Ali Habibnia
@AliHabibniaI am an Assistant Professor in the Department of Economics and the Computational Modeling and Data Analytics, College of Science, Virginia Tech
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Repos
14
PRs
0
Growth
+18%
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Contribution activity over the past year
Thomas Robinson
@tsrobinson
Ben Cummings
@Ben-Cummings-VT
Vahid Balazadeh
@vdblm
Joshua French
@jfrench
Jonathan Gendron, Ph.D.
@jegendron
Top Repositories
This comprehensive, hands-on course provides a thorough exploration into the world of algorithmic trading, aimed at students, professionals, and enthusiasts with a basic understanding of Python programming and financial markets.
This course in applied data science covers the theoretical foundations of advanced quantitative approaches in machine learning, econometrics, risk and portfolio management, algorithmic trading, and financial forecasting. (first taught at Virginia Tech in 2019)
This intermediate applied econometrics course covers the theoretical, computational, and statistical underpinnings of the big data analysis. (first taught at Virginia Tech in 2018)
This course will introduce the student to classic machine learning algorithms and deep neural network structures. The style will be first to describe the theory and math behind algorithms and then demonstrate how to use Python to create and run the models.
"The History and New Trends of Measuring Dependence: From Bayes, Galton, and Pearson to the 21st Century" is a research undertaking led by Ali Habibnia as the principal investigator, with the assistance of Jonathan Gendron and Sanjana Rayani, who serve as research assistants from the Department of Economics at Virginia Tech.
This repository contains code, models, and tools for simulating and estimating portfolios based on constant and time-varying covariance matrices.
In this project, we delve into the principal constructs of quantum computing and quantum machine learning. Our primary focus resides in identifying and elucidating quantum computing solutions for the domain of econometric modeling, with particular emphasis on big data econometrics and nonlinear models.
A Nonlinearity Test for Principal Component Analysis: MATLAB Code
(Paper coming very soon to arXiv) This chapter of my dissertation uses simulations to test which meta-regression methodology specification performs optimally in the face of joint heterogeneity in the location and time that each study was conducted in.
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