DATA SCIENCE FOR DECISION MAKING
Thanks for visiting! Explore my site to learn more about how I use Data Science methods in my academic and applied research work. I also run a blog in which I write about various topics. If you have questions or would like to discuss an opportunity to work together, feel free to get in touch.
I am assistant research professor at the School of Government and Public Transformation of Tecnológico de Monterrey, and affiliated adjunct policy researcher at the Rand Corporation. My work focuses on the development of new computational methods for studying technological change, and the use of data science methods for decision analysis under conditions of deep uncertainty. I currently lead applied research work on Mexico’s water and energy sectors, developing new simulation models and assisting stakeholders in decision making processes. I also lead the Decision Making Center of the School of Government and Public Transformation and co-lead applied policy research on climate change in Costa Rica, Argentina and Ecuador. I teach courses on systems modeling, advanced simulation techniques, econometrics and microeconomics.
I am primarily interested in understanding the mechanisms by which new technologies are created and disseminated globally, with a special focus on the implications of rapid technological change on long-term sustainable and equitable economic growth.
DECISION MAKING UNDER DEEP UNCERTAINTY
Over the last eight years I have worked on various research projects that apply the Robust Decision Making framework to complex and deeply uncertain environments, including planning long-term investments for water and energy infrastructure development, climate change mitigation and adaptation. I have been fortunate enough to do this kind of work in various countries in the American continent, including the US, Mexico, Peru and Argentina. As a result, a large proportion of my research is devoted to the development of new analytical tools and participatory processes for supporting decision analysis under conditions of deep uncertainty.
CLIMATE CHANGE MITIGATION
My work on this field focuses on analyzing the pertinence of different decarbonization strategies amid climate and technological deep uncertainty, considering the heterogeneous economic and technological conditions of advanced and emerging nations.
Graduate & Undergraduate Courses
ECONOMIC ANALYSIS FOR PUBLIC POLICY
I am currently teaching economic analysis courses at various levels: econometrics (undergrad), microeconomic analysis (master level) and climate change economics (phd level). You can find the syllabus of these courses in the link below. In these courses I emphasize the use of economic modeling and real data analysis for studying the implications of different policy alternatives. I also like to discuss theoretical validity of different theories and models, while emphasizing complementarites among them.
DATA SCIENCE FOR PUBLIC POLICY
I teach courses on systems dynamics modeling (master and phd level), introductory statistics (master level) and advanced modeling methods (master and phd level). In all these courses we use the R programming language. In the system dynamics modeling class we use R for operationalization, visualization and interactive analysis (i.e. ShinnyApps). In the advanced modeling methods class I focus on methods for using these models in the context of computational experimentation for supporting decision analysis processes.
I lead and participate in executive education programs at Tec of Monterrey focused on the energy sector. These courses rely substantially on case studies with practical application of advanced modeling tools for analyzing policy decisions in the sector. In particular, these case studies explore implications of decarbonization for emerging economies and the potential of new technologies such as electric vehicles and carbon-capture .
Commissioned Research and Competitive Funding
DESIGNING A ROBUST WATER STRATEGY FOR MONTERREY MEXICO
Diversification and adaptation for coping with climate, economic and technological uncertainties
In partnership with RAND researchers, the Tec of Monterrey team engaged with business, agricultural, industry, community and government representatives to apply RDM methods to evaluate investments for Monterrey’s water master plan for the next 30 years. The Tec-RAND team were principal designers of the water infrastructure plan commissioned by Monterrey’s Water Fund. The Tec analysis and the subsequent work with RAND demonstrated the fragility of the prior plan for water infrastructure and the possibility of developing an adaptive master plan that diversifies risks and which responds optimally to changes in the water, economic and technological conditions.
DIRECTED INTERNATIONAL TECHNOLOGICAL CHANGE AND CLIMATE POLICY
New Methods for Robust Decarbonization
This project explores how RDM methods can be integrated with Integrated Assessment Models (IAMs) to systematically study which forms of international technological cooperation are more robust to technological, economic and climate uncertainties. This study emphasizes that international technological change patterns come from the interaction of technology creation and diffusion dynamics across advanced and emerging nations. It contributes to the development of an integrated understanding of technological change at an international level and its implications for development and climate policy in the coming decades. The analysis offers lessons with respect to how the RMD methods and IAM models can be used to inform policies that support both climate mitigation and development goals, and which can respond to rapidly changing technological, political and environmental conditions.
ROBUST DECISION SUPPORT OF MEXICO'S ENERGY SECTOR
Guiding Sectoral Evolution for the Benefit of the Nation
This Conacyt funded project uses RDM methods for analyzing two key challenges of Mexico's energy sector after the energy reform. The first part of the project centers on analyzing policies and infrastructure investments which can increase the resiliency of Mexico's fuel and transportation infrastructure. The second part of the project combines RDM methods with the economic complexicty framework to quantify multiplier effects of different investment options at sub-national levels. These includes assessing long-term implications of energy investment for technological sophistication and economic diversification.