Regular biography
Martin Jaggi is an Associate Professor in the Department of Computer Science at École Polytechnique Fédérale de Lausanne. His research focuses on optimization for machine learning and machine learning systems. He teaches courses such as 'Optimization for Machine Learning' (CS-439) and 'Topics in Machine Learning Systems' (CS-723). His work involves both theoretical and practical aspects of scalable algorithms for large datasets. Jaggi is affiliated with multiple departments including IINFCOM, MLO, and IC-SIN, and oversees academic and research activities within these units.
Scholar-generated biography
Martin Jaggi is a researcher specializing in AI, Machine Learning, and Optimization. His work focuses on advancing distributed and communication-efficient optimization techniques, particularly in the context of federated learning and large-scale machine learning. Jaggi has contributed to the development of methods for sparse convex optimization, gradient compression, and decentralized stochastic optimization. His research also explores the theoretical foundations of optimization algorithms and their applications in areas such as neural architecture search and medical language models. His publications highlight the importance of efficiency, scalability, and robustness in modern machine learning systems.