Dr Jafet Belmont Osuna

  • Lecturer in Statistics and Data Analysis (Statistics)

Biography

Bsc. in Biology from the School of Sciences, UNAM, México in 2014 and Specialist in Applied Statistics from the Research institute in Applied mathematics (IIMAS, UNAM) in 2016. Then, received MSc in Environmental Statistics from the University of Glasgow in 2017 and PhD in Statistics in 2021. Employed by University of Glasgow since 2018. 

 

Research interests

Research area

My research focuses on developing statistical models to analyse biodiversity data from citizen science projects and applying Bayesian methods to characterize biological communities in changing environments. I am also interested in developing and exploring machine-learning techniques to analyse complex ecological and environmental data.

Current Research

Currently, I investigate methods to estimate species distributions while accounting for the observational process of how data is collected across different citizen science biodiversity monitoring schemes in the UK. I am also interested in developing model validation and diagnostic tools for some commonly used ecological models. I am also part of a multidisciplinary team aiming to develop a generalised modelling framework to integrated species distribution models using INLA (integrated nested Laplace approximation). Such approach will provide both a generalized theoretical framework that can cover a wide range of scenarios and a single practical tool for statisticians and ecologists alike.

Research groups

  • Statistics and Data Analytics Group
  • Species distribution modelling using citizen science data

 

 

Research groups

Publications

List by: Type | Date

Jump to: 2024 | 2022 | 2020 | 2018
Number of items: 4.

2024

Belmont, J. , Martino, S., Illian, J. and Rue, H. (2024) Spatio-temporal occupancy models with INLA. Methods in Ecology and Evolution, 15(11), pp. 2087-2100. (doi: 10.1111/2041-210X.14422)

2022

Belmont Osuna, J. , Miller, C. , Scott, M. and Wilkie, C. (2022) A new statistical approach for identifying rare species under imperfect detection. Diversity and Distributions, 28(5), pp. 882-893. (doi: 10.1111/ddi.13495)

2020

Wilkie, C., Belmont, J., Miller, C. , Scott, M. , August, T. and Taylor, P. (2020) Hierarchical Species Distribution Modelling Across High Dimensional Nested Spatial Scales. In: 35th International Workshop on Statistical Modelling, Bilbao, Spain, 20-24 Jul 2020, pp. 450-453. ISBN 9788413192673

2018

Belmont, J. , Sánchez-Coronado, M. E., Osuna-Fernández, H. R., Orozco-Segovia, A. and Pisanty, I. (2018) Priming effects on seed germination of two perennial herb species in a disturbed lava field in central Mexico. Seed Science Research, 28(1), pp. 63-71. (doi: 10.1017/S0960258518000016)

This list was generated on Thu Nov 21 03:25:57 2024 GMT.
Number of items: 4.

Articles

Belmont, J. , Martino, S., Illian, J. and Rue, H. (2024) Spatio-temporal occupancy models with INLA. Methods in Ecology and Evolution, 15(11), pp. 2087-2100. (doi: 10.1111/2041-210X.14422)

Belmont Osuna, J. , Miller, C. , Scott, M. and Wilkie, C. (2022) A new statistical approach for identifying rare species under imperfect detection. Diversity and Distributions, 28(5), pp. 882-893. (doi: 10.1111/ddi.13495)

Belmont, J. , Sánchez-Coronado, M. E., Osuna-Fernández, H. R., Orozco-Segovia, A. and Pisanty, I. (2018) Priming effects on seed germination of two perennial herb species in a disturbed lava field in central Mexico. Seed Science Research, 28(1), pp. 63-71. (doi: 10.1017/S0960258518000016)

Conference Proceedings

Wilkie, C., Belmont, J., Miller, C. , Scott, M. , August, T. and Taylor, P. (2020) Hierarchical Species Distribution Modelling Across High Dimensional Nested Spatial Scales. In: 35th International Workshop on Statistical Modelling, Bilbao, Spain, 20-24 Jul 2020, pp. 450-453. ISBN 9788413192673

This list was generated on Thu Nov 21 03:25:57 2024 GMT.

Teaching

Lecturer:

  • Data Mining & Machine Learning II (ODL)
  • Data Analysis (STATS3011/STATS4052)

Involved in delivering teaching, tutorial sessions and developing material for the following courses:

  • Probability level M (STATS5024)
  • Statistical Inference level M (STATS5028)
  • Regression Models level M (STATS5025)
  • Data Mining & Machine Learning I (ODL)
  • Predictive Modelling (ODL)
  • Data Management and Analytics using SAS (ODL)
  • Probability & Sampling Fundamentals (ODL)
     
  • Stochastic Processes (STATS4024/5026)
  • Flexible Regression (STATS4040/STATS5052)
  • Advanced Bayesian Methods (STATS4038/STATS5013)
     
     

Professional activities & recognition

Prizes, awards & distinctions

  • 2016: Awarded to the postgraduate student with the highest GPA and who graduated with the highest honors and exceptional thesis defense. (Medalla Alfonso Caso)