Eric Baruch Gutiérrez Castillo

Eric obtained his Master's degree in Applied Mathematics in 2016 from Delft University of Technology in the Netherlands.

Eric obtained his Master’s degree in Applied Mathematics in 2016 from Delft University of Technology in the Netherlands. While there he did an internship in Deltares, a research institute dedicated to water and subsoil research, where he collaborated in the project “Tracking Plastic in the Ocean” for five months, which involved the modelling of global ocean currents for the prediction of plastic waste accumulation in the ocean surface. For the past two years he has worked as a researcher for the Mexican Institute of Oil, in Mexico City, where he has mostly focused in numerical analysis for different geophysical models. The most recent of these projects has been an effort to model low frequency acoustic waves in saturated elastic porous media, for oil reservoir simulation. He speaks four languages, has a passion for books and movies, and enjoys wall climbing, football and capoeira.

Research project title:
On primal-dual algorithms for non-smooth large-scale machine learning

Matthias Ehrhardt, Euan Spence

Project description:
Modern abundance of data motivates automated data analysis methods, such as those provided by machine learning. Classification models such as Support Vector Machines (SVM) belong to an extensive class of optimisation problems which present non-smooth and large scale problems. To this purpose, Eric is studying a wide class of iterative algorithms with two main features: first-order primal-dual algorithms, practical for non-smooth optimisation, and stochastic algorithms that can lower the computational workload and memory requirements for large scale models. The main focus of the project is to establish convergence for these algorithms, and it is expected that they can guarantee faster rates of convergence over state of the art methods.