Rate My Professor Riyadh Baghdadi

Baghdadi's research focuses on the intersection of applied machine learning and compilers, including machine learning-guided automatic code optimization, deep learning-based heuristics for compilers, and compilers optimized for machine learning hardware accelerators and resource-limited platforms such as smartphones and autonomous vehicles. His key contributions include a deep learning-based cost model for automatic code optimization, which earned the Outstanding Paper Award at the Fourth Conference on Machine Learning and Systems (MLSys 2021); Caviar, an e-graph-based transformation rewriting system for code optimization (CC 2022); and a deep learning model for loop interchange (CC 2023). Earlier work features PENCIL, a platform-neutral compute intermediate language for accelerator programming (PACT 2015). Baghdadi has received the Meta Research Award 2022 (AI4AI, $48,000), bronze medals at ACM Student Research Competitions (CGO 2015 and 2012), and Google Summer of Code 2010. He leads the Modern Compilers lab at NYUAD, has taught courses at NYUAD, MIT, and Sorbonne University, mentored 18 students, organized workshops such as the 12th International Workshop on Polyhedral Compilation Techniques (2022), and served on program committees for MLSys 2023, IPDPS 2023, PACT 2021, and editorial boards for ACM TACO, TOPC, and others.