CV

Education

• Ph.D. in Operations Research, Department of Mechanical & Industrial Engineering, University of Toronto, 2026 – present
  • Advisors: Peyman Mohajerin Esfahani (University of Toronto), Vahid Roshanaei (Rotman School of Management, University of Toronto), Amir Ardestani-Jaafari (University of British Columbia)
• M.Eng., emphasis in Data Analytics & Machine Learning, University of Toronto, 2025 – 2026
• B.Sc. in Applied Mathematics and Statistics, University of Toronto, 2021 – 2025

Research interests

• Stochastic programming
• Robust optimization
• Online algorithms
• Inverse optimization
• Applications in health care

Research experience

• Dynamic and robust loss-ratio models for home health care scheduling (ongoing)

Teaching

• Teaching Assistant, MIE1615: Markov Decision Processes, University of Toronto, Fall 2026

Industry experience

• Data Scientist Intern, Fidelity Canada, Toronto, ON, May 2025 – May 2026
  • Built a machine-learning quality-control system over 1M+ transaction records to flag potential transaction errors
  • Handled severe class imbalance with SMOTE variants and ADASYN, raising AUC from 75% to 82%
  • Developed an ensemble of LightGBM, XGBoost, and CatBoost with Bayesian hyperparameter optimization (Optuna)
• Data Analyst Intern, HuaChuang Security Company, Nanchang, China, Summer 2023
  • Developed a hierarchical clustering model for customer segmentation that improved the customer acquisition rate by 18%

Skills

• Programming: Python, R, SQL, Java, Git, LaTeX
• Machine learning & data: scikit-learn, XGBoost, PyTorch, TensorFlow, pandas, NumPy, Spark / PySpark
• Tools: Azure, Tableau, Power BI