Sami Alabed

I am currently a final-year PhD student in the Systems Research Group of the University of Cambridge Computer Laboratory being supervised by Dr Eiko Yoneki. I am also a Doctoral Student at The Alan Turing Institute. My main research interest is the intersection of Machine Learning and Computer systems design. My thesis is on designing computer systems with probabilistic graphical models as building blocks, enabling explainable and robust auto-tuning with Bayesian Optimization and failures recovery using Causal Contextual Bandit.

I obtained a distinction in the MPhil Advanced Computer Science at the University of Cambridge in 2019. I was awarded the Cambridge Trust and the Students of Cambridge awards to fund my MPhil. Before then, I graduated with a first from The University of Manchester in a BSc Computer Science with Industrial Experience. I have received multiple awards in Manchester for improving the students’ life and employability prospects at the department.

Previously I worked at Amazon, Google, Twitter, and DeepMind on large scale distributed systems. I have been to many hackathons where I try quick ideas or implement interesting papers, you can see some of the projects hosted on my GitHub.

Latest publications

  • NeurIPS22 MLForSystems - Alabed, S., Grewe, D., Franco, J., Chrzaszcz, B., Natan, T., Norman, T., Rink, N.A., Vytiniotis, D. and Schaarschmidt, M., 2022. Automatic Discovery of Composite SPMD Partitioning Strategies in PartIR. arXiv preprint arXiv:2210.06352. Preprint.
  • EuroSys22 MLSystems - Alabed, Sami, and Eiko Yoneki. “BoGraph: structured bayesian optimization from logs for expensive systems with many parameters.” In Proceedings of the 2nd European Workshop on Machine Learning and Systems, pp. 45-53. 2022. Preprint, Source Code, Slides, Poster, Recording.
  • EuroSys21 MLSystems - Alabed, Sami, and Eiko Yoneki. “High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB.” In Proceedings of the 1st Workshop on Machine Learning and Systems, pp. 111-119. 2021. Proceeding, Slides, Recording.
  • MPhil Thesis - Alabed, Sami. “RLCache: Automated Cache Management Using Reinforcement Learning.” arXiv preprint arXiv:1909.13839 (2019). Thesis, Source Code.

Project supervisions

  • Zak Singh, Deep Reinforcement Learning for Equality Saturation. Source code.
  • Sean Parker, RLFlow: Optimising Neural Network Subgraph Transformation with World Models. Preprint.
  • Ross Tooley, Auto-tuning Spark with Bayesian Optimization.

Check Dr Eiko’s Yoneki webpage for list of project suggestions I am happy to co-supervise.


Technical Program Committee