Los laboratorios de fabricación digital son espacios que cuentan con maquinaria y personal capacitado para facilitar el diseño y desarrollo de prototipos y para promover la innovación en productos, procesos y servicios. Se conciben como laboratorios que facilitan herramientas de fabricación avanzada y capacidades a la comunidad en general, pudiendo ser más enfocados a emprendedores, empresas e institutos de investigación. Una característica común es que sirven como plataforma para estimular el aprendizaje y la invención en la comunidad. Las máquinas y capacidades técnicas instaladas en estos laboratorios brindan la oportunidad de encontrar soluciones innovadoras a problemas comunes y ser incubadores de microemprendimientos que resuelvan problemas de forma innovadora y sustentable.

El primer laboratorio de fabricación digital, junto con el concepto FabLab, aparece en el MIT (Massachussets Institute of Technology, Estados Unidos) en el año 2000. Actualmente, existe una red mundial de alrededor de 3000 FabLabs distribuidos en 5 continentes. En Chile se pueden encontrar 17 de estos laboratorios, la mayoría de ellos concentrados en la Región Metropolitana; 2 en la Región del Maule y ninguno en la Región de O’Higgins. La ausencia de un laboratorio regional está en concordancia con estadísticas del año 2016 que reportan apenas 118 m2 de espacios dedicados a innovación en la Región de O’Higgins frente a 27 936 m2 en la Región Metropolitana. En ese contexto, la Región de O’Higgins es la segunda región con menor superficie dedicada a innovación.

La instalación de un laboratorio de fabricación digital en la Región de O’Higgins se identifica como una gran oportunidad para promover la innovación, brindando acceso a equipos y a capacitaciones sobre herramientas de fabricación avanzada a industrias y emprendedores regionales.

Problems that cannot be solved by classical computers in reasonable time due to their high computational cost arise in many research areas. In general, the evaluation of conjunctive queries over relational databases belongs to those problems. Conjunctive queries form the core of the Structured Query Language (SQL) which became a de facto standard for querying and maintaining relational databases. This work is about developing new approximation techniques for conjunctive queries which cannot be evaluated in reasonable time. Our new approximation techniques should lead to significant improvements for data aided decision making, e.g., for early warning system which are based on the analysis of big data or to make business-critical decisions by analyzing big data. In the last decades, a very good understanding of the classes of conjunctive queries which can be evaluated in reasonable time has been gained and it has been proven that an under-approximation of a query always exists within each of those classes. However this approach is rather strict and some of the under-approximations can be rather uninformative, i.e., the under-approximation might return the empty result set while the original query would not. over-approximations might be helpful when this happens, as they return all answers to a query. One of our goals is to study the foundational aspects of over-approximations, including the existence problem and the problem of computing an approximation. Unfortunately, over-approximations do not always exist (within a class of queries which can be evaluated in reasonable time), and it is not even known to be decidable whether a conjunctive query admits an over-approximation. Therefore, another goal of the proposed work is the development of more liberal approximation techniques that yield some kind of quantitative guarantees. This means that they should guarantee that the result of the approximation is not too “far” from the result of the original query over a set of databases of interest. Therefore we need to define a measure of disagreement between queries and/or results. For conjunctive query evaluation, such measures do not exist up until now. Based on that measure, we study approximations whose disagreement with the result of the query they approximate is below a certain threshold. Furthermore, we investigate how the underlying data of a database can help us to find better approximations.
It has been shown that there are close relations between the approximation of conjunctive queries over relational databases and some classes of Semantic Web queries over semi-structured data. We also study possible connections between our approximation techniques and approximating Semantic Web queries.

In general, machine learning aims to learn a model from the input data in order to make reliable and repeatable decisions. The learning of a model is either done automatically or semiautomatically. While deep learning can be used to automatically learn a model from arbitrary raw data, the number of successful application domains is still very restricted.
This proposal is concerned with supervised learning – a machine-learning technique that aims at learning a model from input-output examples. A crucial task in supervised learning is the engineering of the features. Features are used to extract the relevant information from the raw data in order to learn a classifier that is based on the extracted data. A classifier is a function that partitions the input data into different categories. Feature engineering is a time-consuming process that includes a lot of trial and error, and stepwise addition or deletion of features. We aim at automating that process and learn a classifier based on some automatically generated features.

The research project “Matrices, Optimization, and Randomness with Applications in Data Science (MORA-DataS)” is composed of research teams from Bolivia, Chile, France, and Peru. This project is funded by the regional program MATH-Amsud in cooperation with UMSA (Bolivia), ANID, CMM (Chile), MEAE (France), and CONCYTEC (Peru).
The aim of this project is to study diverse optimization models, deterministic and stochastic, and to investigate various problems in matrix analysis with potential applications in data science. Some of the research topics are computing angles between convex cones, inverse eigenvalue problems, proximal algorithms for symmetric cone optimization, nonlinear second-order cone programming problems, nonsmooth joint chance constrained optimization problems, and Euclidean Jordan algebras for optimization.

The field of remote sensing is experiencing an unprecedented acceleration. Besides the large public programs such as Sentinel (see e.g. https://sentinel.esa.int/web/sentinel/missions/sentinel-2), private actors are creating fleets of micro-satellites capable of monitoring of the earth with daily revisits. This abundant and cheap data is creating opportunities for developing novel applications for the monitoring of industrial and agricultural activity. The automatic exploitation of this data is bound to specific application domain knowledge, which requires a mastery of advanced techniques such as computer vision and machine learning, as well as expert knowledge in the field of agriculture. To do this, the team must master earth observation satellites, be able to define the adequate mathematical detection theories, and build on a deep knowledge of satellite image processing, while also including expert knowledge in agriculture. This project aims at uniting competences across the fields of computer vision and machine learning, remote sensing to address emerging applications in agronomy. This project will in addition foster the creation of reproducible research by adopting a reproducible research methodology thus contributing the resulting algorithms to the journal Image Processing On-Line (IPOL). The IPOL journal is an initiative to establish a clear and reproducible state-of-the-art in the domain of image processing and computer vision.

The project deals with commutation principles in Euclidean Jordan Algebras, Normal Decomposition systems and Fan-Theobald-von Newman systems. It propose to deal with the generalization of these principles and the application to variational analysis and the Marcus-de Oliveira determinantal conjecture.