Objective: Investigated the relationship between ML model complexity and forecasting accuracy for stock pair spread movements, with a focus on optimizing trading performance.

• Led research investigating the relationship between ML model complexity and forecasting accuracy for stock pair spread movements, with a focus on optimizing trading performance.
• Engineered a rich 280-feature set, including Refinitiv API-derived technical indicators, custom-scraped news headlines with BERT-based sentiment analysis, and Bloomberg Twitter sentiment data.
• Evaluated model performance across the complexity spectrum: from simple Generalized Linear Models (GLMs) to advanced neural networks, including Gradient Boosting Machines (GBMs), Random Forests (RF), Vanilla LSTM (~50k parameters), Hyperparameter-tuned LSTM (2M+ parameters), and Bidirectional LSTM with dropout.
• Developed a backtesting framework incorporating Bayesian-adjusted mean reversion strategies, achieving 80.75% annualized returns with tuned LSTM model—an average 105% improvement over non-deep learning models.
• GitHub Repository

Objective: Explored the predictive power of non-traditional urban data sources in forecasting real estate trends and Real Estate Investment Trust (REIT) performance.

• Led research on the efficacy of non-traditional urban data in predicting real estate trends and REIT performance, analyzing datasets such as Citibike usage, building complaints, business operations, health inspections, evictions, etc.
• Architected a scalable, cloud-based ecosystem to process and analyze terabytes of diverse datasets, leveraging technologies like MongoDB, AWS services, and distributed computing frameworks such as Dask and PyArrow for efficient data handling.
• Engineered high-performance data acquisition systems, including a web scraper for MLS data processing over 1 million listings daily, and a parallel ingestion framework for more than 1 billion historical Citibike rides.
• Integrated Granger-causality optimized alternative data sources, improving market index forecasts by 25% and REIT predictions by 34.2%.
• Designed an interactive Streamlit application for non-technical stakeholders, allowing intuitive exploration of correlations between alternative data and market trends. (Feature Explorer Demo Render)
• GitHub Repository | IEEE Report (Pending Submission)

Objective: Developed a multi-model machine learning framework to valuate residential properties and forecast sale probabilities, integrating data from diverse sources.

• Engineered a multi-model ML framework utilizing ensemble and gradient boosting methods for residential property valuation and sale probability forecasting, integrating data from Zillow, Yelp, and other relevant sources.
• Developed an interactive Streamlit application that allows users to access real-time property valuations, explore detailed market data, and visualize macroeconomic trends in the real estate sector.
• Valuation App Demo | Market Analysis App Demo | Analysis GitHub Repository

Objective: Automated the deployment of Python-based web applications on AWS EC2 instances, streamlining continuous integration and delivery processes.

• Developed a scalable and automated deployment system for Python-based web applications (Dash, Streamlit) on AWS EC2 instances, enhancing the efficiency of continuous integration and delivery (CI/CD).
• Integrated a GitHub webhook-based deployment system that automatically triggers deployments upon code pushes, ensuring seamless and streamlined deployment of MVPs and new features.
• Leveraged PM2 for continuous process management and Nginx as a reverse proxy, providing high availability and load balancing for deployed applications.
• Incorporated dynamic Nginx configuration generation based on deployed applications, enabling the dynamic management of custom domains and routing rules.
• GitHub Repository

Objective: Applied NLP techniques to classify the political orientation of news headlines using advanced embedding and clustering methods.

• Implemented text preprocessing, feature extraction, and clustering algorithms, including Spectral Clustering and K-Means, to classify the political orientation of news headlines.
• Utilized BERT models for text embedding, providing a rich representation of the textual data for clustering purposes.
• Explored dimensionality reduction techniques like PCA and t-SNE for effective visualization of high-dimensional data, enhancing the interpretability of clustering results.
• GitHub Repository

Objective: Developed high-performance web scrapers for automating data extraction from various online platforms, supporting various projects and research initiatives.

• Zillow Scraper: Built a high-performance web scraper using Azure, capable of extracting over 2 million property listings per scrape on a weekly automated schedule. GitHub Repository
• LinkedIn Scraper: Created a scraper for LinkedIn to extract data from both company and user profiles, gathering information such as company posts, user experiences, and education. GitHub Repository
• Yelp Scraper: Developed a Selenium-based web scraper to extract restaurant data, including information on health ratings, review counts, and business details. GitHub Repository