Sam Raymond

Overview

Professor Raymond is an adjunct assistant professor of engineering at Dartmouth and leads the Raymond Research Lab (RRL). His research examines engineering in the age of AI, with interests in AI agents for engineering, physics-informed machine learning, and the foundations of machine intelligence. He is interested in how machines represent their own capabilities and limitations, and how self-models relate to questions about machine consciousness.

Raymond earned his PhD in computational science and engineering at MIT and held postdoctoral appointments at Stanford and MIT. He previously worked as a data scientist at Databricks and is the founding engineer at Apex Ultra. He served as principal investigator for the NVIDIA Generative AI Teaching Kit, developed with NVIDIA, and co-created two Databricks/edX courses on large language models.

Research Interests

AI agents for engineering; physics-informed machine learning and models that learn physical structure; large language models; machine intelligence, self-models and consciousness; computational mechanics; particle-based simulation

Education

BEng Monash University, Australia 2012
MEng, Monash University, Australia 2015
PhD, Computational Science and Engineering, 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.