astronomy

Kaiming He

Kaiming He 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 Kaiming He rather than just read about it. In short: Kaiming He (Chinese: 何恺明; pinyin: Hé Kǎimíng) is a Chinese computer scientist who primarily researches computer vision and deep learning. He is an associate professor at Massachusetts Institute of Technology and works part-time as a Distinguished Scientist at Google DeepMind.

Key takeaways

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

Reference excerpt

Kaiming He (Chinese: 何恺明; pinyin: Hé Kǎimíng) is a Chinese computer scientist who primarily researches computer vision and deep learning. He is an associate professor at Massachusetts Institute of Technology and works part-time as a Distinguished Scientist at Google DeepMind. He is known as one of the creators of the residual neural network (ResNet) architecture.

Early life and education He attended the public Guangzhou Zhixin High School in Guangzhou, Guangdong, China. He scored first place for the total scores in the 2003 Guangdong provincial undergraduate admissions exam. He went to Tsinghua University for undergraduate education and received a Bachelor of Science degree in 2007. In 2007 to 2011, he pursued doctoral studies in information engineering at the Chinese University of Hong Kong at its Multimedia Laboratory, receiving a PhD degree in 2011. His doctoral dissertation was titled Single image haze removal using dark channel prior (2011), and his doctoral adviser was Tang Xiao'ou.

Career He worked at Microsoft Research Asia from 2011 to 2016 and at Facebook Artificial Intelligence Research from 2016 to 2024. In 2024, he became an associate professor at the Department of Electrical Engineering and Computer Science of the Massachusetts Institute of Technology. His 2016 paper Deep Residual Learning for Image Recognition is the most cited research paper in 5 years according to Google Scholar's reports in 2020 and 2021.

Awards and recognitions He won ICCV's best paper award (Marr Prize) in 2017 and CVPR's best paper award in 2009 and 2016. He was awarded the 2023 Future Science Prize along with 3 collaborators for "fundamental contribution to artificial intelligence by introducing deep residual learning".

References

External links Home page Kaiming He publications indexed by Google Scholar

Worked examples

Example 1 — a first encounter with Kaiming He

Start with the simplest possible case. Write down what Kaiming He 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 Kaiming He 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 Kaiming He 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 Kaiming He

In research: Kaiming He 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 Kaiming He 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: Kaiming He is common in secondary-school and first-year university syllabi. It links to neighbouring topics Alumni of the Chinese University of Hong Kong, Computer vision researchers, Future Science Prize recipients, so understanding it makes those chapters shorter.
In everyday life: Look for Kaiming He 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.

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How to study Kaiming He in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Kaiming He 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 Kaiming He out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Kaiming He in simple terms?

Why does Kaiming He 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 Kaiming He?

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 Kaiming He.