I'm Alejandro, a computational scientist from Barcelona working across machine learning, data science, scientific software, and digital health, with a background in bioinformatics, epidemiology, and computational biomedicine.

My work usually revolves around extracting signal from complex biological and health-related data, building reproducible analytical workflows, and turning research questions into robust tools, models, and data products. A recurring thread across many of my projects is applied machine learning on biological signals, from Laser-Induced Fluorescence (LIF) particle-level classification to protein engineering / sequence-to-function modelling, as well as time series and spatial analysis in public health.

I use this space to share research code, open-source tools, data workflows, visualisation projects, infrastructure experiments, and other things I build at the intersection of science and software. I am always glad to connect around interesting problems, collaborations, and applied projects where rigorous analysis, pragmatic engineering, and good tooling all matter.

My interests and past work include:

• machine learning and applied AI
• bioinformatics and computational biology
• epidemiology and health data analysis
• time series, GIS, and spatiotemporal modelling
• scientific software and reproducible research tooling
• APIs, automation, and developer workflows
• self-hosting, lightweight DevOps, and infrastructure
• data visualisation, dashboards, and analytical apps
• digital health and open-source diabetes technology

A large part of my recent work has focused on public health questions such as Kawasaki Disease and infectious disease dynamics, alongside projects in the aerobiome, microbial detection, and broader data-intensive scientific workflows.

I am also deeply interested in the more practical engineering side of software: self-hosting, home lab setups, VPS-based deployments, service orchestration, and automation-heavy workflows, especially with GitHub Actions and containerised tools. I enjoy building systems that are reproducible, maintainable, and useful beyond a single analysis.

Another area I care a lot about is the digital diabetes ecosystem, including CGM data, sensors, Nightscout, open-source diabetes tooling, and the broader intersection of health technology, data access, interoperability, and real-world user needs.