Roger Gonzalez March

I work as a research engineer within the Data Analytics & Visualization Group at the Barcelona Supercomputing Center (BSC), contributing to applied research projects at the intersection of data systems, machine learning, visualization, and societal challenges.

My role focuses on the design and prototyping of computational systems for complex, often non-traditional datasets, particularly in cultural, institutional, and public-sector contexts. I typically collaborate on projects obtained through competitive calls, research grants, and art–science residencies, translating research questions into scalable data and ML systems.

My work spans:

- ML system design for cultural, civic, and institutional data

- Graph-based and recommender systems, with an emphasis on interpretability and representation

- NLP pipelines for historical and unstructured text corpora

- Explainable AI, including the integration of social and contextual covariates

Advanced data visualization as a research and sense-making tool

I have contributed to projects ranging from urban accessibility and public infrastructure analysis to cultural analytics and interactive AI installations, including international exhibitions and interdisciplinary collaborations with artists, designers, and researchers.

Alongside my institutional role, I am pursuing a PhD independently, aligned with my research practice at BSC. My work is situated between applied research, system design, and exploratory computational practice, rather than product development or operational engineering.

Developing and Implementing Machine Learning Models: Utilized traditional machine learning approaches alongside advanced Natural Language Processing (NLP) techniques. Implemented BERT-based language models to analyze and categorize extensive datasets comprising academic curriculum and research publications. Network Analysis: Conducted sophisticated network analysis to map and understand the intricate relationships between various aspects of Planetary Wellbeing, such as Sustainable Development Goals, and UPF's academic offerings and research outputs. Data Mining: Employed data mining strategies to extract and interpret complex data patterns, facilitating a deeper understanding of how Planetary Wellbeing concepts are reflected in UPF's educational and research frameworks.

Engaging in the "Complexity of Information Dynamics" project to analyze disinformation spread on social networks. Reviewing AI models for disinformation detection and visualizing social network data for trend analysis. Developing AI models to predict content virality. Contributing to the design and execution of experiments on social networks. Utilizing advanced Python programming and data analysis skills in a multidisciplinary team, appling ML expertise.