Carlos Eduardo Ribeiro de Mello

Academic Trajectory: Professor Carlos Eduardo Ribeiro de Mello has a robust and diverse academic trajectory. He graduated in Computer Science from the Federal University of Rio de Janeiro (UFRJ), where he achieved academic distinction (laude). He continued his studies at COPPE/UFRJ, where he completed his master’s degree in 1 year and then entered the doctoral program. During his doctoral studies, he joined the program at École Centrale Paris, currently Université Paris-Saclay, in the Applied Mathematics Laboratory, consolidating his training in statistics and artificial intelligence.

In France, he completed a joint doctorate, with supervision in both Brazil and France, under Carlos Pedreira and Geraldo Zimbrão at COPPE and Marie-Aude Aufaure at École Centrale Paris. This provided a highly independent and diverse training experience, offering contact with various academic methodologies.

After completing his doctorate, he joined the Federal Rural University of Rio de Janeiro (UFRRJ) as a professor, where he contributed to the creation of the Department of Computer Science and collaborated in structuring the Master’s program in Digital Humanities. After 6 years, he was reassigned to the faculty of UNIRIO, becoming a reference in Data Science and consolidating his participation in the PPGI-UNIRIO program. Currently, he is the professor responsible for the program’s Data Science area.

Scientific Contribution: Professor Mello has distinguished himself at the intersection of Data Science and Artificial Intelligence, with an emphasis on applied statistics and quantitative methodology. His research ranges from improving predictive modeling and machine learning techniques to developing econometric models for analyzing complex data and quantitatively evaluating public policies. His innovative contributions strengthen both scientific advancement and the practical application of these methodologies.

He has presented several impactful scientific contributions, consolidating his work in different research areas. In 2022, he published the article “Less is More: Improving Neural-Based Collaborative Filtering Using Landmark Modeling,” in which he outlined an innovative approach to recommendation systems. The study demonstrated that simplified modeling can yield more effective results than traditional neural network-based techniques, offering a new perspective on the optimization of these systems.

Furthermore, his research has had a significant impact on the field of public health. In collaboration with the Institute for Applied Economic Research (IPEA), he contributed a study on the price elasticity of medical products acquired by the government, developing a mixed-effects econometric model. The objective was to predict the impacts of government purchases on the health market, contributing to the formulation of more efficient policies. This study was integrated into IPEA reports and has the potential to be published as a book chapter.

Another relevant theme in his research is the privacy and anonymization of health data. He coordinated a study analyzing vulnerabilities in anonymized SUS (Brazilian Unified Health System) databases, demonstrating how de-identified data can still enable the re-identification of individuals. This work made important contributions to the discussion on ethical and regulatory aspects of privacy in healthcare, highlighting the challenges and risks of protecting sensitive data in the public sector.

His work continues to influence both academic research and the formulation of public and regulatory policies, emphasizing the importance of mathematical modeling and artificial intelligence in analyzing complex systems.

In addition to these contributions, Professor Carlos Eduardo is interested in expanding his research to develop methodologies for evaluating public health policies using open data and advanced statistical models.

Contribution to the PPGI: Within the PPGI-UNIRIO, Professor Mello plays a crucial role in ​​Data Science, consolidating his work at the intersection of data and public policy. His work strengthens the program’s vision as an applied space focused on solving real-world problems through technology. He has also been a key liaison between the program and external initiatives, including partnerships with IPEA and the City Hall of Rio de Janeiro. He was the coordinator of the PPGI-UNIRIO in 2021.

Researcher Training: Professor Mello has played a fundamental role in training students who pursue careers in both academia and the private sector. His advisees have held prominent positions in various fields, reflecting the quality and impact of his mentorship.

Among its alumni, two former students have become university professors in the United States, working at renowned institutions: one at the University of Arizona and the other at the University of Colorado.

In the private sector, other alumni have achieved prominent positions at large technology companies, including Facebook, and at data sector startups, demonstrating the applicability of the knowledge acquired under his guidance.

Prof. Mello continues to mentor students in highly relevant projects. One student stands out for completing a sandwich doctorate abroad (in 2024), further expanding the international reach of his advisees.

National and International Visibility: Prof. Mello’s visibility has been expanding through his academic work and scientific outreach. He maintains an active LinkedIn profile, where he publishes technical and academic content, with over 9,000 followers. His content aims to translate science for the general public and to connect academia with society’s demands.

He also started a YouTube channel where he interviewed researchers and discussed topics in data science and artificial intelligence. In addition, he participates in projects with IPEA and the Ministry of Health, reinforcing his work in initiatives with national impact.

1. Strengthen institutional partnerships, including a possible postdoctoral position at IPEA.
2. Deepen research in public policy and health, with more studies on data science applied to precision medicine.
3. Expand collaborations with companies and government agencies to attract new funding.
4. Increase academic and social visibility through scientific publications and outreach.