Alireza Tehrani is a research scientist in machine learning with an 11-year track record of bridging mathematics, high-performance scientific computing, and quantum chemistry to deliver production-grade ML models and open-source tooling. He holds a PhD from Queen’s University where he built a scalable geometric-ML pipeline processing 1TB+ of data and 100k+ 3D objects, cutting preprocessing time by ~10,000× using MPI and custom CUDA kernels. His work produced a novel 3D surface descriptor that enabled state-of-the-art hydration free-energy predictions and a virtual-screening model with 0.97 AUC, and he has led development of GPU-accelerated libraries (e.g., CuGBasis) that outperform predecessors by orders of magnitude. Comfortable moving ideas from theory to deployment, he has secured competitive research funding, supervised students, and standardized cross-platform CI-driven software for the quantum and cheminformatics communities. Outside research he’s an avid snowboarder and tennis player, bringing the same focus on optimization and performance to both code and life.
11 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Mathematics and Computer Science, Minor in Chemistry, Bachelor of Science - BS, Mathematics and Computer Science, Minor in Chemistry at McMaster University
Doctor of Philosophy - PhD, Quantum Chemistry, Doctor of Philosophy - PhD, Quantum Chemistry at Queen's University
Master of Science - MS, Mathematics, Master of Science - MS, Mathematics at University of Guelph
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