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Michael Sipser

Michael Sipser 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 Michael Sipser rather than just read about it. In short: Michael Fredric Sipser (born September 17, 1954) is an American theoretical computer scientist who has made early contributions to computational complexity theory. He is a professor of applied mathematics and was the dean of science at the Massachusetts Institute of Technology.

Michael Sipser — main illustration
Michael Sipser — illustration

Key takeaways

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

Reference excerpt

Michael Fredric Sipser (born September 17, 1954) is an American theoretical computer scientist who has made early contributions to computational complexity theory. He is a professor of applied mathematics and was the dean of science at the Massachusetts Institute of Technology.

Biography Sipser was born and raised in Brooklyn, New York and moved to Oswego, New York when he was 12 years old. His grandparents were Jewish immigrants from Eastern Europe and his father was a professor of mathematics at SUNY Oneonta. He earned his BA in mathematics from Cornell University in 1974 and his PhD in engineering from the University of California at Berkeley in 1980 under the direction of Manuel Blum. He joined MIT's Laboratory for Computer Science as a research associate in 1979 and then was a Research Staff Member at IBM Research in San Jose. In 1980, he joined the MIT faculty. He spent the 1985–1986 academic year on the faculty of the University of California at Berkeley and then returned to MIT. From 2004 until 2014, he served as head of the MIT Mathematics department. He was appointed Interim Dean of the MIT School of Science in 2013 and Dean in 2014. He served as Dean until 2020, when he was succeeded by Nergis Mavalvala. He is a fellow of the American Academy of Arts and Sciences. In 2015 he was elected as a fellow of the American Mathematical Society "for contributions to complexity theory and for leadership and service to the mathematical community." He was elected as an ACM Fellow in 2017.

Scientific career Sipser specializes in algorithms and complexity theory, specifically efficient error correcting codes, interactive proof systems, randomness, quantum computation, and establishing the inherent computational difficulty of problems. He introduced the method of probabilistic restriction for proving super-polynomial lower bounds on circuit complexity in a paper joint with Merrick Furst and James B. Saxe. Their result was later improved to be an exponential lower bound by Andrew Yao and Johan Håstad. In an early derandomization theorem, Sipser showed that BPP is contained in the polynomial hierarchy, subsequently improved by Peter Gács and Clemens Lautemann to form what is now known as the Sipser–Gács–Lautemann theorem. Sipser also established a connection between expander graphs and derandomization. He and his PhD student Daniel Spielman introduced expander codes, an application of expander graphs. With fellow graduate student David Lichtenstein, Sipser proved that Go is PSPACE hard. In quantum computation theory, he introduced the adiabatic algorithm jointly with Edward Farhi, Jeffrey Goldstone, and Samuel Gutmann. Sipser has long been interested in the P versus NP problem. In 1975, he wagered an ounce of gold with Leonard Adleman that the problem would be solved with a proof that P ≠ NP by the end of the 20th century. Sipser sent Adleman an American Gold Eagle coin in 2000 because the problem remained (and remains) unsolved.

Notable books Sipser is the author of Introduction to the Theory of Computation, a textbook for theoretical computer science.

Personal life Sipser lives in Cambridge, Massachusetts with his wife, Ina, and has two children: a daughter, Rachel, who graduated from New York University, and a younger son, Aaron, who graduated from MIT.

References

External links Personal homepage at MIT

Illustrations

Michael Sipser illustration

Worked examples

Example 1 — a first encounter with Michael Sipser

Start with the simplest possible case. Write down what Michael Sipser 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 Michael Sipser 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 Michael Sipser 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 Michael Sipser

In research
Michael Sipser 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 Michael Sipser 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
Michael Sipser is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1954 births, 20th-century American engineers, 20th-century American mathematicians, so understanding it makes those chapters shorter.
In everyday life
Look for Michael Sipser 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 Michael Sipser in 20 minutes

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

Frequently asked questions

What is Michael Sipser in simple terms?

Michael Fredric Sipser (born September 17, 1954) is an American theoretical computer scientist who has made early contributions to computational complexity theory. He is a professor of applied mathematics and was the dean of science at the Massachusetts Institute of Technology.

Why does Michael Sipser 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 Michael Sipser?

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 Michael Sipser.

Tags

  • 1954 births
  • 20th-century American engineers
  • 20th-century American mathematicians
  • 21st-century American engineers
  • 21st-century American mathematicians
  • American computer science educators
  • American quantum information scientists
  • American theoretical computer scientists
  • Cornell University alumni
  • Fellows of the American Academy of Arts and Sciences
  • Fellows of the American Mathematical Society
  • Fellows of the Association for Computing Machinery

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