Platz der Göttinger Sieben 3, office 1.155
Universität Göttingen

Spatial Data Science and Statistical Learning

email: [email protected]; [email protected]

Joaquin Cavieres

I am a postdoctoral researcher in the "Chair of Spatial Data Science and Statistical Learning" led by Professor Elisabeth Bergherr at Georg-August-Universität Göttingen. Previously, I was a postdoctoral researcher in the "Chair of Geoinformatics-Big Spatial Data" working with Professor Meng Lu at Bayreuth University.

My doctoral thesis was supervised by Javier Contreras Reyes and Michael Karkulik, proposing different computational methods to incorporate a smoothing thin plate spline in spatial models.

During my doctoral studies I did two very interesting internships; the first one in the Probabilistic Machine Learning group at Aalto University, under the supervision of Aki Vehtari. The second one was in the Computer, Electrical, and Mathematical Sciences and Engineering Division at KAUST, under the supervision of Paula Moraga.

I have also worked as Data Scientist in the retail (Cencosud-Scotiabank) and the forest industry (Arauco Celulosa).

Generally I use R, Template Model Builder (TMB) and Stan to develop probabilistic models, but I am also interested in numerical methods (from a deterministic approach) for spatial models using Rcpp/RcppArmadillo.

Research interests

📚 Publications

  • Cavieres, J., Monnahan, C.C., Bolin, D., and Bergherr, E. (2024). Approximated Gaussian Random Field Under Different Parameterizations for MCMC. Developments in Statistical Modelling (👉 More Details)
  • Escárate, P, Curé, M., Araya, I., Coronel, M., Cedeño, A.L., Celedon, L., Cavieres, J., Aguero, J.C., Arcos, C., Cidale, L.S., Levenhagen, R.S., Pezoa, R., and Diáz, S.Simpon. (2023). A method to deconvolve stellar profiles: The Non-Rotating Line utilizing Gaussian Sum Approximation. Astronomy & Astrophysics (👉 More Details)
  • Lu, M., Cavieres, J., Moraga, P. (2023). A comparison of spatial and non-spatial machine learning methods in NO2 modelling: prediction accuracy, uncertainty quantification, and model interpretation. Geographical Analysis (👉 More Details)
  • Cavieres, J., Ibacache-Pulgar, G., Contreras-Reyes, J.E. (2022). Thin plate spline model under skew-normal random errors: estimation and diagnostic analysis for spatial data. Statistical Computation and Simulation (👉 More Details)
  • Cavieres, J., Monnahan, C.C, Vehtari, A. (2021). Accounting for spatial dependence improves relative abundance estimates in a benthic marine species structured as a metapopulation. Fisheries Research, 240, 105960 (👉 More Details)
  • Cavieres, J., Nicolis, O. (2018). Using a spatio-temporal bayesian approach to estimate the relative abundance index of yellow squat lobster (Cervimunida johni) of Chile. Fisheries research, 208, 97-104. (👉 More Details)
  • Cavieres, J. (2022). Computational methods for a smoothing thin plate spline in spatial models. Doctoral thesis (👉 More Details).
Chart of Spatial Models Chart of Spatial Models

📚 Publications (in press)

📚 Working papers

👨‍💼 Conference talks

👨‍💼 Conferences & Workshops

👨‍🏫 Teaching