang matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Ming-Hsuan Yang?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Ming-Hsuan Yang.