I am a third-year Ph.D. student in Computer Science at the University of Waterloo and a member of the WatCL lab. I am advised by Kimon Fountoulakis, and my research explores reasoning in neural networks by focusing on their ability to learn algorithmic tasks. I am also broadly interested in theoretical machine learning and graphs. I previously studied AI and Robotics (M.Sc.) at Sapienza University of Rome, and Mech. Eng. (B.Sc.) at the Federal University of Santa Catarina.

During Summer 2025, I joined Amazon New York as an Applied Scientist Intern in the SCOT team, where I worked with Ruijun Ma and Youxin Zhang on inbound event forecasting. Prior to that, in 2022, I interned at Huawei’s Noah’s Ark Lab, focusing on Federated Learning and Domain Generalization with Guojun Zhang, and exploring invariant graph representations with Yingxue Zhang.

Publications

• Learning to Add, Multiply, and Execute Algorithmic Instructions Exactly with Neural Networks
  Artur Back de Luca, George Giapitzakis & Kimon Fountoulakis. NeurIPS, 2025
• Exact Learning of Permutations for Nonzero Binary Inputs with Logarithmic Training Size and Quadratic Ensemble Complexity
  George Giapitzakis, Artur Back de Luca & Kimon Fountoulakis. High-dimensional Learning Dynamics Workshop @ ICML, 2025
• Positional Attention: Expressivity and Learnability of Algorithmic Computation
  Artur Back de Luca, George Giapitzakis, Shenghao Yang, Petar Veličković & Kimon Fountoulakis. ICML, 2025
• Simulation of Graph Algorithms with Looped Transformers
  Artur Back de Luca & Kimon Fountoulakis. ICML, 2024
• Local Graph Clustering with Noisy Labels
  Artur Back de Luca, Kimon Fountoulakis & Shenghao Yang. ICLR, 2024
• Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
  Artur Back de Luca, Guojun Zhang, Xi Chen & Yaoliang Yu. Preprint, 2022