Summary
Sam Polk is a mathematician and technical staff researcher at MIT Lincoln Laboratory with 11 years of experience applying machine learning to scientific, medical, and sociological problems. He designs explainable AI and human-machine teaming architectures—building preference-driven agents in Python, Julia, and MATLAB that are evaluated in virtual environments and large-scale human subject studies. His background spans healthcare EEG modeling, automated recommendation systems for decision support, and quantitative research in finance and economics, reflecting a rare blend of rigorous theory and practical deployment. Currently leading a program to let users constrain AI behaviors for smoother human-AI coordination, he brings a PhD-level mathematical toolkit and hands-on engineering to national security challenges. An unexpected thread in his profile is parallel academic work on neural computations of auditory perception, indicating a deep interest in how humans process complex signals that informs his approach to interpretable ML.
11 years of coding experience
1 year of employment as a software developer
Certificate of Completion Data Science, Certificate of Completion Data Science at General Assembly
Doctor of Philosophy - PhD Mathematics, Doctor of Philosophy - PhD Mathematics at Tufts University
Bachelor of Science (B.S.) and Bachelor of Arts (B.A.) Mathematics; Economics, Bachelor of Science (B.S.) and Bachelor of Arts (B.A.) Mathematics; Economics at University of Rochester
Mathematics study abroad, Mathematics study abroad at Budapest Semesters in Mathematics