Carlos' goal is to address problems of social significance through computational methods and interdisciplinary research, and his current focus is on algorithmic fairness. His background is web mining and information retrieval, and he has been influential in the areas of crisis informatics and web content quality and credibility. He is a prolific, highly cited researcher who has received two test-of-time awards, four best paper awards, and two best student paper awards. His works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search. He currently leads the Web Science and Social Computing group at Universitat Pompeu Fabra.
The focus of my research is algorithmic fairness. Currently, I work on automated risk assessment methods that satisfy algorithmic fairness criteria, such as separability and sufficiency, validating them through interdisciplinary research in two distinct settings: university admission policies and criminal recidivism prediction. I also work on algorithms for fair recommendation in graph-based settings, such as social networks, seeking fairness in the sense of giving similar items similar exposure, as well as trying to steer people away from false or misleding information or harmful content.