Mathematical Biology

I consider myself a Mathematical Biologist working at the intersection of Computational Statistics and Environmental Sciences. However, this interpretation is broad. In particular I’ve done research on:

Spatial Statistical Ecology

Studying biological processes in spatio-temporal contexts. Abstracting the dependencies, patterns and phenomena into mathematical models. I consider this abstraction an element of the field: fundamental biology.

Any respectable biological model should have a notion of randomness, i.e. source of random variation. Therefore, the biological models are stochastic processes, in which statistical sciences have been developed a formal mathematical framework.

In this sense, part of my research is developing or implementing novel statistical methods for accounting ecological variability to infer or predict environmental and ecological processes in space-time.

Geospatial DataScience

Often the data is messy, unstructured and, specially huge . The statistical models presented so far involves different representations of the data and not every model fits each data (e.g. Lattice-data, geostatistics or point-process all in spatial statistics). The development of computational methods and software for storing and querying data is part of my main research work. I’m developing a knowledge engine that stores and query all this different aspects of The Reality. The project is called: Biospytial ; a multi-purpose knowledge engine for environmental sciences.


I work with spatial statistical models applied to environmental and biodiversity sciences. I am developing methods in statistical ecology to infer and predict these spatial patterns. I am also the developer of Biospytial; A Knowledge Engine for the Environmental Sciences.


  • Spatial Statistics
  • Species Distribution Modelling
  • Epidemiology and dispersion
  • Real time Monitoring systems design, (project: Incendios)
  • Remote Sensing, Big Data analytics