Summary
Amir Shahmoradi is an associate professor in the Division of Data Science with 12 years of interdisciplinary experience applying physics, computational methods, and statistical modeling to problems in high-energy physics, astrophysics, biomedical big data, and molecular evolution. He blends deep domain expertise in theoretical and computational physics with hands-on skills in high-performance computing, Bayesian multiscale modeling, Monte Carlo methods, and molecular dynamics to drive research from code optimization to translational biomedical analytics. His career spans academia and medical research centers, contributing to ischemic stroke studies, viral evolution, protein dynamics, and performance modernization of scientific codes. Based in Austin, he is as comfortable teaching scientific computing and Python/R as he is developing multilevel Bayesian techniques, bringing a rare combination of physics intuition and data-science rigor to complex, multi-scale problems.
12 years of coding experience
8 years of employment as a software developer
MSc, High Energy Astrophysics, MSc, High Energy Astrophysics at Michigan Technological University
PhD, Physics ( Biophysical / Plasma sciences ), PhD, Physics ( Biophysical / Plasma sciences ) at The University of Texas at Austin
BSc, High Energy Physics, BSc, High Energy Physics at Sharif University of Technology