Welcome to our Research Webpage

Operations Research, Prescriptive Analytics, and Systems Engineering.

Our Research

What we do…

We are interested in harnessing the power of operations research, predictive analytics, and prescriptive analytics methodologies, to develop decision support tools for applied problems.

Currently, our research has focused on two main methodological areas:

Combinatorial algorithms
Stochastic optimization and Uncertainty modeling

We work in combining both areas, focused on applications, to make robust decision support systems. The applications we are looking into right now are energy management and astronomical observatory operations. They look far away from each other, but similar techniques can help significantly improve the processes if both settings. And both have large amounts of data that we can use to train, test, and validate our developments.

Look into our Projects section for more information on our current projects, and Contact us if you want to participate in some of them.

Research Team

Current researchers and students working in our research group.

Research Engineers

Alfredo de Rodt

Research Engineer

Prescriptive Analytics, Models Deployment, Explainable AI

Camila Marcuello

Research Engineer / M.S. in Data Science

Machine Learning, Scheduling, Optimization

Constanza Lorca

Research Engineer

Machine Learning, Optimization, Observatory Operations

Luis Arias

Research Engineer / B.S. in Engineering

Optimization, Operations Research

Ph.D. Students

Andrés Catlán

Ph.D. Student, Industrial Engineering and Operations Research

Machine learning, Optimization, Maintenance

Graduate Students

César Cerda

M.S. Student, Industrial Engineering and Operations Research

Optimization

Andrés Lagos

M.S. Student, Industrial Engineering and Operations Research

Machine learning, Algorithms, Renewable energy sources

Diego San Martín

M.S. Student, Industrial Engineering and Operations Research

Optimization, Prescriptive Analytics

Rodrigo Munizaga

M.S. Student, Industrial Engineering and Operations Research

Machine learning, Prescriptive Analytics

Undergraduate Students

Matilde Valdés

B.S. in Engineering

Optimization, Operations Research

Tomás Couso

B.S. in Engineering

Machine learning, Satellite Image Processing

Principal Investigator

Rodrigo A. Carrasco

Associate Professor & the UC Data Science Initiative Director

Combining Predictive and Prescriptive Analytics, Combinatorial Algorithms, Algorithm Design & Analysis

Featured Publications

Some of the recent publications we want to highlight.

Uncertainty estimation through quantile forest for prescriptive scheduling of data processing at ALMA

The Atacama Large Millimeter/submillimeter Array (ALMA) is a prominent astronomical observatory known for its detailed imaging …

Rodrigo A. Carrasco, Luis Aburto, Jorge Garcia, Alfredo De Rodt, Gianfranco Speroni

PDF DOI

Is the change deforestation? Using time-series analysis of satellite data to disentangle deforestation from other forest degradation causes

Protecting natural ecosystems requires monitoring approaches that work as early warning systems to avoid degradation and protect …

Ignacio Fuentes, Javier Lopatin, Mauricio Galleguillos, Andrés Ceballos-Comisso, Susana Eyheramendy, Rodrigo A. Carrasco

PDF DOI

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of …

Anthony Cho, Rodrigo A. Carrasco, Gonzalo Ruz

PDF Project DOI

Improving prescriptive maintenance by incorporating post-prognostic information through chance constraints

Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures …

Anthony Cho, Rodrigo A. Carrasco, Gonzalo Ruz

PDF Project DOI GitHub

See all publications

Research Lines & Projects

Current research lines and grants

Our research is driven by the goal of bridging theory and practice, developing both predictive and prescriptive tools through mathematical optimization. These ongoing projects exemplify our approach, and research lines, where we leverage combinatorial optimization and stochastic modeling techniques to create decision support systems across diverse real-world applications.

You can find a list of our grants here, and a full list of current and past projects is available here.

FONDEF PlASMa

We are combining machine learning and stochastic optimization tools to develop optimal energy management systems for fotovoltaic generation with storage.

PolPub Onco

We are combining machine learning and stochastic optimization tools to develop optimal energy management systems for fotovoltaic generation with storage.

FONDECYT

We are combining machine learning and stochastic optimization tools to develop optimal energy management systems for fotovoltaic generation with storage.

Operations Research Tools Applied to Scheduling in Observatory Operations

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

ClimateDL: Forecasting of extreme temperature events

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

GEMS: variable-Generation Energy Management with Storage

We are combining machine learning and stochastic optimization tools to develop optimal energy management systems for fotovoltaic generation with storage.

Predictive Maintenance

Developing tools for fault detection and diagnosis, in order to construct predictive maintenance systems.

Publications

All of our group’s publications; easy to sort and cite.

Search for relevant content by filtering publications.

Rodrigo A. Carrasco, Luis Aburto, Jorge Garcia, Alfredo De Rodt, Gianfranco Speroni (2024). Uncertainty estimation through quantile forest for prescriptive scheduling of data processing at ALMA. Observatory Operations: Strategies, Processes, and Systems X.

PDF DOI

Ignacio Fuentes, Javier Lopatin, Mauricio Galleguillos, Andrés Ceballos-Comisso, Susana Eyheramendy, Rodrigo A. Carrasco (2024). Is the change deforestation? Using time-series analysis of satellite data to disentangle deforestation from other forest degradation causes. Remote Sensing Applications: Society and Environment.

PDF DOI

Anthony Cho, Rodrigo A. Carrasco, Gonzalo Ruz (2022). A RUL Estimation System from Clustered Run-to-Failure Degradation Signals. Sensors.

PDF Project DOI

Anthony Cho, Rodrigo A. Carrasco, Gonzalo Ruz (2022). Improving prescriptive maintenance by incorporating post-prognostic information through chance constraints. IEEE Access.

PDF Project DOI GitHub

Rodrigo A. Carrasco, Susana Mondschein, Macarena Azar (2022). Dealing with Uncertain Surgery Times in Operating Room Scheduling. European Journal of Operational Research.

PDF DOI

See all publications

Recent Talks

Conferences and science communication

These are some of the recent talks and conferences in which we have participated. A full list is available here.

Optimization and waiting lists

Rodrigo A. Carrasco and Jocelyn Dunstan discuss the versatile applications of mathematical optimization and data science, from radio telescopes to oncology waiting lists.

Aug 14, 2024 8:30 AM — 9:00 AM Spotify

Rodrigo A. Carrasco

imfd spotify

Algorithms in a hyperconnected world

In this interview we talk about operations research and its importance in a data-centered world.

May 13, 2024 8:30 AM — 9:00 AM Radio 13c

Rodrigo A. Carrasco

Video radio_13c

Opportunities and challenges of generative AI

In this webcast, we share ideas and insights with Maria Francisca Yañez (Microsoft) and Patricio Cofre (EY) on the Opportunities and challenges we see with the new tools in generative AI.

May 17, 2023 9:00 AM — 10:00 AM EY Chile

Rodrigo A. Carrasco

Video