Teaching & Academic Leadership

My teaching activity is centered on computer engineering, distributed systems, cloud and edge computing, data engineering and digital technologies. I combine solid foundations with hands-on experimentation, real research and technology-transfer cases, and learning-by-doing methodologies that bring students close to current professional and scientific practice.

2015–Coordinator, Master in Big Data Engineering

Lab-firstHands-on, learning-by-doing methodology

Research → ClassIntegration of project results into teaching

InternationalErasmus+ and European collaboration in the classroom

Main Teaching Areas

Distributed Systems Architecture

Undergraduate · Computer Engineering

Distributed-system fundamentals including shared-nothing and shared-memory models, mutual exclusion, concurrency control, logical clocks, distributed synchronization, consensus, fault tolerance, recovery and message-based communication.

Distributed SystemsConsensusFault Tolerance

Operating Systems Administration

Undergraduate · Computer Engineering

Redesigned from a lecture-centered course into a laboratory-driven subject. Students prepare core concepts before class, solve practical administration exercises and are evaluated through continuous weekly lab work. Contents are aligned with professional Linux administration skills.

LinuxLPIC-orientedLearning by Doing

Digital Systems & Microprocessors

Undergraduate · Core subject

A large foundational subject taught across several degrees. More than 80% of sessions follow a laboratory format, with a flipped-classroom approach in the first part and practical design and construction of microcontroller-based systems.

MicrocontrollersFlipped ClassroomLaboratory

Data Analytics

Master in Big Data Engineering

Advanced data-analysis concepts taught through real cases, datasets and challenges coming from funded research and technology-transfer projects, connecting methodological learning with realistic engineering constraints.

Big DataData AnalyticsResearch-led Teaching

Databases

Undergraduate · Computer Science & Multimedia

Updated to emphasize database interaction, querying and design through practical work. Approximately half of the teaching activity is carried out in the laboratory to reinforce conceptual knowledge with direct implementation.

SQLDatabase DesignPractice-oriented

Technology Trends

Undergraduate / Cross-disciplinary

Development of a cloud-computing module that transfers knowledge and cases from research and innovation projects into the classroom, offering students a current view of cloud architectures and their evolution.

Cloud ComputingEmerging TechnologiesProject-based Content

Teaching Approach

Learning by Doing

I progressively redesign technical subjects so that students spend more time building, configuring, measuring, debugging and making engineering decisions. Theory is used as a tool to solve practical problems rather than as an isolated final objective.

Research-led Teaching

Results, datasets, architectures and challenges from active research and technology-transfer projects are systematically translated into teaching materials, exercises and case studies. This keeps course content connected with current scientific and industrial practice.

Internationalisation

European projects and international academic networks are used to introduce multilingual resources, partner-developed materials, international cases and collaborative activities that expose students to different educational and professional contexts.

Continuous Improvement

Course design is treated as an iterative process: learning activities, assessment and content are adjusted to improve student engagement, strengthen practical competences and keep pace with technological change.

Academic Leadership

Master in Big Data Engineering

Designed the programme in 2015 and have coordinated it since its creation, linking data engineering education with the knowledge generated by the research group and with evolving professional needs.

Cloud & Edge Computing Research Line

Coordination of the research line since 2015 also provides a direct bridge between research activity and teaching in distributed systems, cloud/edge architectures, IoT and data-intensive computing.

Doctoral Supervision

Supervision and co-supervision of doctoral research spanning learning analytics, educational technology, data-intensive systems and transformative learning, reinforcing the connection between advanced research and academic mentoring.

Educational Innovation & European Projects

From funded projects to reusable learning resources

European and national projects are not treated only as research activities: their outputs are used to enrich courses with new materials, methodologies, technologies, real-world challenges and international collaboration. This includes work related to cloud and edge computing education, service-learning, educational analytics, AI in higher education, STEAM and Industry 5.0 skills.

Erasmus+Service-LearningAI in EducationCloud & EdgeSTEAMIndustry 5.0

My objective is to consolidate an open and dynamic teaching model in which teaching, research and knowledge transfer reinforce one another: students work on authentic problems, research outputs become educational resources, and international collaboration becomes part of the learning experience.