prescriptive analytics"

A decision-focused learning and two-stage stochastic optimization framework combining SPO+ with intraoperative recourse for operating room scheduling under uncertain surgery durations.

A novel Meta-Reinforcement Learning (Meta-RL) framework for adaptive scheduler selection in offline, non-preemptive mixed-criticality scheduling on varying-speed processors[cite: 13].

A time-indexed mixed-integer programming framework incorporating chance constraints and a grid-based linearized Chernoff-bound approximation to address inventory estimation uncertainty in post-wildfire salvage logging.

Fusing machine learning for fault detection with stochastic optimization to orchestrate robust, resource-aware maintenance schedules under uncertainty.

Using operations research methodologies, we are developing new tools for observatory operations.

In this interview (starting at 40:45), we discuss the practical impact of data science in Chile, focusing on prescriptive analytics in healthcare, traditional industries, and territorial management.

Developing a cloud-based analytical platform to optimize predictive and prescriptive maintenance across industrial systems.

We are studying data-driven prioritization models that can help hospitals manage their oncology waiting lists with application in the South Eastern Metropolitan Health Service

FONDECYT Regular project. Developing resource cost-aware scheduling solutions to optimize performance under uncertainty, with applications in astronomy and other industries.

Using deep learning spatial-temporal graph models for seasonal forecasting of extreme temperature events.