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The core of my research focuses on methodology for decision-making problems under uncertainty and applications of those techniques. On the methodology side, I am interested in the development of models that consider ambiguity in the probability distributions of the uncertainty factors in the problem. I am also interested in risk models that can be used within an integrated framework of optimization and simulation. I have also been studying models where interdependencies exist between the uncertainty factors and the operating decisions, which occurs in many practical problems.

On the application side, I am currently interested in energy planning since many of the topics in that area fall into the realm of decision-making problems under uncertainty. Energy planning is viewed here both as long term (for example, capacity planning) and short-term (management of energy systems taking into account uncertainty of renewable sources of energy). I have also worked in other applications areas such as finance, transportation and mine planning where uncertainty plays an important role.


  • Optimization under uncertainty
  • Risk models
  • Energy planning
  • Climate change
  • Finance

Current Research Projects

  • Adapting to the uncertainties and risks of climate change: Advanced methods and models for energy systems and markets

    Funded by Programa de Investigación Asociativa (PIA), grant ACT192094, ANID, Chile

    Energy systems play a key role in the world economy. The importance of power transmission grids and generation systems was made clear by the U. S. National Academy of Sciences, which selected “electrification” as the most important engineering achievement of the 20th century. Currently, energy systems face two major challenges due to climate change: transformation and adaptation.

    The first challenge, transformation, refers to the direct contribution of energy systems to global warming as a result of the use of fossil fuels, and governments as well as private companies are already addressing it through environmental policies.

    The second challenge, which is the focus of this project, has to do with climate change: the adaptation of energy systems to the physical and financial or transitional risks. A changing climate imposes unprecedented challenges to private investors, utilities, and governments. Renewable energy technologies that rely on solar, wind, and hydro resources will play a key role in the decarbonization of energy systems, yet these resources strongly depend on climate conditions and, as a consequence, are highly susceptible to the uncertainties and risks posed by this changing environment.

    The goal of the project is to develop new mathematical models and computational methods to help private and public sector adapt energy systems and markets to the great uncertainties and risks that result from climate change. To achieve this goal, the research  addresses three research objectives: (1) To model and classify the different types of uncertainties and risks associated with climate change; (2) To develop energy planning models under uncertain conditions from climate change; and (3) To design new energy market and regulatory models with risk-averse agents. The project seeks to combine techniques from operations research, climate modeling, and financial economics to develop an integrated approach to the problem.

  • GEMA: Improving energy management in micro grids with storage via stochastic optimization and machine learning

    Funded by FONDEF Program, Grant ID19I10158, Chile

    The main objective of this project is to perfect and implement an optimal energy management system for generation systems based on photovoltaic panels and with storage units. These systems can be both on-grid (connected to the network) and off-grid (isolated systems, generally in remote locations), and connected to a small number of users, such as a home or a small business. In particular, the management system to be implemented will learn both the characteristics of the generation and demand curves of the connected user, information that will be used to determine how to manage energy. This system will define when to store, use or inject the energy generated into the grid, as well as what to do with the stored energy, given the future generation and demand scenarios. This, with the aim of maximizing the economic / social benefits of the generation system, making distributed generation / storage systems more profitable and increasing their penetration.

    The development of the system is based on advanced optimization techniques under uncertainty and machine learning methods. Part of the research effort of the project focuses on the development of techniques in these areas, specialized in the problem of energy management. The other part of the project focuses on the application of these techniques and the physical implementation of prototypes to test the proposed methods.

  • Integrating predictive and prescriptive analytics for stochastic optimization

    Funded by grant FONDECYT 1221770, ANID, Chile

    The area of business analytics has undergone a revolution in the past ten years with the explosive growth in the use of data to solve problems. The combination of immense availability of data, relatively inexpensive data storage, fast and reliable data transmission plus maturity of algorithms have resulted in the almost ubiquitous use of data science in many organizations.

    The focus of this research is on advancing prescriptive analytics methods to allow for more integration between the prediction and optimization steps. The common practice is to first use data to create a model for the uncertain elements such as demand, and then use this model as an input into an optimization scheme that chooses the best course of action among a set of alternatives. Our goal is to construct prescriptive models that integrate those two steps, and to measure quality by a loss function that captures the decision error (as opposed to prediction error).

    We apply our  methodology to problems in energy planning. This is an area in which there is abundance of data, and where stochastic optimization models have been widely used. Thus, we believe that many problems in that area can benefit from integrated approaches that optimize the systems by making the best use of data. Examples of such problems include day-ahead unit commitment and strategic energy planning.