Valeria Javalera Rincón

Research Scholar

Exploratory Modeling of Human-natural Systems Research Group

Advancing Systems Analysis Program

Biography

Valeria Javalera is a researcher in the Exploratory Modeling of Human-Natural Systems Research Group of the IIASA Advancing Systems Analysis Program.

At IIASA, Javalera is the principal investigator of the Food, Agriculture, Biodiversity, Land-use, and bio-Energy (FABLE) project, as part of which she lead the development of the Smart Linker Platform. The Platform links land-use models from 24 regions and countries participating in the project. Part of her work in FABLE is to help coordinate the collaboration between the different country teams to address some Sustainable Development Goals (SDGs) by aligning countries' land-use planning through sustainable international trade. She is also the principal investigator, at IIASA, of the Land-use Planning and Financial Innovation to Increase Mexico's Resilience to Climate Change Project (SAbERES), where IIASA contributes to developing novel participatory land-use planning tools at different layers to increase small farmer resilience to climate change in vulnerable areas.

Javalera holds a PhD (cum laude) in Automatic Control, an MSc in Computer Science, and an Engineering degree in Computer Systems. She has participated in several projects in the EU related to the distributed optimization of natural resources and has more than 15 years of teaching experience in Computer Science. Her main contributions are developing machine-learning methods to solve multi-objective and multi-agent optimization problems where a Pareto optimum is needed.

Last update: 16 JAN 2024

Publications

Apetrei, C., Strelkovskii, N. , Khabarov, N. , & Javalera Rincón, V. (2024). Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning. Ecological Modelling 489 e110609. 10.1016/j.ecolmodel.2023.110609.

FABLE (2024). Transforming food and land systems to achieve the SDGs. In: The SDGs and the UN Summit of the Future. Sustainable Development Report 2024. Eds. Sachs, J.D., Lafortune, G., & Fuller, G., pp. 50-82 Dublin, Ireland: Dublin University Press. ISBN 978-0-903200-18-9 10.25546/108572.

Orduña-Cabrera, F. , Sandoval-Gastelum, M., McCallum, I. , See, L. , Fritz, S. , Karanam, S., Sturn, T., Javalera Rincón, V. , & Gonzalez-Navarro, F.F. (2023). Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information. Geographies 3 (3) 563-573. 10.3390/geographies3030029.

Apetrei, C., Strelkovskii, N. , Khabarov, N. , & Javalera Rincón, V. (2023). Learning Extension - RAGE RAngeland Grazing Model 1.0.0.