Fondos para apoyar la realizacion de la Fourth Latin American Summer School on Robotics (LACORO 2025). La primera edición se realizó online en octubre de 2020; la segunda fue presencial en enero de 2023; la tercera 2024 en la Universidad de O'Higgins en Rancagua, Chile. La cuarta edición tendrá lugar en diciembre de 2025 en la Universidad de O'Higgins. https://lacoro.org/ Esta Escuela de Verano beneficiará principalmente a Estudiantes y Académicos de las Américas interesados en la Investigación en Inteligencia Artificial aplicada a la Robótica. Nuestro objetivo es fomentar la colaboración nacional y regional en esta área de investigación. Para la primera edición, alcanzamos 241 inscripciones para actividades online de todo el mundo, y la segunda versión tuvo 166 inscripciones para actividades presenciales en enero de 2023, principalmente de Chile, México, Argentina, Brasil y Uruguay.

Fondos para apoyar la realizacion de la Fourth Latin American Summer School on Robotics (LACORO 2025). La primera edición se realizó online en octubre de 2020; la segunda fue presencial en enero de 2023; la tercera 2024 en la Universidad de O'Higgins en Rancagua, Chile. La cuarta edición tendrá lugar en diciembre de 2025 en la Universidad de O'Higgins. https://lacoro.org/ Esta Escuela de Verano beneficiará principalmente a Estudiantes y Académicos de las Américas interesados en la Investigación en Inteligencia Artificial aplicada a la Robótica. Nuestro objetivo es fomentar la colaboración nacional y regional en esta área de investigación. Para la primera edición, alcanzamos 241 inscripciones para actividades online de todo el mundo, y la segunda versión tuvo 166 inscripciones para actividades presenciales en enero de 2023, principalmente de Chile, México, Argentina, Brasil y Uruguay.

The school is a crucial socialisation institution that forms young people’s perceptions, attitudes, and beliefs (Gidengil et al., 2016). Moreover, it is the main socialisation space that provides the youth with broader social relationships compared to the intimate social context of the family (Vincent et al., 2018). This Fondecyt project will ethnographically analyse the school’s role in socialising attitudes towards socioeconomic inequality among secondary students in classrooms with a diversified socioeconomic composition. Studying these attitudes is crucial since they might symbolically legitimise or challenge the existing social order (Sandel, 2021) and contribute to the reproduction or transformation of socioeconomic inequalities (Alesina et al., 2012). Since evidence on this issue is key for democratic governments in socioeconomically unequal societies to inform policies for reducing inequality and increasing social cohesion, the project focuses on Chile, a country with extreme levels of socioeconomic inequality (WID, 2022). By identifying opportunities and challenges for schools to promote egalitarian attitudes towards socioeconomic inequality among their socioeconomically diverse students, the project will provide decisive evidence for education policy to improve their support to school communities. The general objective of this project is to analyse how the schools contribute to shaping attitudes towards socioeconomic inequality among secondary students in classrooms that have diversified their socioeconomic composition. The specific objectives are as follows: 1. To characterise the socioeconomic heterogeneity within schools with secondary education in Chile and identify those that have diversified the socioeconomic composition of their students in the last decade. 2. To determine the schools’ pedagogical, organisational and community responses to the increase in their socioeconomic diversity and examine how these responses promote particular attitudes towards socioeconomic inequality. 3. To describe the students' inter-class relationships and identify how these relate to particular attitudes towards socioeconomic inequality, including awareness of inequality, perceptions of the fairness of social inequalities, meritocratic beliefs, and egalitarian preferences. The study will use a sequential mixed methods design, with a first quantitative phase aimed at answering Specific Objective 1 and informing the selection of the sample of schools for the second - ethnographic - phase, which is the predominant one and seeks to answer Specific Objectives 2 and 3: a) Quantitative phase: A Socioeconomic Diversity Index (SDI) will be created using data from the Quality of Education Agency through its SIMCE questionnaire to parents of secondary students for 2014-2024, which asks about the families’ socioeconomic information. Descriptive statistics will be used to describe SDI and its temporal variation by school. b) Ethnographic phase: Ethnographic fieldwork will be deployed in three schools with increased socioeconomic diversity, as measured in the SDI. Fieldwork in each school will last approximately two months, and the following methods will be used: - Interviews with staff members: 10 in each school (30 total), including the Principal, two Deputy Principals, three professionals from a psychosocial support team and four teachers. - Friendship maps and interviews with students: Maps of the students’ social relationships will be built with secondary students, seeking to include the whole class in each school (approximately 100 students in total). Maps will be followed by interviews. - Observations: They will be conducted in classrooms, playgrounds, and school events for approximately 100 hours in each school (approximately 300 hours of observation in total). The expected results include a) a map of all Chilean schools according to their current socioeconomic diversity and its temporal variation; b) in-depth evidence of inter-class relationships between secondary students and how these affect their attitudes towards socioeconomic inequality; and c) a systematisation of the schools’ responses to the students’ socioeconomic heterogeneity, their links to the development of attitudes towards socioeconomic inequality, and their facilitating and hindering elements. These findings will be useful for other schools that are diversifying their student body, wish to do so, or are interested in promoting egalitarian attitudes among their students. Ultimately, the project will provide critical evidence for public policy to promote schools that foster the development of egalitarian attitudes and potentially contribute to reducing social inequalities.

Combinatorial objects frequently appear in various areas of computer science and discrete mathematics. These objects are central to questions in algorithmic design, where we aim to program a computer to efficiently perform tasks involving them. These tasks may include counting objects based on certain parameters, sampling an object uniformly at random, optimizing with respect to an objective function, searching for objects that satisfy specific properties, or generating all objects exactly once. This project focuses on two of these problems: combinatorial generation and the search for highly distinct combinatorial objects. While many of the aforementioned tasks have general-purpose techniques that allow them to tackle multiple problems simultaneously, the situation becomes less clear when dealing with combinatorial generation or the search for distant objects. Much of the effort in these areas has been devoted to developing ad hoc methods. Despite this, these last two problems can be naturally phrased in the language of flip graphs, which encode the similarity between combinatorial objects. In this context, the problem transforms into the traditional graph problems of Hamiltonicity (finding a path that traverses all the vertices exactly once) and diameter (finding two vertices that are farthest apart). Recent research has highlighted the significant value of exploiting polytopal properties and symmetry of flip graphs, leading to unified frameworks that can address many problems simultaneously. The main objective of this project is to contribute to this perspective. Specifically, it aims to enhance our understanding of the polytopal and symmetric properties of flip graphs and use this knowledge to develop efficient algorithms for tackling Hamiltonicity and diameter problems

Producción Escalable de Inmunoestimulantes Nanoestructurados para el Control de Piscirickettsiosis en Salmón del Atlántico

El proyecto tiene como objetivo desarrollar e implementar modelos de aprendizaje automático (Machine Learning, ML) para analizar datos ómicos generados a partir de plantas agronómicas, con el fin de identificar patrones moleculares clave asociados a su productividad, resistencia a estreses abióticos (como sequía y salinidad) y bióticos (como plagas y enfermedades), y su adaptación a condiciones climáticas cambiantes.