mathematics

Ravindran Kannan

Ravindran Kannan 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 Ravindran Kannan rather than just read about it. In short: Ravindran (Ravi) Kannan (Tamil: ரவீந்திரன் கண்ணன்); born 12 March 1953, is a theoretical computer scientist. His work has mainly focused on efficient algorithms for problems of a mathematical (often geometric) flavor that arise in Computer Science, Machine Learning and Optimization.

Key takeaways

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

Reference excerpt

Ravindran (Ravi) Kannan (Tamil: ரவீந்திரன் கண்ணன்); born 12 March 1953, is a theoretical computer scientist. His work has mainly focused on efficient algorithms for problems of a mathematical (often geometric) flavor that arise in Computer Science, Machine Learning and Optimization. Ravi was a Principal Researcher at Microsoft Research Lab, India. Before joining Microsoft, he was William K. Lanman Jr. Professor of Computer Science and Professor of Applied Mathematics at Yale University. Prior to that he has been on the faculty of CMU and MIT. The ACM Special Interest Group on Algorithms and Computation Theory (SIGACT) presented its 2011 Knuth Prize to Ravi Kannan for developing influential algorithmic techniques aimed at solving long-standing computational problems. Ravi Kannan did his B.Tech at IIT, Bombay. He received his PhD in 1980 at Cornell University under Leslie Earl Trotter, Jr.

Awards and honors Joint Winner of the 1991 Fulkerson Prize in Discrete Mathematics for his work on the volumes of convex bodies. Knuth Prize 2011 for developing influential algorithmic techniques aimed at solving long-standing computational problems. In 2017 he became a Fellow of the Association for Computing Machinery. Member, American Academy of Arts and Sciences Member, National Academy of Sciences

Key contributions 1. Algorithm for approximating the volume of high dimensional convex sets via Markov Chains 2. Algorithms for Integer Programming the Frobenius Problem drawing on Geometry of Numbers 3. Randomized algorithms for Principal Component Analysis and Matrix Compression 4. Algorithmic version of the regularity lemma in Graph Theory

Selected works

Books 2020. Foundations of Data Science. (with Avrim Blum and John Hopcroft). 2009. Spectral Algorithms.(with Santosh Vempala)

Other representative publications "Clustering in large graphs and matrices," with P. Drineas, A. Frieze, S. Vempala and V. Vinay, Proceedings of the Symposium on Discrete Algorithms, 1999. "A Polynomial-Time Algorithm for learning noisy Linear Threshold functions," with A. Blum, A. Frieze and S. Vempala, Algorithmica 22:35–52, 1998. "Covering Minima and lattice point free convex bodies," with L. Lovász, Annals of Mathematics, 128:577–602, 1988.

See also Szemerédi regularity lemma Alan M. Frieze Avrim Blum László Lovász Santhosh Vempala

References

External links Ravi Kannan's home page Ravi Kannan at DBLP Bibliography Server Distinguished Alumni Awardees 1999, IIT Bombay Fulkerson Prize Award

Illustrations

Worked examples

Example 1 — a first encounter with Ravindran Kannan

Start with the simplest possible case. Write down what Ravindran Kannan 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 Ravindran Kannan 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 Ravindran Kannan 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 Ravindran Kannan

In research: Ravindran Kannan 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 Ravindran Kannan 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: Ravindran Kannan is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1953 births, 20th-century Indian mathematicians, 21st-century Indian mathematicians, so understanding it makes those chapters shorter.
In everyday life: Look for Ravindran Kannan 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 Ravindran Kannan in 20 minutes

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

Frequently asked questions

What is Ravindran Kannan in simple terms?

Why does Ravindran Kannan 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 Ravindran Kannan?

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 Ravindran Kannan.