TEACHING

Teaching and mentoring activities in AI, machine learning, and computational finance.

Teaching Experience

As a Teaching Assistant at McMaster University, I have supported over 500 undergraduate students across multiple graduate and undergraduate courses, focusing on machine learning, computational finance, and mathematical computation. My teaching philosophy emphasizes bridging theoretical foundations with practical applications, helping students develop both conceptual understanding and hands-on programming skills.

Current Teaching Roles

Graduate Teaching Assistant - McMaster University (2023 - Present)

Graduate-Level Courses:

  • MFM 713 - Computational Finance II (Dr. Anastasis Kratsios)
    • Numerical solutions to PDEs and SDEs
    • Exotic and path-dependent options pricing
    • Free boundary problems for American options
  • MFM 714 - Topics in Risk Management (Dr. David Lozinsky)
    • Credit risk capital and counterparty risk
    • Risk in retail portfolios
    • Algorithmic and high-frequency trading (HFT)
    • Financial technologies and applications
  • MFin 704 - Numerical Methods in Finance (Dr. Michael Milewski)
    • Financial data processing pipelines
    • Data integration from Yahoo Finance API and Bloomberg
    • Insightful analysis and reliable forecasting methods

Undergraduate Course:

  • Math 1MP3 - Introduction to Mathematical Scientific Computation (Dr. Erin Clements)
    • Selected as TA 4 times in a row due to excellent performance
    • Python programming and scientific computing
    • Supporting 500+ students with both instructor and student satisfaction

CSE Seminar Organizer (Fall 2024)

  • Organized bi-weekly Computational Science & Engineering seminars
  • Contacted and invited guest speakers from various fields and universities
  • Planned speaker commute and handled all booking and catering responsibilities
  • Bridged cutting-edge research with real-world applications

Previous Teaching Experience - University of Tehran (2020-2022)

Computer Science Courses:

  • Data Structures and Algorithms
  • Advanced Programming in C++
  • Introduction to Graph Theory
  • Fundamentals of Computer Science and Programming

Areas of Expertise

Machine Learning & AI

  • Geometric Deep Learning: Hypernetworks and manifold-based approaches
  • Parameter-Efficient Fine-Tuning (PEFT): Making LLMs more accessible and sustainable
  • High-Performance Computing: Multi-GPU execution and parallel algorithms
  • Neural Architecture Design: Advanced architectures for complex problems

Computational Finance

  • Stochastic Processes: Synthetic data generation and forecasting
  • Quantitative Modeling: Monte Carlo methods and evolutionary algorithms
  • Algorithmic Trading: Strategy development and backtesting

Programming & Tools

  • Python: Advanced PyTorch, TensorFlow, HuggingFace Transformers
  • Scientific Computing: NumPy, SciPy, Pandas, Statsmodels
  • Development Tools: Git, Linux servers, Docker, AWS, web scraping
  • Languages: C, C++, Python, R, MATLAB, Bash scripting, SQL

Leadership Experience

  • CSE Seminar Committee (McMaster University, Fall 2024): Organized bi-weekly seminars
  • Editorial Board Member (University of Tehran, 2022): Applied Mathematics section of “Jong-e Riazi” publication
  • Volunteer Coordinator (Iranian Hemophilia Association, 2018): Organized fundraising and volunteer activities

For teaching opportunities or collaboration, please feel free to reach out via email.