PACM Colloquium

Joint PACM-CSML Colloquium: Mark Tygert, Facebook Research

Machine Learning at Facebook

Machine intelligence for processing big data sets is big business. A statistical mathematician's point of view has led to (1) effective large-scale principal component analysis and singular value decomposition, and (2) some theoretical foundations for convolutional networks (convolutional networks underpin the recent revolution in artificial intelligence).

PACM Colloquium: Martin Bazant, MIT

Electrokinetic Control of Interfacial Instabilities

This talk will describe three examples of interfacial dynamics – viscous fingering, deionization shock propagation, and dendritic electrodeposition – whose stability can be controlled by electrokinetic phenomena in charged porous media, as evidenced by both theory and experiments.  Potential applications include electrically enhanced oil recovery, water purification by shock electrodialysis, and energy storage with metal batteries.

PACM Colloquium: Nicolas Boumal, Princeton University

Handling non-convexity in low-rank approaches for semidefinite programming

A semidefinite program (SDP) is an optimization problem where one seeks to minimize a linear function of a positive semidefinite matrix X under linear constraints. SDPs have generated sustained interest since the nineties owing to their powerful expressiveness. Standard algorithms solve SDPs in polynomial time, but they fail in practice for large problems.

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