Summary
Ali Movahedi is a Senior Data Scientist in Chicago at Parts Town, driving R&D in Gen AI, NLP, and large language models to fuel growth and innovation. He blends a rigorous mathematical foundation with a PhD in Complex Urban Networks and an MA in Economics from the University of Illinois Chicago, plus a Civil Engineering background from Sharif University, giving him a broad, quantitative lens on complex systems. In academia and industry, he built hundreds of ML/DL models, including 250+ LSTM forecasts for energy consumption in Chicago, and used XGBoost with SHAP to analyze interrelations among electricity, gas, and water usage in NYC buildings. Notably, his XGBoost model for accident detection achieved 99% accuracy and a 0.16% false-alarm rate, demonstrating a track record of high-impact, dependable insights. With Django web development experience, he bridges model development and production-grade applications, delivering scalable AI solutions at the intersection of research and real-world product.
4 years of coding experience
9 years of employment as a software developer
Mathematics and Physics Diploma, Mathematics and Physics Diploma at National Organization for Development of Exceptional Talents (Sampad)
Doctor of Philosophy - PhD Complex Urban Networks, Doctor of Philosophy - PhD Complex Urban Networks at University of Illinois at Chicago - Graduate College
Bachelor of Science - BS, Civil Engineering, Bachelor of Science - BS, Civil Engineering at Sharif University of Technology
English, Persian