I am interested in the crossroads of machine learning, distributed/parallel computing, data analytics and mathematical modeling. I am passionate about scalable data-intensive techniques to analyze large datasets. I received my B.Sc. in Physics in 2002 from the National University of Mexico (UNAM), and my Ph.D in Computational Physics at the University of British Columbia, (with Matthew Choptuik) in Vancouver, Canada. My research and skills have benefited from several internships since my undergraduate years (CERN, Summer Program 1996), more recently at the Max-Planck Institute for Gravitational Physics, in Golm, Germany (2008) and The Perimeter Institute for Theoretical Physics (2010) in Waterloo, Canada. Before starting my doctoral studies I worked as a Linux server specialist at my home university's Institute for Mathematical Research, and later at Hewlett Packard Mexico. After my Ph.D. I worked for Gumstix Research Canada and then i took a postdoctoral position at the Argonne Leadership computing facility, in suburban Chicago, where I contributed to the analysis of large datasets from cosmological simulations using clustering methods. Later on I worked at Intel as a Linux Integration engineer, working with opensource technology, such as Openstack, and linux distributions optimized for cloud. Next i worked on HPC on clouds at ANSYS. I want to apply my experience with high performance computing and data-intensive workflows to cloud computing, computational fluid dynamics, bioinformatics, fraud detection and big data mining. Recently I have became interested in accelerating machine learning training using GPUs, Xeon Phi many cores and other spin-off techniques from HPC.