Institute for Mathematics and its Applications

Organization profile

Organization profile

The Institute for Mathematics and its Applications was established in 1982 by the National Science Foundation, as a result of a national competition. The Institute connects scientists, engineers, and mathematicians in order to address scientific and technological challenges in a collaborative, engaging environment, developing transformative, new mathematics and exploring its applications, while training the next generation of researchers and educators.

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Convex Body Mathematics
Tiling Mathematics
Surface area Mathematics
Feedback Engineering & Materials Science
Theorem Mathematics
Graph in graph theory Mathematics
Domino Tilings Mathematics
Log-concave Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.


No photo of Ankit Bansal

Ankit Bansal

Person: Professional-in-Training

No photo of Alexander S Estes

Alexander S Estes

Person: Professional-in-Training

No photo of Shannon R Negaard-Paper

Shannon R Negaard-Paper

Person: Professional-in-Training

Projects 2008 2020

EDT: Math-to-Industry Boot Camp

Santosa, F., Spirn, D. & Tolmasky, C.

National Science Foundation


Project: Research project


Institute for Mathematics and Its Applications

Spirn, D., Tolmasky, C., Wang, J. & Santosa, F.

National Science Foundation


Project: Research project

Partial differential equation
Numerical Methods

Research Output 1988 2019

On the entropy power inequality for the rényi entropy of order [0, 1]

Marsiglietti, A. & Melbourne, J. C., Mar 1 2019, In : IEEE Transactions on Information Theory. 65, 3, p. 1387-1396 10 p., 8502868.

Research output: Contribution to journalArticle

5 Citations (Scopus)

A copositive approach for two-stage adjustable robust optimization with uncertain right-hand sides

Xu, G. & Burer, S., May 1 2018, In : Computational Optimization and Applications. 70, 1, p. 33-59 27 p.

Research output: Contribution to journalArticle

Robust Optimization
Linear programming
Computational complexity
2 Citations (Scopus)

A data-driven distributionally robust bound on the expected optimal value of uncertain mixed 0-1 linear programming

Xu, G. & Burer, S., Jan 1 2018, In : Computational Management Science. 15, 1, p. 111-134 24 p.

Research output: Contribution to journalArticle

Linear programming