אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

A common observation in data-driven applications is that high dimensional data has a low intrinsic dimension, at least locally. Thus, when one wishes to work with data that is not governed by a clear set of equations, but still wishes to perform statistical or other scientific analysis, an optional model is the assumption of an underlying manifold from which the data was sampled. This manifold, however, is not given explicitly but we can obtain samples of it (i.e., the individual data points). In this talk, we will consider the mathematical problem of estimating manifolds from a finite set of samples, possibly recorded with noise. Using a Manifold Moving Least-Squares approach we provide an approximant manifold with high approximation order in both the Hausdorff sense as well as in the Riemannian metric (i.e., a nearly isometry). In the case of bounded noise, we present an algorithm that is guaranteed to converge in probability when the number of samples tends to infinity. The motivation for this work is based on the analysis of the evolution of shapes through time (e.g., handwriting or primates' teeth) and we will show how this framework can be utilized to answer scientific questions in paleontology and archaeology.

Bio:
Barak Sober is a Phillip Griffiths Assistant Research Professor of Mathematics at Duke University. He received his B.Sc in Mathematics and Philosophy (2009), M.Sc (2014), and Ph.D. (2019) in Applied Mathematics all from Tel-Aviv University. His research ranges between analysis of high dimensional data from a geometrical perspective and the development of machine learning and statistical methods to study art and history, for which he received the Dan-David Scholarship of 2020.