Applied research
Designing machine learning workflows for multisensor ecological data and biodiversity monitoring.
Browse projects
AI for ecology • biodiversity monitoring
Hi! I'm Alba
Computer engineer and PhD researcher developing AI methods for ecological sensing, multisensor analysis, and scalable biodiversity assessment.
4+ years
Research in AI for ecological monitoring
10+ publications
Peer-reviewed contributions across ecology and AI
Deep Learning · Computer Vision · Ecoacoustics
Multisensor pipelines for conservation research
About me
I am a Computer Engineer from the University of Huelva with a Master's degree in Computational Intelligence and IoT from the University of Córdoba. Combining my passion for computer science with deep environmental awareness, I focus on the intersection of technology and ecology.
I am currently a PhD student at the University of Cádiz, where I focus my thesis on applying artificial intelligence to the analysis of ecological data collected from remote sensors for biodiversity monitoring.
My work sits at the intersection of computer science and environmental conservation. I specialize in developing AI-driven tools for automating the processing of ecological datasets—acoustic recordings, camera trap images, underwater audio, and other remote sensing technologies—to support efficient, scalable, and reliable wildlife monitoring.
What I work on
From research to practical conservation tools
My work bridges AI, environmental sensing, and applied ecology to build tools that help researchers analyze data faster and make better-informed decisions.
Scientific communication
Sharing results through publications, talks, and collaborations with academic and conservation partners.
View publicationsOpen connections
Always interested in new ideas, partnerships, and opportunities to support conservation technology.
ConnectResearch Focus
My work centers on applying artificial intelligence to ecological monitoring, with a strong emphasis on ecoacoustics and multisensor data. I design automated pipelines that support scalable biodiversity assessment and help researchers extract meaningful patterns from large datasets.
Multisensor AI
Detection, classification, analysis and integration of audio, video, and radar data for automated ecological monitoring.
Automated pipelines
End-to-end workflows for remote sensing data: preprocessing, model training, validation, and scalable deployment.
Conservation Technology
Tools that support biodiversity monitoring, reduce expert workload, and promote open and reproducible research.
Current work
AI Computer Scientist
Contributor to the development and delivery of DeTect's AI-enabled product lines, with a focus on designing, deploying, and maintaining machine learning features in production-grade software systems for biological target classification.
PhD Student in Computer Science
Doctoral researcher working on artificial intelligence methods for ecological monitoring, including multisensor analysis and scalable biodiversity assessment.
Upcoming events
II Jornadas de Ecoinformática de la AEET
1-3 October 2026 | Sevilla, Spain
I will give an invited talk titled Cómo integrar la Inteligencia Artificial en la investigación ecológica during the event.
The jornadas bring together researchers, students, and practitioners working on data analysis, statistics, modelling, AI, and programming for ecology.
CV4Ecology 2027 (Instructor)
January 2027 | Smithsonian Conservation Biology Institute
I will be part of CV4Ecology 2027 as an instructor. Applications are open until 13th June. You can find details and apply through the official event page.