Data Science Intern

Designed an AI-enabled product selector that works across multiple data modalities. I carried the project end to end, from reading source data out of SharePoint to deploying the application for internal use. I also built an automated data ingestion pipeline so the underlying data refreshes on its own and the selector stays current without manual updates.

Data Science Intern

Graph Retrieval-Augmented Generation (Graph-RAG) pipeline using Open Source and Azure Models: I built a Q/A system to process and query 20 years of software manuals. Using LangChain framework, I converted document knowledge into a neo4j-based graph database to retrieve information that is contextually rich and useful enough to assist Applications Engineers. Delivered scalable knowledge access – Enabled engineers to efficiently search and extract insights of multi-fold difficulties (temporal, vague, reasoning) from extensive technical documentation.

Data Science Intern

Developed a Graph Neural Network (GNN) framework – Built a heterogeneous network graph for product recommendation using node classification. I ensured effective information flow in non-homogeneous graphs to improve recommendation accuracy. Researched Variational Autoencoder (VAE) applications – I explored generative modeling for novel product formulation based on graph structures. I also identified how large, dense graphs bias the latent space and hinder stable VAE training.