I have designed and taught courses in econometrics, data science, machine learning, forecasting, and quantitative finance across undergraduate, graduate, and professional programs. My teaching emphasizes a combination of statistical foundations, computational implementation, and hands-on applications using real economic and financial data.
Virginia Tech & Selected Courses
Advanced Big Data Economics
Graduate and upper-level undergraduate courses in high-dimensional econometrics and modern statistical learning, including regularization, dimensionality reduction, factor models, forecasting, large covariance estimation, deep learning, and causal machine learning.
Data Science for Quantitative Finance
An applied quantitative-finance course covering econometrics, machine learning, volatility modeling, portfolio methods, algorithmic trading, reinforcement learning, and financial forecasting.
Machine Learning from Theory to Practice
An applied machine- and deep-learning course that connects the mathematical foundations of learning algorithms with Python implementation and real economic, financial, and business applications.
AI-Based Algorithmic Trading with Python
A hands-on course for practitioners and firms covering market data, trading-system design, strategy research and backtesting, machine learning for trading, execution, broker and exchange APIs, and live deployment.
Student feedback
Average rating for instructor preparation in Virginia Tech student evaluations.
| Instructor | Department of Economics | College of Science |
|---|---|---|
| 5.52/6 | 5.33/6 | 5.30/6 |
“The most helpful thing to me was how Dr. Habibnia presented both the theory and the practice for each topic.”
“He has deep practical and industry knowledge and always shares it with his students.”
“He explained concepts clearly, gave helpful examples, and encouraged questions, which made the material easier to understand.”
“I truly appreciate the freedom and flexibility with the final project to explore what we were interested in. I felt like I learned a lot during the process!”
“The notebooks are a great resource, and Dr. Habibnia is always willing to help out on projects by sharing academic literature.”
“It didn’t matter how simple or seemingly insignificant my questions were, Dr. Habibnia went out of his way to provide detailed answers.”
“One of the best courses in Virginia Tech that I’ve taken. The Professor is well informed, kind, enthusiastic and engaging.”
Source: Virginia Tech Student Perceptions of Teaching (SPOT), “The instructor was well prepared.” Department and college benchmarks use the same course-response weights as the instructor average. Quotations are selected anonymous student comments, reproduced verbatim.
Selected Academic Teaching
Machine and Deep Learning from Theory to Practice (M.Tech. in Data Science), with hybrid delivery combining remote and in-person teaching. Also Invited Instructor for Applications of Machine Learning (B.Tech. in Data Science) at NMIMS University, 2024.
Elementary Statistical Theory (ST102), Quantitative Methods (ST107), Time Series Forecasting (ST304), Applied Regression with R (ST211), and Factor Models in Time Series (Summer Programme, with Matteo Barigozzi).
Teaching Assistant, Global Business Environment (MBA).
Teaching Assistant: Econometrics (graduate); Macroeconomics I and II (undergraduate).
Instructor: Machine Learning Applied to Finance (graduate).
Selected Professional and Executive Education
- Machine Learning and Data Classifiers, module C7, Business Analytics program.
- Big Data and Machine Learning for Causal Inference and Treatment Effects with Python, TeIAS, 2022.
- Linear Algebra, Probability & Time Series Forecasting with Python, Tehran Stock Exchange, 2018.
- Algorithmic Trading with Python and Deep Learning and Machine Learning Applied to Trading, Tehran Stock Exchange, 2017.
- Technical Analysis of Financial Markets, delivered 25+ times at universities and institutes, 2008–2016.