Summary
Yifeng Qi is a quantitative researcher with eight years of experience blending computational modeling, machine learning, and theoretical chemistry to solve complex, data-driven problems. Currently at Citadel Securities, he applies rigorous quantitative methods honed during a PhD in Computational Biophysics at MIT to real-world finance and trading challenges. His background spans interdisciplinary research roles and industry internships, including building AutoML-driven EfficientNet pipelines for personalized ML at Facebook. Comfortable moving between high-performance research code and production systems, he brings both academic depth and pragmatic engineering. Based in Miami, he leverages a strong foundation in chemical physics from USTC and diverse research stints at UCLA and MIT to deliver novel modeling approaches. Colleagues describe him as someone who turns abstract theoretical insight into scalable computational solutions.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Biophysics/Theoretical Chemistry, Doctor of Philosophy - PhD, Computational Biophysics/Theoretical Chemistry at Massachusetts Institute of Technology
Bachelor of Science - BS, Chemical Physics, Bachelor of Science - BS, Chemical Physics at University of Science and Technology of China
Summer session, Summer session at University of California, Los Angeles
English, Chinese