Prof. João Papa
Department of Computing, São Paulo State University, (Brazil)
Perspectives on Quantum-based Learning Approaches in Graphs
Quantum Machine Learning has gained attention recently due to the initial but prominent results in some domains. In lockstep, graph-based machine learning has been around for quite a while. Still, some basic graph algorithms that are the foundation for some machine learning models and are said to be efficient (polynomial) can no longer hold such an assumption in the modern world. Large-scale datasets turn some linear and quadratic algorithms prohibitive in real-time applications. This talk presents some advances in addressing graph-based algorithms in quantum computing, highlighting their applications in machine learning.
Joao Papa is a Full Professor at the Department of Computer Science, Sao Paulo State University, Brazil. He is a Fellow of the International Association for Pattern Recognition, the Asia-Pacific Artificial Intelligence Association, and the Alexander von Humboldt Foundation in Germany. He is also the Brazilian representative on the International Association for Pattern Recognition committee and an IEEE Senior Member. He was a visiting professor at Harvard University (2014-2015) and MIT (2024-2025). He is the head of the Data Science Program at the Sao Paulo Research Foundation and a member of the advisory committee in Computer Science at the Brazilian National Council for Scientific and Technological Development. He also received the Ibn Sina Global Scholarship at the King Fahd University of Petroleum and Minerals, Kingdom of Saudi Arabia.