Machine Learning | Software Engineering | ML Research

📍 Tempe, AZ
📧 [email protected]
🔗 LinkedIn • 💻 GitHub

I’m a Machine Learning–focused Software Engineering graduate student at Arizona State University, passionate about building data-driven systems and understanding how neural networks learn and generalize.

My work spans:

• Applied ML (healthcare, environmental analytics)
• ML research (late generalization / grokking in neural networks)
• Data visualization & analytics (interactive dashboards, global datasets)

I enjoy turning complex models into reproducible, interpretable, and deployable systems.

🚀 Pinned repositories below

Empirical study of grokking and delayed generalization in deep neural networks under extended training.
Tech: Python, PyTorch / TensorFlow, NumPy
📄 Research-style experiments • 📊 Optimization dynamics

Built predictive models on 50k+ health and environmental records, achieving 95% accuracy and identifying key cardiovascular risk factors.
Tech: Python, SQL, Scikit-learn, PowerBI

Developed an interactive D3.js world dashboard visualizing AI-driven job automation risk across countries and occupations.
Tech: JavaScript, D3.js, GeoJSON, HTML/CSS

Built ML models achieving 92% accuracy on patient data and optimized CI/CD pipelines, reducing deployment time by 25%.
Tech: Python, XGBoost, AWS, Jenkins, Docker

Languages: Python, C++, SQL, JavaScript
Machine Learning: XGBoost, Decision Trees, KNN, SHAP, NLP, Neural Networks
Deep Learning: PyTorch, TensorFlow
Data & Visualization: Pandas, NumPy, PowerBI, Matplotlib, D3.js
Cloud & MLOps: AWS (S3, EC2, SageMaker), Docker, Jenkins, GitHub Actions

Arizona State University — MS in Software Engineering (Expected Aug 2026)
University of Mumbai — BE in Information Technology (2023)

I’m actively looking for:

• Machine Learning Internships
• ML Engineer / Data Scientist roles
• Research Intern opportunities

📫 Reach me at [email protected] or connect on LinkedIn

⭐ If you find my work interesting, feel free to star or fork a repository!