Statistical Inference
Applying probability, hypothesis testing, and statistical models to draw insights from real-world datasets.
Machine Learning
Designing supervised and unsupervised ML models with Python, scikit-learn, and TensorFlow for intelligent predictions.
Cloud Platforms
Using Google Cloud and Azure to build scalable data pipelines, BigQuery analytics, and MLOps deployment workflows.
Data Engineering
Building ETL pipelines, data wrangling with Pandas & SQL, and automating processes for large-scale ingestion.
Neo4j & Graph Databases
Leveraging graph theory with Neo4j to model and query relationships in recommendation engines and RAG systems.
MLOps & CI/CD
Integrating ML models with CI/CD pipelines using GitHub Actions, Cloud Functions, and containerized deployments.
Backend With Flask
I build end-to-end web applications using Flask for backend logic and JavaScript for interactive frontends. This allows me to rapidly prototype and deploy data-driven tools, dashboards, and AI-powered features that are scalable and user-friendly.
Retrieval-Augmented Generation (RAG)
Combining LLMs with vector search and document retrieval for intelligent question answering and summarization.
Physics Simulations (C++)
Running Geant4-based Monte Carlo simulations for neutron capture and particle physics modeling.
Data Visualization
Communicating insights through dashboards using Power BI, Tableau, and Python libraries like Matplotlib.