Profile

My research focuses on data-driven algorithm auditing to reduce inequalities (SDG 10), foster gender equity (SDG 5), tackle poverty (SDG 1), and promote health and well-being (SDG 3). I work with diverse algorithmic approaches, including machine learning, deep learning, and ranking systems, to address inequality, systemic bias, and fairness at individual, group, and relational levels. An important aspect of my work involves impact assessment through feedback loops and interventions. Leveraging multimodal data (e.g., network, tabular, and image data) and both real and synthetic data, I develop frameworks and applications tackling network inequality, fairness, and societal disparities, with contributions like high-resolution poverty maps and analyses of online visibility in academia. My goal is to advance equitable, impactful AI systems across societal domains. 

The coauthorship network below reflects years of shared work and generosity. I am grateful to everyone who has walked with me, from my early days at ESPOL in Ecuador, through the University of Saarland and the Max Planck Institute for Software Systems in Saarbrücken, GESIS in Cologne, and the University of Koblenz-Landau; to Central European University and the Complexity Science Hub in Vienna. I also thank the colleagues I met during internships at Stanford and USC-ISI. To all collaborators, mentors, and the students I began supervising during my postdocs, thank you for your trust, ideas, and support.

If my work resonates and you wish to collaborate, drop me an email. Ideas, curiosity, ambition, and kindness are always welcome.

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