Sara Adkins is an AI music technologist and machine learning engineer with 11 years of experience blending music performance and production-grade ML systems. She currently builds generative music tools at Suno and previously shipped high-performance LLM inference software at Neural Magic while also designing real-time generative engines used by hundreds of Twitch streamers. A Fulbright-funded MS in Sound and Music Computing underpins her research on steerable Transformer-XL models that generate loopable musical phrases—work that earned an Outstanding Student Award at EvoMUSART. At Bose she bridged research and product, optimizing speech-enhancement models to run on embedded devices and leading generative audio projects that produced patents and prototypes. Equally at home onstage live-coding with Tidal Cycles and looping electric guitar, she focuses on human-AI improvisation and timbre synthesis, bringing performer-first constraints to algorithm design. Notably, she has practical experience translating SOTA sparsification and quantization research into production-ready inference stacks.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Computer Science and Arts Computer Science and Music Technology, Bachelor of Computer Science and Arts Computer Science and Music Technology at Carnegie Mellon University
Master of Science - MS Sound and Music Computing, Master of Science - MS Sound and Music Computing at Queen Mary University of London
Contributions:1 review, 1 PR, 11 pushes in 2 years 9 months
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