My research agenda is guided by the conviction that time and nonlinearity are the fundamental dimensions of economic and institutional behavior. Institutions, firms, and markets do not evolve in linear or predictable ways; they follow complex trajectories of persistence, adaptation, and rupture. Capturing these dynamics requires methodological frameworks that integrate temporal processes, structural interdependence, and the computational depth of artificial intelligence.

To address this challenge, I develop and apply AI-based methodologies that combine survival analysis, causal inference, and graph neural networks (GNNs). A central outcome of this agenda is the Graph-Embedded Hazard Model (GEHM)—a framework that merges survival econometrics with graph-based embeddings to model structural risk and systemic resilience. GEHM illustrates how artificial intelligence can be used not merely to predict firm exit, institutional collapse, or contagion, but to reveal the nonlinear pathways of adaptation and interdependence that underlie economic and financial systems.

In parallel, I advance causal-graph approaches for fairness, transparency, and accountability in high-stakes algorithmic decision-making. Traditional predictive models, while accurate, often conceal the mechanisms through which shocks propagate or interventions succeed. By embedding financial and institutional relations within graph structures and combining them with causal inference, my research seeks to uncover both direct and mediated channels of change. This reflects a broader ambition: to ensure that artificial intelligence in economics and policy domains is not only effective but also interpretable and normatively grounded.

Beyond methodological innovation, my work is driven by substantive questions:

How do institutions survive—or decay—amid technological and political disruption? What determines whether public policies become self-sustaining systems or fragile short-term equilibria? Can generative and graph-based models (GNNs and GANs) reveal the structural forces that govern economic resilience and inclusion?

By embedding survival analysis within network and generative frameworks, I aim to redefine how econometrics captures institutional dynamics, enabling simulations of counterfactual policy environments and enhancing the empirical and normative relevance of economic analysis. 

In sum, my research demonstrates how artificial intelligence can advance economic science—bridging machine learning and econometrics to create models that are both theoretically rigorous and practically consequential. My goal is to build a research program that deepens our understanding of resilience, risk, and development while equipping institutions to govern more effectively in an age of complexity and uncertainty.

Teaching is a natural extension of my professional and research journey. Over the past two decades, I have combined leadership roles in global firms, policy advising at multilateral institutions, and academic research in statistics and data science. This dual identity—professor and practitioner—defines my teaching philosophy: I aim to bridge methodological rigor with practical relevance, preparing students to use data and artificial intelligence not only as analytical tools, but as frameworks for responsible decision-making.

My teaching focuses on statistics, econometrics, and applied data science, with particular emphasis on how modern AI methods—such as survival models, graph neural networks, and machine learning architectures—can illuminate problems of risk, resilience, and innovation. I design courses around real-world dilemmas I have faced in practice, from modeling systemic risk and predicting firm survival to detecting fraud in financial systems. This case-driven pedagogy transforms the classroom into a laboratory where theory and practice constantly inform one another.

I have also taught extensively in executive education, working with managers, regulators, and policymakers across Latin America, Europe, and North America. Having lived the responsibilities of executives myself, I bring a unique ability to communicate complex technical concepts with clarity, translating them into actionable strategies. My global career has also shaped my teaching: by experiencing diverse institutional and cultural contexts, I equip students to see how methods operate differently across societies, markets, and governance structures.

Ultimately, my teaching philosophy is to train students as both analysts and decision-makers. By combining methodological depth, AI-driven approaches, and comparative insights, I seek to empower them to navigate the complex realities of business, policy, and research in a globalized world.