mathematics

Martin Wainwright (statistician)

Martin Wainwright (statistician) 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 Martin Wainwright (statistician) rather than just read about it. In short: Martin James Wainwright (born 1973) is a statistician and the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at the Massachusetts Institute of Technology (MIT), a position he has held since July 2022.

Key takeaways

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

Reference excerpt

Martin James Wainwright (born 1973) is a statistician and the Cecil H. Green Professor in Electrical Engineering and Computer Science and Mathematics at the Massachusetts Institute of Technology (MIT), a position he has held since July 2022. At MIT, Wainwright is also affiliated with the Laboratory for Information and Decision Systems, where he is a principal investigator, and a member of the Statistics and Data Science Center. Before joining MIT in 2022, Wainwright was part of the faculty at the University of California, Berkeley, where he held the Howard Friesen Chair at the time of his departure.

Education and career Wainwright earned a bachelor's degree in mathematics from the University of Waterloo in 1994 and a master's degree in vision science from Harvard University in 1998. In 2002, Wainwright completed his Ph.D. in electrical engineering and computer science at the Massachusetts Institute of Technology (MIT) under the supervision of Alan S. Willsky and Tommi S. Jaakkola. His dissertation, which he developed at the MIT Laboratory for Information and Decision Systems, was titled Stochastic processes on graphs with cycles: geometric and variational approaches. For his thesis, Wainwright received the electrical engineering and computer science department's George M. Sprowls Award for the best Ph.D. thesis in computer science. Following his Ph.D., Wainwright moved to the University of California, Berkeley, where he was a postdoctoral researcher for Michael I. Jordan from 2002 to 2004. In the fall of 2004, he joined the faculty at the University of California, Berkeley with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. In July 2022, he returned to MIT, becoming the Cecil H. Green Professor in both the Department of Electrical Engineering and Computer Science, where he is in the Faculty of Artificial Intelligence and Decision-Making, and the Department of Mathematics.

Awards and recognition Wainwright has received multiple awards for his work, including the COPSS Presidents' Award from the Committee of Presidents of Statistical Societies in 2014 for his "fundamental and ground-breaking contributions to high-dimensional statistics, graphical modelling, machine learning, optimization and algorithms ..., as well as new methodology with wide-ranging implications for numerous applications". From the Institute of Mathematical Statistics, he received a Medallion Award and Lecture in 2013 and the Blackwell Award and Lecture in 2017. He was awarded a Sloan Research Fellowship in 2005, National Science Foundation CAREER Award in 2006, and a Guggenheim Fellowship in 2024. Wainwright was selected as a Fellow of the Institute of Mathematical Statistics in 2014 "for his fundamental research in statistical machine learning and high-dimensional statistics".

Publications In addition to numerous peer-reviewed articles in journals and conference proceedings, Wainwright has (co-)authored the following three books:

Wainwright, Martin J.; Jordan, Michael I. (2008). "Graphical Models, Exponential Families, and Variational Inference". Foundations and Trends in Machine Learning. 1 (1–2): 1–305. doi:10.1561/2200000001. ISBN 978-1-60198-184-4.{{cite journal}}: CS1 maint: periodical has ISBN (link) Hastie, Trevor; Tibshirani, Robert; Wainwright, Martin (2015). Statistical Learning with Sparsity: the Lasso and Generalizations. Monographs on Statistics and Applied Probability. Vol. 143. New York: Chapman and Hall/CRC. doi:10.1201/b18401. ISBN 978-1-4987-1216-3. Wainwright, Martin J. (2019). High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge Series in Statistical and Probabilistic Mathematics. Vol. 48. Cambridge University Press. doi:10.1017/9781108627771. ISBN 978-1-108-49802-9.

References

External links 2014 interview with Martin Wainwright on the Simply Statistics blog by Jeffrey T. Leek, Roger Peng, and Rafael Irizarry

Worked examples

Example 1 — a first encounter with Martin Wainwright (statistician)

Start with the simplest possible case. Write down what Martin Wainwright (statistician) 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 Martin Wainwright (statistician) 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 Martin Wainwright (statistician) 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 Martin Wainwright (statistician)

In research: Martin Wainwright (statistician) 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 Martin Wainwright (statistician) 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: Martin Wainwright (statistician) is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1973 births, 21st-century statisticians, Fellows of the Institute of Mathematical Statistics, so understanding it makes those chapters shorter.
In everyday life: Look for Martin Wainwright (statistician) 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 Martin Wainwright (statistician) in 20 minutes

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

Frequently asked questions

What is Martin Wainwright (statistician) in simple terms?

Why does Martin Wainwright (statistician) 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 Martin Wainwright (statistician)?

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 Martin Wainwright (statistician).