Profile

Samy received his bachelor’s in civil engineering and a master’s in computer science and engineering both from the Free University of Brussels. During his master studies, he joined Sony Computer Science Laboratories for a three-month internship and fell in love with research. He decided to join Sony AI after graduating to grow further in this environment. His research focus is in neurosymbolic AI, where he explores non-classical logical operators and their mathematical properties in machine learning contexts.

Message

“My current focus at Sony AI is in the Gastronomy project and my expertise is in neurosymbolic AI. Neurosymbolic AI is a field that combines reasoning from complex knowledge representations, such as logical knowledge bases or semantic networks, with learning from data. I thrive to address topical industry problems such as understanding gastronomy and health by relying on machine learning capabilities and pre-existing domain and logical knowledge. When there are ways to efficiently let a neural network know that cats are mammals, without learning it from scratch, I want to explore them.”

Publications

Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists

AI4X, 2025
Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Chrysa Iliopoulou, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner

Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o…

Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

AIR, 2024
Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold

Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp…

What's Wrong with Gradient-based Complex Query Answering?

NeSy, 2023
Ouns El Harzli, Samy Badreddine, Tarek Besold

Multi-hop query answering on knowledge graphs is known to be a challenging computational task. Neurosymbolic approaches using neural link predictors have shown promising results but are still outperformed by combinatorial optimization methods on several benchmarks, including…