Data & HPC

Directors: Daniel Remenik and Felipe Tobar

Researchers: Jorge Amaya, Joaquín Fontbona, Raúl Gouet, Alejandro Jofré, Alejandro Maass, Raúl Manásevich, Servet MartínezJaime San Martín
Scientists: Jocelyn DunstanSalvador FloresFrancisco FörsterGinés GuerreroAndrew HartJuan Carlos Maureira, Fabián Medel, Eduardo Vera
Postdocs: María Paz Cortés
Engineers:
 Guido Besomi, Eduardo CabreraRicardo ContrerasEugenio Guerra, Erick HormazábalCamilo IturraFermín Molina, Pablo Muñoz, Jorge PradoGonzalo Ríos, Iván Rojas, Patricio TorresDante Travisany, Paula Uribe

Data Science (DS) refers to the use of scientific and computational resources to extract intelligible information from datasets in various formats. Although the amount of available data may be large in general, this does not always have to be the case. DS comprises: (i) the acquisition, curation and transfer of data, (ii) the development and implementation of data analysis algorithms to transform datasets into information, and (iii) an interpretation of such information relying on expert knowledge in each relevant topic. Consequently, DS is inherently interdisciplinary, where areas such as high performance computing (HPC), mathematical modelling, machine learning, statistics, visualisation and expert knowledge cooperate towards a common goal: making sense of volumes of data which are unintelligible at first sight.

The focus of CMM-Data is on the design and implementation of innovative solutions for data-related challenges, seeking to make a contribution towards the improvement of processes within both the private and public sectors. In practice, our methodology is twofold: we implement and adapt solutions based on existing theory, and we also develop novel theoretical grounds to address situations for which off-the-shelf methods fail to deliver. Our field experience in applied DS includes projects in areas such as bio and astroinformatcis, marketing, security, finance, and audio processing, to name a few.

The key resource of CMM-Data is its multidisciplinary team of mathematicians, computer scientists, and engineers, who collaborate and interact with experts of fields relevant to the applications being developed. In this sense, our solutions comprise the entire DS spectrum, that is: the access, transformation, and curation of data; the modelling, implementation and execution of algorithms using HPC; the interpretation and visualisation of results; and finally the development of the software required for a standalone solution.

Lastly, in addition to the DS projects, CMM-Data offers postgraduate courses within the School of Engineering, as well as through the continuing education office, on Machine Learning, Scientific Computing, and High Performance Computing.

Some featured projects: