Sandra Patricia Castillo Landínez

• • Led the management of the "Artificial Intelligence for the Judicial Branch" and "Judicial Map" projects, connecting strategic vision, technical-functional execution, and institutional alignment to drive data and artificial intelligence-based modernization initiatives.

• • Directed the prioritization, feasibility assessment, and technical implementation of 6 artificial intelligence use cases, articulating mission needs, data availability, scalability criteria, and adoption feasibility.

• • Exercised legal-technical leadership in structuring official responses on the use of artificial intelligence, providing technical and institutional clarity on petitions for information and requests for information.

• • Managed the evaluation and strategic acquisition of Copilot as an enabler for the development of artificial intelligence-assisted solutions in judicial offices, strengthening the innovation and modernization agenda of judicial work.

• • Acted as a bridge between institutional priorities, technological possibilities, and implementation conditions, contributing to consolidating a data and artificial intelligence roadmap with a focus on adoption and sustainability.

• • Led the technical evaluation of operational codes and algorithms, identifying areas for improvement and failure points to enhance stability, maintainability, and scalability.

• • Implemented containerization strategies with Docker to optimize the architecture of existing solutions, facilitate their technical evolution, and improve knowledge transfer between teams.

• • Structured technical documentation for architecture, processes, and developments to increase traceability, replicability, and technical knowledge governance.

• • Organized and delivered digital and documentary assets under operational continuity and compliance criteria, strengthening the sustainability of developments and the orderly transition of capabilities.

• • Led the Data Analysis and Processing line of the MEDES research group, articulating research, training, and academic development to strengthen institutional capabilities in data science and applied research.

• • Coordinated the Data Mining seed group, fostering talent training in advanced analytics and consolidating learning environments oriented towards academic production, applied research, and project development.

• • Designed and taught database courses and data science electives, translating technical concepts into high-impact learning experiences and strengthening analytical capabilities in students and academic teams.

• • Advised on degree theses and research projects, contributing methodological rigor in design, data analysis, and results construction.

• • Generated scientific production in data mining, learning analytics, and sentiment analysis, contributing to the line's academic positioning through publications and participation in specialized events.

• • Represented institutional research processes as a speaker, increasing the visibility of results and consolidating exchange networks in data analytics and evidence-based transformation.