Antonio Aguirre Data Modeling | Bayesian Statistics | Machine Learning

Antonio Aguirre

Welcome

Hello! I'm Antonio Aguirre, a Ph.D. candidate in Statistics at the University of California, Santa Cruz. I work with Dr. Bruno Sansó and Dr. Raquel Prado on Bayesian time series forecasting and quantile modeling, with a focus on uncertainty quantification and scalable variational inference.

I develop modern statistical and machine learning methods for forecasting, including Deep Echo State Networks (Deep ESN). I also have industry experience running experiment workflows and automated backtests on AWS.

I hold a B.Sc. in Applied Mathematics and an M.Sc. in Economics from Instituto Tecnológico Autónomo de México (ITAM).

Research Interests

  • Time Series Forecasting: Bayesian dynamic models for probabilistic prediction and decision support.
  • Quantile Modeling and Risk Assessment: Quantile-based methods for calibration and extremes.
  • Scalable Bayesian Inference: Variational Bayes and approximate inference for large, complex models.
  • Continual Learning: Online updates and adaptive forecasting as new data arrive.
  • Forecast Combination: Multi-model synthesis and bias correction for operational forecasts.

Real-Time Monitoring: San Lorenzo River Discharge Flow

Note: This plot provides real-time monitoring of the San Lorenzo River's discharge at Big Trees. It is currently offline because the university servers and ngrok tunnel used for streaming were not approved for continuous public access. I am migrating to a 24/7 hosting setup and will bring it back online soon.
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