MS-16 Linear Algebra and Positivity with Applications to Data Science (Positivity with Applications)

Organizers: Dominique Guillot (University of Delaware), Apoorva Khare (Stanford University), and Bala Rajaratnam (Stanford University)

Making sense of vast amounts of data has become one of the great challenges of the 21st century. This mini-symposium will highlight how recent advances in analysis (in particular positivity and related topics), linear algebra, algebraic geometry, and statistics provide new tools to analyze data in areas such as covariance estimation and the theory of graphical models.

More information regarding this mini-symposium can be found here


Mahya Ghandehari, LAA Early Career Speaker, Geometric graphs and uniform embeddings

Alexander Belton, A quantitative form of Schoenberg's theorem in fixed dimension

Peter Diao, Distribution-Free Consistency of Graph Clustering

Shaun Fallat, Hadamard Powers, Critical Exponents, and Total Positivity

Alfred Hero, Continuum limits for shortest paths

Ilse Ipsen, Randomized matrix-free trace and log-determinant estimators

Tanvi Jain, Hadamard powers of two classes of positive matrices

Helene Massam, Precision matrix estimation and sampling in coloured graphical Gaussian models

Nikolas Stott, Minimal upper bounds in the Loewner order: characterizations and parametrization.

Caroline Uhler, Your dreams may come true with MTP2

Cynthia Vinzant, Hyperbolicity and reciprocal linear spaces