Transforming data into intelligent solutions through AI, Machine Learning, and Applied Mathematics.
🤖 Machine Learning • 📊 Data Science • 📈 Data Analytics • 🧠 Generative AI • 🐍 Python • 🗄️ SQL • ☁️ AWS
I'm Dr. Pooja Shah, a Ph.D. in Applied Mathematics and an M.S. student in Data Analytics Engineering at George Mason University.
My academic journey began with a strong foundation in mathematics, which shaped my analytical thinking and problem-solving approach. Today, I apply that foundation to Machine Learning, Artificial Intelligence, Data Science, and Analytics to develop practical, data-driven solutions for real-world challenges.
My work spans predictive modeling, statistical analysis, optimization, healthcare analytics, and intelligent decision support systems. I enjoy transforming complex data into actionable insights through research, machine learning, and modern analytical techniques.
- 🤖 Machine Learning & Artificial Intelligence
- 📊 Data Science & Predictive Analytics
- 🧠 Generative AI & Large Language Models
- 📈 Business Intelligence & Data Visualization
- 📐 Applied Mathematics & Optimization
- 🏥 Healthcare Analytics
"Transforming mathematical thinking into intelligent AI solutions."
| Project | Description |
|---|---|
| 🤖 Machine-Generated Code Detection | NLP, TF-IDF, handcrafted features, and ensemble machine learning for detecting AI-generated code. |
| 💳 Credit Card Fraud Detection | End-to-end fraud detection using Random Forest, XGBoost, SMOTE, threshold optimization, and explainability. |
| 🌉 Bridge Infrastructure Analytics | Large-scale bridge infrastructure analysis using Apache Spark (PySpark), ETL, exploratory data analysis, XGBoost, predictive maintenance, and Power BI dashboard design. |
M.S. in Data Analytics Engineering (Expected: December 2026)
📍 Fairfax, Virginia, USA
⭐ GPA: 3.85/4.00
Ph.D. in Applied Mathematics (2023)
📍 Vadodara, Gujarat, India
M.Sc. in Applied Mathematics (2013)
📍 Vadodara, Gujarat, India
An end-to-end machine learning pipeline for detecting fraudulent credit card transactions using advanced feature engineering, cost-sensitive learning, and explainable AI.
🛠️ Tech Stack: Python • SQL • Scikit-Learn • XGBoost • Tableau • Pandas • NumPy
- 📊 Processed and analyzed 1.8M+ real-world financial transactions.
- 🤖 Developed and compared multiple machine learning models for fraud detection.
- ⚙️ Applied advanced feature engineering, class imbalance handling, and decision-threshold optimization.
- 🎯 Achieved 95.7% fraud recall with a 0.93 PR-AUC, minimizing false negatives while maintaining strong detection performance.
- 📈 Built an interactive Tableau dashboard to visualize fraud trends, model performance, and key risk metrics.
An NLP-based machine learning pipeline for distinguishing AI-generated and human-written source code using scalable text processing and classification techniques.
🛠️ Tech Stack: Python • NLP • TF-IDF • XGBoost • Scikit-Learn • Pandas • NumPy • Jupyter Notebook
- 💻 Developed an end-to-end NLP classification pipeline to distinguish AI-generated and human-written source code.
- 📂 Processed and analyzed 1M+ code samples using efficient text preprocessing and feature engineering techniques.
- 🔍 Applied TF-IDF vectorization with an XGBoost classifier to build a scalable code classification model.
- 🎯 Achieved 96.61% validation accuracy, demonstrating strong predictive performance.
- 📖 Built a fully reproducible research workflow with well-documented Jupyter notebooks and version-controlled source code.
🔗 Repository:
Repository: https://github.com/DrPoojaShah/machine_generated_code_detection
An end-to-end business intelligence solution that transforms raw infrastructure data into interactive dashboards and actionable insights through ETL, predictive analytics, and data visualization.
🛠️ Tech Stack: Power BI • SQL • Python • ETL • DAX • Power Query
- 🔄 Designed and implemented an end-to-end ETL pipeline for data extraction, transformation, and loading.
