mathematics

Marloes Maathuis

Marloes Maathuis is a mathematics 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 Marloes Maathuis rather than just read about it. In short: Marloes Henriette Maathuis (born 1978) is a Dutch statistician known for her work on causal inference using graphical models, particularly in high-dimensional data from applications in biology and epidemiology. She is a professor of statistics at ETH Zurich in Switzerland.

Key takeaways

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

Reference excerpt

Marloes Henriette Maathuis (born 1978) is a Dutch statistician known for her work on causal inference using graphical models, particularly in high-dimensional data from applications in biology and epidemiology. She is a professor of statistics at ETH Zurich in Switzerland.

Education and career Maathuis is originally from Groningen, the daughter of a physician. She studied applied mathematics at the Delft University of Technology, earning a bachelor's degree in 2001 and a master's degree in 2003. Her master's program included travel to Ethiopia to study the lifetime risks of HIV-related deaths there. With the assistance of Delft University professor Piet Groeneboom, Maathuis traveled to the University of Washington to work with Jon A. Wellner and complete her master's thesis. She stayed at the University of Washington for Ph.D. in statistics, completed in 2006, and then for an additional year as an acting assistant professor. Her doctoral dissertation, Nonparametric Estimation for Current Status Data with Competing Risks, was jointly supervised by Groeneboom and Wellner. She joined ETH Zurich as an untenured assistant professor of applied mathematics in 2007. In 2013, following the creation of a professorship in statistics at ETH Zurich, she was named an associate professor of statistics, as an early replacement for a retiring professor. She was promoted to full professor in 2016.

Recognition Maathuis is a Fellow of the Institute of Mathematical Statistics, elected in 2017. In 2020, with Daniel Dadush, she won the Van Dantzig Award of the Netherlands Society for Statistics and Operations Research (VVSOR), "the highest Dutch award in statistics and operations research". She is the 2021 winner of the Ethel Newbold Prize of the Bernoulli Society.

References

External links Home page Marloes Maathuis publications indexed by Google Scholar

Worked examples

Example 1 — a first encounter with Marloes Maathuis

Start with the simplest possible case. Write down what Marloes Maathuis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Marloes Maathuis 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 Marloes Maathuis 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 Marloes Maathuis

In research: Marloes Maathuis appears in mathematics 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 Marloes Maathuis 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: Marloes Maathuis is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1978 births, 21st-century statisticians, Academic staff of ETH Zurich, so understanding it makes those chapters shorter.
In everyday life: Look for Marloes Maathuis 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 Marloes Maathuis in 20 minutes

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

Frequently asked questions

What is Marloes Maathuis in simple terms?

Why does Marloes Maathuis matter?

Because it connects several mathematics 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 Marloes Maathuis?

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 Marloes Maathuis.