Summary
Peijin Chen is a data scientist and machine learning engineer with nine years of experience, currently pursuing a PhD in Mathematics at UQAM after graduate studies at the University of Vermont. She specializes in deep learning and time series forecasting across NLP, computer vision, finance, and natural sciences, with strong hands-on experience in Python, PyTorch/TensorFlow, sktime, XGBoost/CatBoost, R, and cloud-based ML engineering on Google Cloud. Her background in pure mathematics and graduate-level statistics underpins a rigorous approach to supervised learning and forecasting problems. Peijin has taught and developed curriculum—from IB/AP calculus instruction to community-focused Python and Excel training at the New York Public Library—and authored data science posts and course content. Fluent in Mandarin and having lived 12 years in China, she bridges quantitative rigor with cross-cultural communication. She maintains practical projects on GitHub focused on deep learning for time series and is actively seeking opportunities to translate research-grade models into production.
9 years of coding experience
6 years of employment as a software developer
BA Mathematics, BA Mathematics at University of California, Berkeley
Deep Learning Specialization, Deep Learning Specialization at Coursera
hi universe
Master of Arts - MA Mathematics, Master of Arts - MA Mathematics at University of Vermont
Doctor of Philosophy - PhD Mathematics, Doctor of Philosophy - PhD Mathematics at UQAM | Université du Québec à Montréal
Master of Arts (M.A.) East Asian Studies, Master of Arts (M.A.) East Asian Studies at Stanford University
Chinese