- 🧹 Performed data cleaning, preprocessing, and integration to ensure high-quality, reliable, and consistent analytics.
- 📊 Developed interactive Power BI dashboards featuring KPIs, trend analysis, and executive-level reports.
- 📈 Applied predictive analytics to identify patterns and support data-driven decision-making.
- ⚡ Automated reporting workflows, enabling stakeholders to monitor infrastructure performance through real-time dashboards.
🔗 Repository: Repository: https://github.com/DrPoojaShah/bridge_infrastructure_analytics
Advancing Artificial Intelligence through Applied Mathematics, Machine Learning, and Data-Driven Research.
My research focuses on Machine Learning, Artificial Intelligence, Applied Mathematics, Healthcare Analytics, Optimization, and Intelligent Decision Support Systems. I have authored 7 peer-reviewed journal publications that integrate mathematical modeling with modern AI techniques to solve real-world problems.
- 🤖 Machine Learning & Artificial Intelligence
- 📊 Data Science & Predictive Analytics
- 🧠 Healthcare Analytics
- 📐 Applied Mathematics & Optimization
- 🧬 Intelligent Medical Decision Support Systems
- 📈 Statistical Modeling & Computational Intelligence
📄 Radial Basis Function Network Equipped with an Ensemble-Based Lasso Ridge Model in Diagnosis of Breast Cancer
International Journal of Medical Engineering and Informatics (2025)
Results in Control and Optimization (2024)
International Journal of Applied Pattern Recognition (2022)
📄 Identification of Breast Tumor Using Hybrid Approach of Independent Component Analysis and Deep Neural Network
International Journal of Intelligent Systems and Applications in Engineering (2021)
Malaya Journal of Matematik (2025)
➡️ View my complete publication list on Google Scholar.
- 📚 7 Peer-Reviewed Journal Publications
- 🎓 Ph.D. in Applied Mathematics
- 🤖 Research in Artificial Intelligence & Machine Learning
- 🏥 Applications in Healthcare Analytics & Medical Decision Support
- 🌍 Published in International Peer-Reviewed Journals
2026 – Present
- 📊 Analyzed financial and operational data to support university business processes and reporting.
- 📈 Automated data validation and reporting workflows using Excel, SQL, and analytical tools.
- 📋 Generated reports and insights to support data-driven decision-making and process improvements.
- 🤝 Collaborated with cross-functional teams to improve operational efficiency and data accuracy.
- 🎓 Taught courses in Applied Mathematics, Machine Learning, Python Programming, Statistics, and Data Science.
- 👨🎓 Mentored undergraduate and postgraduate students in academic research and project development.
- 📚 Authored 7 peer-reviewed journal publications in Artificial Intelligence, Machine Learning, and Applied Mathematics.
- 🔬 Conducted research in optimization, healthcare analytics, and intelligent decision support systems.
- 🤖 Developed machine learning models for healthcare analytics and breast cancer diagnosis.
- 📐 Applied optimization techniques, statistical modeling, and computational intelligence to solve real-world problems.
- 🤝 Collaborated on interdisciplinary research integrating Artificial Intelligence with Applied Mathematics.
- 🎓 Deep Learning – NPTEL (IIT Madras)
- 🐍 Python for Everybody – Coursera
- 📊 Microsoft Power BI Data Analytics
- ☁️ AWS Cloud Foundations
- 🤖 Large Language Models (LLMs) & Prompt Engineering
- 🧠 Generative AI Applications
- ☁️ AWS for Machine Learning & Data Engineering
- 🚀 MLOps & Model Deployment
- 📊 Explainable AI (XAI) & Responsible AI
I'm always open to collaborating on projects in Machine Learning, Artificial Intelligence, Data Science, Data Analytics, and Applied Mathematics. Feel free to connect if you'd like to discuss research, collaborate on projects, or explore new opportunities.
📍 Fairfax, Virginia, USA
%20271--4029-25D366?style=for-the-badge&logo=phone&logoColor=white){kind=icon}
⭐ Thanks for visiting my profile! ⭐
Let's build intelligent solutions through AI, Machine Learning, Data Science, and Applied Mathematics.