News

• Aug'23: Chen-Yu (Elton) has defended his PhD thesis titled “Tackling the Communication Bottlenecks of Distributed Deep Learning Training Workloads” and will next join Bytedance (USA). Congratulations!
• Mar'23: Arnaud has defended his PhD thesis titled “Verification and Privacy Techniques for Improving the Trustworthiness of Neural Networks” and will next join Nokia Bell Labs. Congratulations!
• LineFS wins the Best Paper Award at SOSP'21.
• Rethinking gradient sparsification as total error minimization accepted as spotlight paper (top 3%) at NeurIPS'21.
• We organize a tutorial on Network-Accelerated Distributed Deep Learning at SIGCOMM'21.
• In our NSDI'21 paper we demonstrated how to accelerate distributed ML via in-network aggregation with SwitchML. In our upcoming SIGCOMM'21 paper introducing OmniReduce, we are advancing streaming aggregation to leverage the sparsity of large models’ gradient vectors to accelerate training.
• In the GRACE project, we survey popular gradient compression techniques for distributed deep learning and perform a comprehensive comparative evaluation. Read our ICDCS'21 paper.

Teaching

• CS 230 - Computer Systems Security (Summer 2020)
• CS 240 - Computing Systems and Concurrency (Fall 2022) (Fall 2021) (Fall 2019) (Fall 2018) (Fall 2017)
• CS 294E - Computer Systems Support for AI (Fall 2021) (Fall 2019 as CS 290E)
• CS 345 - Advanced Distributed and Networked Systems (Spring 2020) (Spring 2019) (Spring 2017 as CS 390G)
• CS 394B - Blockchain Systems and Cryptocurrencies (Spring 2018)

Open Positions

I’m always looking for bright and enthusiastic people to join my group. If you are looking to do a PhD with me, thank you for your interest, but please read this first. If you don’t I will know, and I’m afraid I will have to ignore your message.

• New positions: We invite applications for a post-doctoral researcher interested in working on optimizing distributed machine learning (ML) systems. See details here.
• Internships: KAUST Visiting Student Research Program (VSRP) projects on High-efficiency AI and ML distributed systems at Big-Learning scales and on Making ML-based Networked Systems more Trustworthy