Antonio Aguirre Data Modelling·Bayesian Statistics·Machine Learning

Antonio Aguirre

Antonio Aguirre

I’m a Ph.D. candidate in Statistics at the University of California, Santa Cruz. Working with Bruno Sanso and Raquel Prado, my research focuses on improving statistical methods to make them practical and effective for solving real-world problems. My work emphasizes uncertainty quantification, forecasting, and scalable Bayesian techniques, especially in environmental and hydrological contexts.

Ongoing Projects

  • Improving Climate Data Accuracy: Integrating advanced methods such as Markovian Processes, Quantile Regression, and Variational Inference to improve hydrological system historical estimates, predictions, and ensemble forecasts.
  • Integrating Climate Forecasts and Historical Data: Applying Generalized Bayesian Updating and Forecast Scoring to combine and refine multiple forecast outputs and historical analysis for improved accuracy and reliability.
  • Building Scalable Real-Time Forecast Systems: Developing inference algorithms such as Variational Inference, Sequential Monte Carlo, and Particle Filtering to enable fast, scalable, real-time updates for dynamic forecasting systems.
  • Advancing TA Training Programs: Designing and conducting workshops that promote equity and active learning for incoming TAs and maintaining the department's recently created TA-Wiki to support professional development.

Education

Beyond Research

When I'm not PhDing, I enjoy baking bread, cooking Mexican dishes, reading history, diving into the philosophy of science, and learning German.

Feel free to reach out if you’d like to connect or discuss ideas. Thanks for stopping by!