Hello!

I am Samar (pronounced as Summer or /ˈsʌmə(ɹ)/), a PhD candidate at the University of Pennsylvania. I work under the supervision of Prof. Alejandro Ribeiro. I spend my time at Penn exploring how to make neural networks more efficient, reliable, and generalizable for solving optimization problems, going beyond imitation learning. A central theme of my work is descent neural networks, which are models whose layers emulate optimization dynamics. I study these networks in settings spanning unconstrained, distributed, constrained, and stochastic optimization. I am also interested in graph learning and currently investigating generative modeling for graph-structured signals. My ongoing research aims to extend these threads to optimization-driven generative models, with the goal of developing principled learning systems for decision making under constraints. Prior to joining Penn, I was a lecturer assistant at Port Said University, Egypt, from which I had received B.Sc. and M.Sc. degrees in Electrical Engineering.

Reach out at selaraby (at) seas (dot) upenn (dot) edu.

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News
Sep. 2025: Three papers got accepted to NeurIPS workshops. Details to follow.
Sep. 2025: Two new preprints are out: Unrolled graph neural networks for constrained optimization; and Graph signal generative diffusion models.
Sep. 2025: Our paper Generative diffusion models for resource allocation in wireless networks got accepted to IEEE Int. Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Oral.
Apr. 2025: Our new paper Generative diffusion models for resource allocation in wireless networks is out.
Apr. 2025: Gave a talk about our work Robust stochastically-descending unrolled networks at ICASSP 2025.
Aug. 2024: Our new paper Robust stochastically-descending unrolled networks was accepted to IEEE transactions on Signal Processing.
June 2024: Gave a Talk, Robust unrolled networks, at BIRS workshop.
Oct. 2023: Presented a poster at the 2023 Fall Fourier Talks at University of Maryland.
July 2023: Presented a poster at the institute for learning-enabled optimization at scale (TILOS) at UCSD.
May 2023: A new pre-print, Stochastic unrolled federated learning, is out.
May 2023: A short talk for Space-time graph neural networks with stochastic graph perturbations is now available.
Feb. 2023: Our paper Space-time graph neural networks with stochastic graph perturbations was accepted to ICASSP 2023.
Oct. 2022: A new pre-print is out, Space-time graph neural networks with stochastic graph perturbations.
Apr. 2022: Our paper Space-time graph neural networks was accepted to ICLR 2022. A five-minute talk is now available.