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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	Ph.D. 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, Ph.D. Dissertation Defense
	Oct 27, 12:00 - 13:00, BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training (B9 L2 R2325), Laurent Condat, Senior Research Scientist, Computer Science	Distributed algorithms, Graduate Seminar, optimization, Signal and Image Processing
	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
	Dec 4, 08:30 - 11:00, First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data (B4/5 L0 A0215), Arto Maranjyan, Ph.D. Student, Computer Science	asynchronous algorithms, machine learning, optimization, Ph.D. Dissertation Defense, Training
	Nov 13, 12:00 - 13:00, First Provably Optimal Asynchronous SGD for Homogeneous and Heterogeneous Data (B9 L2 R2325), Arto Maranjyan, Ph.D. Student, Computer Science	asynchronous algorithms, Graduate Seminar, machine learning, optimization, Training
	Sep 15, 12:00 - 13:00, From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs (B9 L2 R2325), Peter Richtarik, Professor, Computer Science	AI, algorithms, Graduate Seminar, LLM, machine learning, optimization
	Sep 4, 12:00 - 13:00, From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs (B9 L2 R2325), Peter Richtarik, Professor, Computer Science	AI, algorithms, Graduate Seminar, LLM, machine learning, optimization
	Sep 16, 16:00 - 17:00, From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs (B1 L3 R3119), Peter Richtarik, Professor, Computer Science	AI, algorithms, LLM, machine learning, Mathematics and Applications Colloquium, optimization
	Nov 24 - 26, All day, KAUST Workshop on Distributed Training in the Era of Large Models (Auditorium between B4 & 5, L0, R0215)	Distributed algorithms, generative models, machine learning, ML, Workshop
	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 Science	Ph.D. 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 Science	Graduate Seminar
	Oct 28, 14:30 - 15:30, Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity (B1 L3 R3119), Arto Maranjyan, Ph.D. Student, Computer Science	Mathematics and Applications Colloquium
	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	Ph.D. 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, Ph.D. Dissertation Defense, proximal algorithms
	Nov 24, 12:00 - 13:00, Training Neural Networks at Any Scale (B9 L2 R2325), Volkan Cevher, Associate Professor, School of Engineering, Swiss Federal Institute of Technology (EPFL), Switzerland	deep learning, Graduate Seminar, machine learning, Numerical simulation and analysis, optimization, Reinforcement Learning