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The field of optimization supplies crucial methodology for formulating and solving key problems in data science. Examples range from linear programming for support vector machines in the 1960s, to compressed sensing in the 2000s, and up to deep learning and matrix optimization problems today. Cross-fertilization between machine learning, statistics, and optimization has been remarkably beneficial for all areas. This workshop brings together researchers in both optimization and data science, broadly defined, with the aim of promoting further collaboration at the intersection.
Organisers: Nam Ho-Nguyen (University of Sydney) and Vera Roshchina (UNSW).
Registration is free, but places are limited due to covid restrictions. Please contact the organisers if you are interested in participating.
This workshop is a UNSW Data Science Hub (uDASH) event.
