Proyectos

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Postulación a Fondecyt Regular[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Postulación a Fondecyt Regular[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Game theory and artificial intelligence are deeply connected fields, particularly when model- ing adversarial learning problems as Stackelberg games under uncertainty. In such settings, defenders allocate resources randomly to prevent attackers from anticipating their moves. As time evolves, the environment changes, and new strategies or actions may emerge. A suitable subfield of game theory to model these dynamics is stochastic games, where the state captures the set of available actions in a time-dependent process. Moreover, the attacker–defender inter- action naturally exhibits a hierarchical bilevel structure: the defender must design randomized strategies to protect targets, while the attacker responds by executing an action that maxi- mizes its reward. This makes stochastic dynamic Stackelberg games a model of choice for such applications [15], the Chilean party being very active in the domain [9, 10, 11]. In a previous collaboration [1], we have shown that defining an appropriate solution concept for these games is far from trivial, and in some cases remains an open research question. Even when a proper solution concept can be established, computing it becomes prohibitively expensive for games with massive state and action spaces. These challenges are not only of theoretical interest but also of high practical relevance. This associate team will focus on two among the many possible applications. Challenge 1: The first one is wildfire prevention. This theme has been widely examined in the literature, particularly through models that optimize the design of firebreaks [16], [12] and the the placement of surveillance cameras to limit fire spread. In contrast, the operational dimension of prevention and firefighting—where resources must be dynamically allocated un- der uncertainty—has received far less attention. Emerging distributed AI technologies with autonomous agents, both centralized (e.g., coordinated command-and-control systems) and de- centralized (e.g., drones and sensor swarms), could play a key role in addressing this challenge. Challenge 2: Similarly, in the domain of distributed AI learning systems (e.g. Federated Learning), a central authority is in charge of agregating the updates of distributed clients in the process of minimizing a global metric. The presence of Byzantine (or faulty) agents can derail convergence or induce a biais in this process. To mitigate this, robust agregation techniques have been proposed in the literature based on identifying and discarding suspect contributions [17, 18, 19]. But concrete attacks like ALIE [20] or FoE [21] tend to exhibit patterns. This lead researchers to propose the first history-aware defenses [22, 23, 24]. Purely deterministic rules, though, are likely to be defeated by powerful and well-informed agents. The attacker–defender framework is crucial to design secure training environments against adversarial manipulations, the principal issue being to decide if and when to exclude a suspected malicious agent. Both these applications can be tackeled with tools of Game Theory. More applications could be cited, like environmental conservation problems, such as poaching prevention [25], environmental tax evasion [26] or border patrol security [9].[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Game theory and artificial intelligence are deeply connected fields, particularly when model- ing adversarial learning problems as Stackelberg games under uncertainty. In such settings, defenders allocate resources randomly to prevent attackers from anticipating their moves. As time evolves, the environment changes, and new strategies or actions may emerge. A suitable subfield of game theory to model these dynamics is stochastic games, where the state captures the set of available actions in a time-dependent process. Moreover, the attacker–defender inter- action naturally exhibits a hierarchical bilevel structure: the defender must design randomized strategies to protect targets, while the attacker responds by executing an action that maxi- mizes its reward. This makes stochastic dynamic Stackelberg games a model of choice for such applications [15], the Chilean party being very active in the domain [9, 10, 11]. In a previous collaboration [1], we have shown that defining an appropriate solution concept for these games is far from trivial, and in some cases remains an open research question. Even when a proper solution concept can be established, computing it becomes prohibitively expensive for games with massive state and action spaces. These challenges are not only of theoretical interest but also of high practical relevance. This associate team will focus on two among the many possible applications. Challenge 1: The first one is wildfire prevention. This theme has been widely examined in the literature, particularly through models that optimize the design of firebreaks [16], [12] and the the placement of surveillance cameras to limit