Antonio Aguirre Bayesian Forecasting | Quantile Modeling | Research Software

Antonio Aguirre

Antonio Aguirre

I'm a Ph.D. candidate in Statistics at the University of California, Santa Cruz, advised by Bruno Sansó and Raquel Prado. My research centers on Bayesian time series forecasting, quantile modeling, and uncertainty quantification, with an emphasis on scalable inference and research software that can be inspected, tested, and reused. I work on river-flow forecast correction, Q-DESN quantile forecasting, and dynamic quantile state-space models.

Current Highlights

  • exdqlm: R package available on CRAN, with a companion manuscript submitted to the Journal of Statistical Software.
  • Forecast correction manuscript: Bayesian quantile-based correction and synthesis of river-flow forecasts, currently in revision.
  • Q-DESN working paper: Bayesian quantile readouts for fixed Deep Echo State Network features.
  • Recent recognition: ISBA 2026 poster accepted and EnviBayes Student Paper Competition winner.

Ongoing Projects

  • Quantile-Based Forecast Correction: Bayesian correction and synthesis of river flow forecasts (manuscript in revision).
  • Q-DESN Forecasting: Working paper on Bayesian quantile readouts for fixed Deep Echo State Network features.
  • Scalable Bayesian Inference: Variational Bayes and Sequential Monte Carlo for real-time updating in operational settings.

Applied and Professional Experience

  • Computer Systems Coordinator, UCSC Statistics: Administer Linux research servers and build automation for research workflows (2024-present).
  • Quantitative Researcher, Delos Financial Technologies: Built evaluation workflows, automated backtests on AWS, and standardized model diagnostics (2025).
  • Data Analyst, NeatLeaf Inc.: Developed data pipelines and spatiotemporal models for greenhouse telemetry and anomaly detection (2021-2022).
  • Data Analyst, Banco de México: Built pipelines for image datasets, anomaly classification models, and forecasting prototypes (2018-2019).

Teaching and Mentoring

  • Graduate Student Instructor: Data Visualization (STAT 80B), Spring 2025.
  • Teaching Assistant: Supported Probability Theory, Classical and Bayesian Inference, Statistics, and related courses (2021-present).
  • TA Training Program: Co-organize workshops on inclusive teaching and active learning.
  • UCSC Statistics TA Resources: Co-maintain the department TA wiki, a public guide for TA responsibilities, teaching practices, and course support.
  • ASA DataFest Mentor: Guided student teams on modeling and communication (2023).

Education

Beyond Research

Outside of work, I enjoy baking bread, cooking Mexican dishes, reading history, the philosophy of science, and learning German.

For collaboration, questions, or related work, the Contact page lists the best ways to reach me.