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Welcome
I’m Steven VanOmmeren, an applied economist and data scientist with expertise in machine learning predictions, microeconometric methods, and comprehensive data science applications.
About Me
As a researcher and practitioner, I focus on applying advanced quantitative methods to real-world economic and business problems. My work spans predictive analytics, natural language processing, and econometric analysis, with particular expertise in time series forecasting and sentiment analysis.
Current Interests
- Machine Learning Predictions: Developing models for financial markets and economic forecasting
- Microeconometric Methods: Applied econometric analysis in labor, development, and health economics
- Data Science Applications: Real-time data processing, sentiment analysis, and predictive modeling
Featured Work
Recent Projects
Sentiment Volume Forecasting
Research project investigating news sentiment for predicting intraday airline stock trading volume using machine learning and real-time GDELT data. Achieved results comparable with state-of-the-art models with minimal improvement from sentiment features.
Super Search
Semantic indexing tool for PDF repositories using advanced natural language processing techniques to enable intelligent document search and retrieval.
Labor Economics Research
Applied econometric analysis examining labor market dynamics and policy impacts using modern microeconometric methods.
Technical Skills
- Languages: Python, R, Stata, LaTeX, SQL
- Machine Learning: TensorFlow, PyTorch, scikit-learn, LightGBM
- Data Analysis: pandas, NumPy, polars, matplotlib, seaborn
- Research: Academic publication, replication studies, statistical modeling