fire spread. In contrast, the operational dimension of prevention and firefighting—where resources must be dynamically allocated un- der uncertainty—has received far less attention. Emerging distributed AI technologies with autonomous agents, both centralized (e.g., coordinated command-and-control systems) and de- centralized (e.g., drones and sensor swarms), could play a key role in addressing this challenge. Challenge 2: Similarly, in the domain of distributed AI learning systems (e.g. Federated Learning), a central authority is in charge of agregating the updates of distributed clients in the process of minimizing a global metric. The presence of Byzantine (or faulty) agents can derail convergence or induce a biais in this process. To mitigate this, robust agregation techniques have been proposed in the literature based on identifying and discarding suspect contributions [17, 18, 19]. But concrete attacks like ALIE [20] or FoE [21] tend to exhibit patterns. This lead researchers to propose the first history-aware defenses [22, 23, 24]. Purely deterministic rules, though, are likely to be defeated by powerful and well-informed agents. The attacker–defender framework is crucial to design secure training environments against adversarial manipulations, the principal issue being to decide if and when to exclude a suspected malicious agent. Both these applications can be tackeled with tools of Game Theory. More applications could be cited, like environmental conservation problems, such as poaching prevention [25], environmental tax evasion [26] or border patrol security [9].[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]El objetivo principal de este proyecto es actualizar y profundizar el conocimiento sobre la distribución y prevalencia de triatominos vectores de Trypanosoma cruzi en la Región de O’Higgins, Chile. Esto se realizará a través del análisis de datos históricos y actuales de entidades de salud, complementado con muestreos intensivos de mamíferos silvestres y monitoreo de animales domésticos en áreas identificadas como de alto riesgo. Se explorarán además los focos históricos y zonas predichas como aptas para la presencia de triatominos, utilizando avanzadas técnicas de detección molecular y análisis de modelación. Este estudio busca no solo llenar un vacío de conocimiento sobre la presencia de estos vectores, sino también evaluar cómo los cambios climáticos y antrópicos podrían estar alterando su distribución geográfica, afectando así el riesgo de transmisión de la enfermedad de Chagas en la región.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]El objetivo principal de este proyecto es actualizar y profundizar el conocimiento sobre la distribución y prevalencia de triatominos vectores de Trypanosoma cruzi en la Región de O’Higgins, Chile. Esto se realizará a través del análisis de datos históricos y actuales de entidades de salud, complementado con muestreos intensivos de mamíferos silvestres y monitoreo de animales domésticos en áreas identificadas como de alto riesgo. Se explorarán además los focos históricos y zonas predichas como aptas para la presencia de triatominos, utilizando avanzadas técnicas de detección molecular y análisis de modelación. Este estudio busca no solo llenar un vacío de conocimiento sobre la presencia de estos vectores, sino también evaluar cómo los cambios climáticos y antrópicos podrían estar alterando su distribución geográfica, afectando así el riesgo de transmisión de la enfermedad de Chagas en la región.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Implementación de una ruta de turismo arqueológica y paleontológica que abarque tres comunas: San Vicente de Tagua Tagua, Navidad y San Fernando.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]Implementación de una ruta de turismo arqueológica y paleontológica que abarque tres comunas: San Vicente de Tagua Tagua, Navidad y San Fernando.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]We investigate how rain-mediated mechanical processes influence the spread of pathogens under field conditions. While it is well established that water is a primary vector for bacterial movement between plants, few studies have examined the detailed hydrodynamic mechanisms involved, particularly in the context of leaf morphology, surface roughness, and microbial adhesion. This gap restricts our ability to develop predictive models and preventive strategies for managing rain-borne plant diseases. The project's general objective is to elucidate the coupling between raindrop impact dynamics and bacterial dispersal patterns on cherry leaves under realistic rainfall conditions. Specifically, it aims to (i) characterize the mechanical interaction between raindrops and cherry leaves using high-speed imaging and physical analysis to observe the dispersal patterns of Pseudomonas syringae pv. syringae (Pss). (ii) evaluate the spatial dispersal of Pss inoculated artificially onto cherry leaves at different concentrations under controlled temperature and rainfall conditions, and (iii) develop an integrative predictive model based on physical variables of rain-leaf interaction and experimentally measured environmental conditions