William Moses is a Ph.D. Candidate at MIT, where he also received his M.Eng in electrical engineering and computer science (EECS) and B.S. in EECS and physics. William's research involves creating compilers and program representations that enable performance and use-case portability, thus enabling non-experts to leverage the latest in high-performance computing and ML. He is known as the lead developer of Enzyme (NeurIPS '20, SC '21), an automatic differentiation tool for LLVM capable of differentiating code in a variety of languages, after optimization, and for a variety of architectures and the lead developer of Polygeist (PACT '21), a polyhedral compiler and C++ frontend for MLIR. He has also worked on the Tensor Comprehensions framework for synthesizing high-performance GPU kernels of ML code, the Tapir compiler for parallel programs (best paper at PPoPP '17), and compilers that use machine learning to better optimize. He is a recipient of the U.S. Department of Energy Computational Science Graduate Fellowship and the Karl Taylor Compton Prize, MIT's highest student award.