Aspiring Data Scientist | Machine Learning Enthusiast | Big Data Explorer

I'm a Master's student in Applied Data Science at the University of Chicago, passionate about machine learning, big data, and AI-driven problem-solving. With experience in Python, SQL, PySpark, and data analytics, I thrive on transforming complex datasets into actionable insights. I have a background in big data, risk assessment, and cloud computing, with hands-on experience in consulting and research. Currently, I'm exploring deep learning and its applications in finance and trading. Always eager to learn and collaborate, I aim to bridge the gap between data science and real-world decision-making.

• Passionate about Data Science & AI
• Experienced in Python, SQL, PySpark, Machine Learning
• Currently learning Bayesian Modelling, GenAI, Machine Learning
• Interested in Finance, Trading, and Sports Analytics
• Reach me at: academics.zeel@gmail.com

• Wiki Data Viewer (May 2025): Developed a full-stack web app to replace a disorganized data archive, enabling non-technical researchers to intuitively access decades of historical research data.

• CheXRetriever: Text-to-Image Multimodal Retrieval on Medical X-rays (May 2025): Built a multimodal system that maps radiology text queries to chest X-rays using vision-language models for clinically relevant image retrieval.

• AlgoTradeX: Intelligent Automated Trading System (March 2025): Built a robust trading system using Python to analyze market data, execute automated trades, and manage portfolios efficiently.

• Crude Gasoline Dynamics: Cointegration Forecasting for Energy Arbitrage (March 2025): Analyzed cointegration between gasoline and crude oil prices using statistical and deep learning models, providing valuable insights for optimized trading strategies in energy markets.

• Web Application Vulnerability Analysis and Prediction (Dec 2024): Built predictive models using Poisson Regression and Negative Binomial Modeling to forecast vulnerabilities in web applications.

• Turing Bots vs Human Developers (Dec 2024): Investigated AI-generated versus human-written code using 1.36 TiB GitHub data on GCP, analyzing programming trends, repository growth, and commit patterns to evaluate AI coding capabilities.

• Corporate Data Analysis and Insights Repository (Nov 2024): Conducted comprehensive data analysis and generated actionable insights for three businesses—McDonald's, Planet Fitness, and Verizon—focusing on exploratory analysis, predictive modeling, and business recommendations.

• Business Case Studies with AI, Deep Learning, and NLP (Aug 2024): Conducted five in-depth business case studies (e.g., customer segmentation, sales forecasting, sentiment analysis, medical diagnostics, employee retention) leveraging AI, Deep Learning, and NLP techniques to solve complex business challenges and derive actionable insights.

• Check out my published research papers on Machine Learning, Data Analytics, and Data Processing on ResearchGate.