Topics in Analysis and Geometric Measure Theory — NSF Award to Georgia Tech Research Corporation (GA, $253,498)
A basic principle in statistics and data science is that high-dimensional data can often be analyzed efficiently when it depends on only a few significant features. In analysis and geometric measure theory, an analogous question is whether an infinite set of points in space can be well-parameterized by a small number o
| Award title | Topics in Analysis and Geometric Measure Theory |
|---|---|
| Award ID | 2453251 |
| Awardee | Georgia Tech Research Corporation |
| City | ATLANTA |
| State | GA |
| Amount obligated | $253,498 |
| Principal investigator | Benjamin Jaye |
| Program | ANALYSIS PROGRAM |
| Start date | 08/01/2025 |
| Abstract | A basic principle in statistics and data science is that high-dimensional data can often be analyzed efficiently when it depends on only a few significant features. In analysis and geometric measure theory, an analogous question is whether an infinite set of points in space can be well-parameterized by a small number of variables—a property known as rectifiability. Determining whether a set is rectifiable is fundamental in analysis, and this project will develop new techniques to better understa |
| Source | NSF Awards |
$799/mo
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