Sam Raymond

Research Interests

Large language models; physics-informed machine learning; computational mechanics; particle-based simulation methods

Education

• BEng Monash University, Australia 2012
• MEng, Monash University, Australia 2015
• PhD, Massachusetts Institute of Technology 2020

Selected Publications

• Raymond SJ, Collins DJ, O’Rorke R, Tayebi M, Ai Y, Williams J. "A deep learning approach for designed diffraction-based acoustic patterning in microchannels." Scientific Reports. 10(1): 8745.
• Bell SM, Raymond SJ, Yin H, Jiao W, Goll DS, Ciais P, Olivetti E, et al. "Quantifying the recarbonization of post-agricultural landscapes." Nature Communications. 14(1): 2139.
• Raymond SJ, Cecchi NJ, Alizadeh HV, Callan AA, Rice E, Liu Y, Zhou Z, et al. "Physics-informed machine learning improves detection of head impacts." Annals of Biomedical Engineering. 50(11): 1534-1545.
• Zhan X, Liu Y, Raymond SJ, Alizadeh HV, Domel AG, Gevaert O, et al. "Rapid estimation of entire brain strain using deep learning models." IEEE Transactions on Biomedical Engineering. 68(11): 3424-3434.
• Raymond SJ, Baker S, Liu Y, Bustamante MJ, Ley B, Horzewski MJ, et al. "A low-cost, highly functional, emergency use ventilator for the COVID-19 crisis." Plos One. 17(3), e0266173.
• Raymond SJ, Jones B, Williams JR. "A strategy to couple the material point method (MPM) and smoothed particle hydrodynamics (SPH) computational techniques." Computational Particle Mechanics. 5: 49-58.

Courses

• ENGS 15.08: AI Demystified: A Roadmap to Understand Evolving Technologies
• ENGM 204: Data Analytics Project Lab

News

NVIDIA Deep Learning Institute Collaborates with Dartmouth to Build New Generative AI Teaching Kit
Mar 05, 2025