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Assistant Professor
Sulaiman A. Alghunaim is an Assistant Professor at Kuwait University, electrical engineering department. He received his B.S. in electrical engineering from Kuwait University in 2013. He received his M.S. degree in electrical engineering and Ph.D degree in electrical and computer engineering from the University of California, Los Angeles in 2016 and 2020, respectively. His research interests include optimization algorithms design and analysis, as well as their applications in multi-agent systems, control, signal processing, and machine learning. His current research focuses on the design and analysis of distributed optimization and learning algorithms.
Degrees and Certificates
- BS in Electrical Engineering; Kuwait University Kuwait, 2013.
- MS in Electrical Engineering (Control and Signal Processing), University of California Los Angeles Los Angeles, USA, 2016.
- Ph.D in Electrical and Computer Engineering (Control and Signal Processing), University of California Los Angeles Los Angeles, USA, 2020.
Research Interests
Design and analysis of optimization algorithms and their applications in:
- Multi-agent systems (distributed processing)
- Control and signal processing
- Machine learning.
Publications
Journals and Conferences
- [J] S. A. Alghunaim, ‘‘Local exact-diffusion for decentralized optimization and learning,’’ IEEE Trans. Automatic Control, vol. 69, no. 11, pp. 7371-7386, Nov. 2024. [arXiv]
- [C] H. Cai, S. A. Alghunaim, and A. H. Sayed, ‘‘Diffusion optimistic learning for min-max optimization,’’ Proc. IEEE ICASSP, pp. 1-5, Seoul, South Korea, April 2024.
- [J] S. A. Alghunaim and K. Yuan, ‘‘An enhanced gradient-tracking bound for distributed online stochastic convex optimization,’’ Signal Processing, Volume 217, April 2024. [arXiv]
- [J] K. Yuan, S. A. Alghunaim, and X. Huang, ‘‘Removing data heterogeneity influence enhances network topology dependence of decentralized SGD,’’ Journal of Machine Learning Research (JMLR), vol. 24, no. 280, pp. 1–53, 2023. [arXiv]
- [C] H. Yuan, S. A. Alghunaim, and K. Yuan, ‘‘Achieving linear speedup with network-independent learning rates in decentralized stochastic optimization,’’ Proc. IEEE CDC, pp. 139-144, Marina Bay Sands, Singapore, December 2023.
- [C] E. D. H. Nguyen, S. A. Alghunaim, K. Yuan, and C. A. Uribe, ‘‘On the performance of gradient tracking with local updates,’’ Proc. IEEE CDC, pp. 4309-4313, Marina Bay Sands, Singapore, December 2023. [arXiv]
- [J] S. A. Alghunaim and K. Yuan, ‘‘A unified and refined convergence analysis for non-convex decentralized learning,’’ IEEE Trans. on Signal Processing, vol. 70, pp. 3264–3279, June 2022. [arXiv]
- [J] S. A. Alghunaim, Q. Lyu, M. Yan, and A. H. Sayed, ‘‘Dual consensus proximal algorithm for multi-agent sharing problems,’’ IEEE Trans. on Signal Processing, vol. 69, pp. 5568-5579, September 2021.
- [J] S. A. Alghunaim, E. K. Ryu, K. Yuan, and A. H. Sayed, ‘‘Decentralized proximal gradient algorithms with linear convergence rates,’’ IEEE Trans. Automatic Control, vol. 66, no. 6, pp. 2787-2794, June 2021. [arXiv]
- [C] S. A. Alghunaim, M. Yan, and A. H. Sayed, ‘‘A multi-agent primal-dual strategy for composite optimization over distributed features,’’ in Proc. EUSIPCO 2020, pp. 2095-2099, Amsterdam, The Netherlands, January 2021. [arXiv]
- [J] S. A. Alghunaim, K. Yuan, and A. H. Sayed, ‘‘A proximal diffusion strategy for multi-agent optimization with sparse affine constraints,’’ IEEE Trans. Automatic Control, vol. 65, no. 11, pp. 4554-4567, November 2020. [arXiv]
- [J] K. Yuan, S. A. Alghunaim, B. Ying, and A. H. Sayed. ‘‘On the influence of bias-correction on distributed stochastic optimization,’’ IEEE Trans. on Signal Processing, vol. 68, 4352-4367, July 2020. [arXiv]
- [J] S. A. Alghunaim and A. H. Sayed, ‘‘Linear convergence of primal-dual gradient methods and their performance in distributed optimization,’’ Automatica, Volume 117, July 2020. [arXiv]
- [J] S. A. Alghunaim and A. H. Sayed, ‘‘Distributed coupled multi-agent stochastic optimization," IEEE Trans. Automatic Control, vol. 65, no. 1, pp. 175-190, January, 2020. [arXiv]
- [C] S. A. Alghunaim, K. Yuan, and A. H. Sayed, ‘‘A linearly convergent proximal gradient algorithm for decentralized optimization,’’ in Advances on Neural Information Processing Systems (NeurIPS), volume 32, Vancouver, Canada, December 2019. [arXiv]
- [C] K. Yuan, S. A. Alghunaim, B. Ying, and A. H. Sayed, ‘‘On the performance of exact diffusion over adaptive networks,’’ Proc. IEEE CDC, pp. 4898-4903, Nice, France, December 2019.
- [C] L. Cassano, S. A. Alghunaim, and A. H. Sayed, ‘‘Team policy learning for multi-agent reinforcement learning,’’ Proc. IEEE ICASSP, pp. 3062-3066, Brighton, UK, May 2019.
- [C] S. A. Alghunaim, K. Yuan, and A. H. Sayed, ‘‘Dual coupled diffusion for distributed optimization with affine constraints,’’ Proc. IEEE CDC, pp. 829-834, Miami Beach, FL, USA, December 2018.
- [C] S. A. Alghunaim and A. H. Sayed, ‘‘Distributed coupled learning over adaptive networks,’’ Proc. IEEE ICASSP, pp. 6353-6357, Calgary, Canada, April 2018.
- [C] S. A. Alghunaim, K. Yuan, and A. H. Sayed, ‘‘Decentralized exact coupled optimization,’’ Proc. Allerton Conference on Communication, Control, and Computing, pp. 338-345, Allerton, IL, October 2017.
- [C] J. Y. Ishihara, S. A. Alghunaim, ‘‘Diffusion LMS filter for distributed estimation of systems with stochastic state transition and observation matrices,’’ Proc. American Control Conference (ACC), pp. 5199-5204, Seattle, USA, May, 2017.