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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. His interests lie at the intersection of computer systems and machine learning, developing systems that automatically enable non-experts to leverage the latest in high-performance computing and ML. He is known as the lead developer of Enzyme (NeurIPS '20), an automatic differentiation tool for LLVM capable of differentiating code in a variety of languages, after optimization, and for a variety of architectures. 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.
Heterogeneous Systems
Scientific Computing
Best Student Paper Finalist
Best Reproducibility Advancement Finalist
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