# Dr Daniela Castro-Camilo

**Senior Lecturer**(Statistics)

**telephone**:
01413304748

**email**:
Daniela.CastroCamilo@glasgow.ac.uk

Room 315, Mathematics and Statistics Building, Glasgow G12 8QQ

## Biography

Daniela Castro-Camilo is a Lecturer in Statistics at the University of Glasgow. Her research focuses on the theory and applications of multivariate and spatial extremes, spatial statistics, environmental statistics and Bayesian inference, with a particular interest in environmental, geological, ecological, and energy-related applications. During the last few years, she has developed user-friendly methods promoting the need to adequately capture extreme observations within the usual statistical analysis centred around mean values. She actively contributes to the statistical community, serving as the meeting secretary for the Environmental Section of the Royal Statistical Society, as an associated editor for the journal Environmetrics, and as a member of the International Environmetrics Society outreach and liaison committee.

## Research interests

My research focuses on the theory and applications of multivariate and spatial extremes, with a particular interest in environmental, geological, and ecological applications. During the last few years, my work has gravitated around the integrated nested Laplace approximation (INLA) method for Bayesian inference. Specifically, I have developed methods promoting the need to adequately capture extreme observations and collaborated in the implementation and improvement of extreme value models within INLA to help to bridge the gap between statistical theory and practice. I co-authored the book “Advanced spatial modelling with stochastic partial differential equations using R and INLA” (CRC Press, 2018).

Currently, my research interest is divided into the following topics:

- Spatial and spatio-temporal extremes with applications to meteorological and environmental data (temperature, precipitation, wind speeds, soil pollutants).
- Natural hazard modelling with a focus on landslide modelling using the SPDE-INLA approach.
- I’m working on two grants at the moment. One is related to the forecast of faults in electricity networks, funded by OFGEM (office for gas and electricity markets). The second one is an NCR-funded project "Mitigating Landslides Impact in Scotland" which aims at combining a novel landslide susceptibility model with elements at risk to support emergency services and landslide hazard mitigation strategies in Scotland.
- Geostatistics of binary extremes.
- Climate extreme event attribution.

### Research groups

## Grants

- "
**Improving landslide hazard assessment in Scotland**" funded by the Scottish Government through the National Centre for Resilience in collaboration with the British Geological Survey and the Department of Applied Earth Sciences, University of Twente. - "
**Predict4Resilience - beta phase**" funded by the Office for Gas and Electricity Markets (UK government) in collaboration with Scottish Power, SIA Partners and the Met Office.

## Supervision

**Current PhD students**

**Bryce**, Erin

Statistical landslide hazard modelling with a view towards medium to long term territorial planning**Cuba**, Miriam

Understanding heavy-metal variation and extremes in the Glasgow Clyde River Basin using the G-BASE dataset**Hu**, Chenglei

Natural hazard risk estimation using Multivariate Extreme-Value Mixture Models (MEVMM)**Li**, Mengran

Climate extreme event attribution using sub-asymptotic models and counterfactual theory**Villejo**, Stephen Jun

A Bayesian Spatio-Temporal Model to Test for Stability of Risks for Spatially Misaligned Data

**Honours projects supervision (2019-present)**

- Caitlin Fox
- Tianhao Tan
- Mahi Siddika
- Paddy O'Hara
- Erin Bryce
- Louis Chislett
- Holly Moran
- Huinan Zhu
- Maria Tsiarkezou
- Samuele D'Avenia
- Sixiang Cheng
- Ula Wolanowska
- Rob Corner
- Cara McPhail

## Teaching

**2023/24 session**

On study leave (sabbatical)

**2022/2023 session**

- STATS4047-STATS5022: Principles of Probability and Statistics. Moodle page (for enrolled students only).
- STATS5103: Introduction to Statistical programming in R and Python. Moodle page (for enrolled students only).

## Additional information

Responsibilities

- I am the Deputy Exam Officer for Statistics. My main role is to manage and coordinate all Stats exam processes across all the levels, in collaboration with the office of teaching and the School Exams Officer.
- I am an academic advisor of studies for MSc students. My main role is to provide advice on course choices and offer pastoral support throughout their University career.