About
ahmed . khaled @ princeton . edu
Welcome to my tiny corner of the internet! I’m Ahmed, I work on optimization and machine learning. I’m a first-year Ph.D. student in the ECE department at Princeton University, advised by Prof. Chi Jin. I have a Bachelor’s degree in Computer Engineering from Cairo University, Egypt.
Before joining Princeton, I was fortunate to intern in the group of Prof. Peter Richtárik at KAUST in the summers of 2019/2020, where I worked on the distributed & stochastic optimization. Prior to this, I did some (applied) research on accelerating the training of neural networks with Prof. Amir Atiya.
Papers
(In reverse order of preparation)
- Proximal and Federated Random Reshuffling
- To appear in ICML 2022, with Konstantin Mishchenko and Peter Richtárik. (bibtex).
- FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning
- The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), with Elnur Gasanov, Samuel Horváth, and Peter Richtárik. (bibtex).
- Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
- Preprint (2020), with Othmane Sebbouh, Nicolas Loizou, Robert M. Gower, and Peter Richtárik. (bibtex).
- Random Reshuffling: Simple Analysis with Vast Improvements
- Advances in Neural Information Processing Systems 33 (NeurIPS 2020), with Konstantin Mishchenko and Peter Richtárik. (bibtex).
- Better Theory for SGD in the Nonconvex World
- Preprint (2020), with Peter Richtárik. (bibtex).
- Distributed Fixed Point Methods with Compressed Iterates
- Preprint (2019), with Sélim Chraibi, Dmitry Kovalev, Peter Richtárik, Adil Salim, and Martin Takáč. (bibtex).
- Tighter Theory for Local SGD on Identical and Heterogeneous Data
- The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020, with Konstantin Mishschenko and Peter Richtárik. (bibtex). Extends the workshop papers (a, b) below.
- Better Communication Complexity for Local SGD
- Oral presentation at the NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, with Konstantin Mishschenko and Peter Richtárik. (bibtex).
- First Analysis of Local GD on Heterogenous Data
- NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, with Konstantin Mishschenko and Peter Richtárik. (bibtex).
- Gradient Descent with Compressed Iterates
- NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, with Peter Richtárik. (bibtex).
- Applying Fast Matrix Multiplication to Neural Networks
- The 35th ACM/SIGAPP Symposium On Applied Computing (ACM SAC) 2020, with Amir F. Atiya and Ahmed H. Abdel-Gawad. (bibtex).
Talks
- On the Convergence of Local SGD on Identical and Heterogeneous Data
- Federated Learning One World Seminar (2020). Video and Slides.