My research focuses on the intersection of statistical machine learning and big data econometrics, with a particular interest in the high-dimensional nonlinear time-series analysis and their applications in macroeconomic/financial forecasting and estimation of big financial networks. I received my Ph.D. (2017) from the London School of Economics and Political Science, Department of Statistics.
I also work occasionally as a consultant for financial firms and governmental organizations for all business related to pattern recognition and knowledge discovery, especially computer modeling and forecasting. I have been in the industry for a few years working as a trader and portfolio strategist, and I developed different algorithmic trading strategies.
Big Data Econometrics, Statistical Machine Learning, Deep Learning, Nonlinear Econometric Analysis, Forecasting, Computational Finance, Network Analysis, Nonlinear Systems, Hyperparameter Optimization
- I will present “Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)” at Stanford Institute for Theoretical Economics – Asset Pricing Theory and Computation Summer Workshop, 18 August 2019 | USA
- I will present “Financial Time-Series Analysis: A Deep Learning Approach” at MATLAB Computational Finance Conference 2018, 24 May | London, UK
- Join us for the 2nd Big Data Economics Summer School, 5-8 August 2018 | Tehran, Iran
2018 - present Assistant Professor
Department of Economics &
Computational Modeling and Data Analytics
2012-2017 Graduate Teaching Assistant
Department of Statistics
London School of Economics