Proyectos

Objetivo general: contribuir con información y metodología de estudio inexistente sobre la ubicación y la estimación de las reservas en agua subterránea para descubrir nuevos acuíferos, monitorear y gestionar su rol hídrico, especialmente en periodo de sequía, en la cuenca de Limarí, en la Región de Coquimbo. Objetivos específicos: (i) identificar áreas donde los acuíferos se encuentran bajo fuertes presiones antropogénicas o naturales y donde hay un alto grado de incertidumbre sobre los recursos subterráneos; (ii) en cada punto identificado, evaluar el contenido en agua de los acuíferos, sus bases y sus techos con la técnica de Magnetic Resonance Sounding (MRS); (iii) contribuir en la elaboración de una metodología para determinar las reservas en agua subterránea; (iv) construir un modelo tridimensional de las reservas de agua subterránea presentes en la región; (v) diseñar un plan que permita monitorear y gestionar eficientemente los acuíferos de la zona como recurso hídrico económico-social de la cuenca del Limarí.

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Fondecyt postdoctorado N° 3200806 Análisis de la dieta de los mamíferos extintos que vivieron en Chile Central durante el Pleistoceno

Fondecyt postdoctorado N° 3200806 Análisis de la dieta de los mamíferos extintos que vivieron en Chile Central durante el Pleistoceno

Market design plays a key role in contexts where asymmetric information is relevant and there are di�culties arising from the complexities of each particular market. Moreover, in real world situations, it is important to bridge the gap between the theoretical insights developed through mechanism design and the implementability issues stressed in the market design literature. Real world situations might be complex for di↵erent reasons, and in this proposal I plan to study two issues which up to now have not been fully analyzed in this theoretical/applied interface. The first is regulation in the presence of vertical structures, an issue which is very common in industrial organization, but it has received surprisingly little attention in the mechanism design literature. The second is the design of new markets for natural resources, where property rights are crucial to their e�cient use, but dynamic issues make standard simple solutions very di�cult to implement and they usually perform poorly. Curbing the monopoly power of firms is a fundamental issue for regulators where the cost structure makes a monopoly unavoidable in the first place. In particular, the study of simple mechanisms that help with this goal becomes relevant. Firms are better informed about its production cost or the demand that they faced. This information gives them infor- mational rents that the regulators attempt to control to increase social welfare. While this is a well-studied problem by economists, it still generates challenges due to the complexity of real world situations. The granting of a monopoly right and the establishment of certain rules (like a quantity to produce or a maximum price to be set) is not the end of the story. Subsequently, the regulated firm will deal with downstream firms, which in turn might have significant market power in its dealings with consumers and even providers. In the first project, I study how an agent designs a mechanism to allocate resources that afterwards turns the chosen firm downstream into a mechanism designer on its on. Applications include infrastructure regulation: for example airports that in turn chose how to deal with airlines, which use their market power to extract rent from consumers. Creating markets under the “tragedy of the commons” has a long tradition in economics, dating back to Pigou (1920) . The basic intuition is simple: prices would provide agents with the right scarcity signals, leading to an e�cient resource allocation. However, dynamic issues and the uncertainty about the real value of di↵erent uses for the resource introduce significant challenges. On the second project, I study e�cient mechanisms in a dynamic framework where the value of the asset is known only by experimentation of investors. A general objective of this project is to study the conditions for which simple mechanism can be e�cient on one hand, and furthermore they increase the social welfare.

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 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 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.