Summary
Adeline Hillier is a Scientific ML Software Developer with seven years of experience building high-performance scientific software at the intersection of machine learning, data assimilation, and climate science. Trained at MIT in EECS, she has developed production-ready emulators, uncertainty-quantification pipelines, and neural differential models for climate and turbulence problems and recently joined JHU Applied Physics Laboratory. Her background spans image- and video-based signal recovery in Julia, U-Net–based denoising and segmentation, and interactive visualization tools that accelerate research iteration. Comfortable communicating results to interdisciplinary audiences, she has presented work to Caltech, MIT and large scientific meetings and produced first-of-their-kind results validating new uncertainty quantification approaches. Intentional and detail-oriented, she pairs a curiosity for physical systems with pragmatic software engineering to turn research ideas into reproducible, high-performance code.
6 years of coding experience
3 years of employment as a software developer
M.Eng. Electrical Engineering and Computer Science, M.Eng. Electrical Engineering and Computer Science at Massachusetts Institute of Technology
Newport Senior High School
Spanish, Portuguese