Adrián Csiszárik is a Hungarian mathematician and AI researcher based at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest, where he works in the Artificial Intelligence research group. He holds a mathematics degree from Eötvös Loránd University (ELTE), and his research lies at the intersection of theoretical mathematics and machine learning. His main interests include neural network representation similarity, model combinability, gradient regularization, and optimal transport methods in generative models.
Csiszárik has co-authored several notable publications, including papers presented at NeurIPS and other top venues, and has contributed to both fundamental and applied AI research, often within EU- and ERC-funded projects. He is also involved in teaching and science communication, known for his clear explanations and active participation in academic and public forums, particularly in the context of AI’s role in fields like healthcare.