Reza Arabpour

WORK HISTORY

EDUCATION

ABOUT REZA ARABPOUR

I’m a Computational Science & Engineering M.Sc. candidate at McMaster University (GPA 3.9/4.0, full scholarship) with a passion for the intersection of deep learning and problem solving. I am currently working on geometric deep Learning in finance. As a graduate student at Mac and Vector Institute, I’ve built and optimized hypernetworks that approximate complex stochastic processes, parallelizing them across multi-GPU clusters to slash compute times by over 60×. I’m currently innovating in Parameter-Efficient Fine-Tuning (PEFT) to make large-scale models faster, more accessible/sustainable, and cheaper to run.My academic journey began with a B.Sc. in Applied Mathematics from the University of Tehran, where I earned a Bronze medal in ACM ICPC West Asia, and I have since explored a broad spectrum of machine learning methodologies, from architecture design to optimization and evaluation. As co-founder and quantitative researcher, I engineered evolutionary-algorithm-driven trading strategies and conducted extensive Monte Carlo backtesting to maximize risk-adjusted returns.I’ve equally valued both industry and academic experiences, TA’d three graduate-level machine learning and finance-related courses at McMaster, supported over 500 undergraduate students in Python, and organized bi-weekly CSE seminars that bridged cutting-edge theory with real-world applications. I’m always keen to connect with fellow researchers and practitioners in deep learning and finance.