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	May 8, 19:00 - 21:00, Optimization Methods and Software for Federated Learning (B5 L5 R5209), Konstantin Burlachenko, Ph.D. Student, Computer Science	PhD Dissertation Defense
	Jun 27, 18:00 - 20:00, Better Methods and Theory for Federated Learning: Compression, Client Selection, and Heterogeneity (B5 L5 R5209), Samuel Horváth, Ph.D. Student, Statistics	Federated learning, Optimization for Machine Learning, PhD Dissertation Defense
	May 12, 12:00 - 13:00, Error Feedback for Communication-Efficient First and Second-Order Distributed Optimization: Theory and Practical Implementation (B9 L2 R2325), Konstantin Burlachenko, Ph.D. Student, Computer Science	Federated learning, Graduate Seminar, software development
	Nov 11, 12:00 - 13:00, Langevin Monte Carlo as an optimization algorithm (B9 L2 H1 R2322), Adil Salim, Postdoctoral Research Fellow, Computer Science	Graduate Seminar, Langevin Monte Carlo, machine learning, optimization
	Jul 30, 16:00 - 18:00, Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters (KAUST), Filip Hanzely, Ph.D., Applied Mathematics and Computational Sciences	PhD Dissertation Defense
	Dec 2, 12:00 - 13:00, Proximal Splitting Methods for Convex Optimization: An Introduction (B9 L2 H1 R2322), Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering	Graduate Seminar
	May 30, 11:00 - 14:00, Stein Variational Gradient Descent and Consensus-Based Optimization: Towards a Convergence Analysis and Generalization (B3 L5 R5220), Lukang Sun, Ph.D. Student, Computer Science	PhD Dissertation Defense
	May 28, 10:00 - 12:00, The Many Faces of Compression: Theory and Practice in Federated Optimization (B3, L5, R5209), Elnur Gasanov, Ph.D. Student, Computer Science	communication efficiency, compression methods, PhD Dissertation Defense, proximal algorithms