Regular biography
Nicolas Macris is an Adjunct Professor in the CS department at École Polytechnique Fédérale de Lausanne. His research interests include statistical mechanics, quantum mechanics, mathematical physics, error-correcting codes, and related areas. He has contributed to publications on topics such as matrix estimation, generalized linear models, and spatial coupling techniques. Macris teaches courses on quantum science and technology, quantum information processing, and learning theory. His work includes collaborations on topics like adaptive interpolation methods and threshold saturation for spatially-coupled codes.
Scholar-generated biography
Nicolas Macris is a researcher at Ecole Polytechnique Federale Lausanne, specializing in statistical physics, mathematical physics, information and coding theory, and high dimensional inference. His work explores the interplay between statistical physics and machine learning, focusing on high-dimensional generalized linear models, deep neural networks, and Bayesian inference. He has contributed to understanding computational to statistical gaps in learning neural networks and the optimality of approximate message-passing algorithms. His research also includes quantum algorithms for GHZ and W states and the analysis of spatially coupled codes. Macris' studies often involve rigorous mathematical techniques to analyze phase transitions and mutual information in complex systems.