Christian Kuemmerle

Personal Website: http://ckuemmerle.com Degree Institution: Technical University of Munich Degree: Ph.D. in Mathematics
Research Areas: Mathematical Foundations of Machine Learning, Non-Convex Optimization, Trustworthy Machine Learning, Scalable Algorithms, Information Theory, Recommender Systems
Dr. Christian Kümmerle’s research interests are in the mathematical foundations of machine learning and the development and analysis of efficient algorithms for large scale data analysis. His research leverages continuous optimization to address computational and statistical challenges arising from data models involving graph, sparsity and low-rank structures, leading to scalable algorithms with provable guarantees. Dr. Kümmerle was a Postdoctoral Fellow at Johns Hopkins University from 2020 to 2022 and received a Ph.D. in Mathematics from Technical University of Munich. His research has been published at premier venues in machine learning (JMLR, ICML, NeurIPS) and in mathematics.