MIT Associate Professor Justin Solomon is using modern geometric techniques to solve thorny problems that often seem to have nothing to do with shapes.
For example, perhaps a statistician wants to compare two datasets to see how using one for training and the other for testing might impact the performance of a machine-learning model. The contents of these datasets might share some geometric structure depending on how the data are arranged in high-dimensional space, says Solomon. Comparing them using geometric tools can bring insight, for example, into whether the same model will work on both datasets.
"The language we use to talk about data often involves distances, similarities, curvature, and shape — exactly the kinds of things that we've been talking about in geometry forever. So, geometers have a lot to contribute to abstract problems in data science," he says.
From MIT News
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