to estimate the dispersal and severity of Pss attack. Methodologically, our study combines high-speed photography, controlled laboratory rain simulations, and microbiological assays. We will perform experiments in a custom-designed rainfall simulator allowing precise control of droplet size, velocity, and impact angle. Bacterial suspensions of Pseudomonas syringae—a pathogen commonly associated with cherry canker—will be applied to leaves under standardized conditions. The dynamics of droplet impact, splash formation, and secondary droplet ejection will be recorded at high temporal resolution to quantify mechanical energy transfer and spatial distribution of splashed particles. Parallel microbiological analyses will determine bacterial survival rates, concentration profiles, and the extent of leaf-to-leaf contamination. We will integrate these results into a predictive model linking rainfall characteristics to potential bacterial dispersal distances and infection probabilities. We aim to enhance our understanding of the biophysical coupling between rainfall and pathogen mobility, establish a set of empirical relationships for disease spread modeling, and provide practical recommendations for orchard management under varying climatic scenarios. By bridging the gap between plant pathology and fluid mechanics, this project will provide a mechanistic foundation for reducing rain-mediated bacterial diseases in high-value fruit crops, contributing to the sustainability and resilience of O'Higgins agriculture.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]

[vc_section el_class="container mx-auto align-items-center circle--pattern" css=".vc_custom_1648956589196{padding-top: 3rem !important;}"][vc_row el_class="pb-5"][vc_column][vc_wp_custommenu nav_menu="6"][uoh_breadcrumb_component automatic_breadcrumb="true"][uoh_title_component title_dropdown="big" title_decorator="true"]{{title}}[/uoh_title_component][vc_column_text css=""]We investigate how rain-mediated mechanical processes influence the spread of pathogens under field conditions. While it is well established that water is a primary vector for bacterial movement between plants, few studies have examined the detailed hydrodynamic mechanisms involved, particularly in the context of leaf morphology, surface roughness, and microbial adhesion. This gap restricts our ability to develop predictive models and preventive strategies for managing rain-borne plant diseases. The project's general objective is to elucidate the coupling between raindrop impact dynamics and bacterial dispersal patterns on cherry leaves under realistic rainfall conditions. Specifically, it aims to (i) characterize the mechanical interaction between raindrops and cherry leaves using high-speed imaging and physical analysis to observe the dispersal patterns of Pseudomonas syringae pv. syringae (Pss). (ii) evaluate the spatial dispersal of Pss inoculated artificially onto cherry leaves at different concentrations under controlled temperature and rainfall conditions, and (iii) develop an integrative predictive model based on physical variables of rain-leaf interaction and experimentally measured environmental conditions to estimate the dispersal and severity of Pss attack. Methodologically, our study combines high-speed photography, controlled laboratory rain simulations, and microbiological assays. We will perform experiments in a custom-designed rainfall simulator allowing precise control of droplet size, velocity, and impact angle. Bacterial suspensions of Pseudomonas syringae—a pathogen commonly associated with cherry canker—will be applied to leaves under standardized conditions. The dynamics of droplet impact, splash formation, and secondary droplet ejection will be recorded at high temporal resolution to quantify mechanical energy transfer and spatial distribution of splashed particles. Parallel microbiological analyses will determine bacterial survival rates, concentration profiles, and the extent of leaf-to-leaf contamination. We will integrate these results into a predictive model linking rainfall characteristics to potential bacterial dispersal distances and infection probabilities. We aim to enhance our understanding of the biophysical coupling between rainfall and pathogen mobility, establish a set of empirical relationships for disease spread modeling, and provide practical recommendations for orchard management under varying climatic scenarios. By bridging the gap between plant pathology and fluid mechanics, this project will provide a mechanistic foundation for reducing rain-mediated bacterial diseases in high-value fruit crops, contributing to the sustainability and resilience of O'Higgins agriculture.[/vc_column_text][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649209804184{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5"][vc_row el_class="container mx-auto align-items-center p-md-0 pt-5"][vc_column el_class="p-0"][/vc_column][/vc_row][/vc_section][vc_section css=".vc_custom_1649210787516{background-color: #f6faff !important;}" el_class="p-md-0 pt-md-5 pb-md-5"][vc_row el_class="container mx-auto align-items-center"][vc_column][/vc_column][/vc_row][/vc_section]