Daniel is a computational social scientist and Postdoctoral Researcher at the Centre for Care, at the Oxford Sociology Department.
His research in the Centre is focused on the development of robust estimation methods for social science and in the development of software libraries in Python and R to perform multiverse-type estimations. Additionally, he researches the application of machine learning / deep learning models (e.g., BERT, RoBERTa, GPT-2) on social science problems like misinformation detection and characterisation on social media text, and the characterisation of social movement emotions over time based on associated tweets. He is also familiar with the management of large quantities of data and the use of High Performance Computers (HPC) and cloud computing.
Prior to joining Oxford, Daniel completed his PhD in Computational Social Science at the University of Leeds, and before that worked for several years as a quantitative analyst at the Pontifical Catholic University of Chile. His general interests are related to the use of machine learning methods to understand human behaviour and the application of novel methods for robust parameter estimation, either using multiverse-type approaches or Bayesian / probabilistic approaches.