معرفی
Iker Perez is a Transitional Assistant Professor in the Department of Statistics at the University of Nottingham, UK. He is affiliated with the Faculty of Science, focusing on interdisciplinary research at the intersection of applied mathematics, statistics, and computational methods.
Research Interests: His work spans Bayesian numerical homogenization for PDEs, federated learning dynamics, control theory for wave phenomena, and neural ordinary differential equations (neural ODEs). He also investigates optimal convergence rates in finite element approximations of Sobolev constants.
Recent Work: His publications explore clustering mechanisms in transformer architectures, game-theoretic perspectives in federated learning, and cluster-based classification frameworks using neural ODEs. These contributions highlight his expertise in integrating mathematical rigor with modern machine learning paradigms.
Awards & Grants: No specific awards or grants are explicitly mentioned in the provided text, but his active publication record suggests ongoing research activity.
Labs & Teams: Affiliation with specific research groups or collaborative projects is not detailed in the text.
