Recent work first: enterprise automation, 3D AI, semantic search and applied ML.

I automated enterprise validation flows across refresh, masking, and post-refresh operations. I validated data movement, exception patterns, and compliance signals across Salesforce and GCP-backed workflows. I built Python-driven reporting around Cloud Spanner, SonarQube, Harness, CI/CD gates, and operational evidence. I converted manual checks into repeatable runbooks that teams could audit and rerun. The result was clearer platform governance, faster refresh execution, and stronger release confidence.

I engineered preprocessing steps for Gaussian Splatting-based 3D reconstruction. I prepared multi-view video frames for scene reconstruction pipelines. I applied masking to suppress background noise and visual artifacts. I tuned the input pipeline so reconstructed scenes retained cleaner geometry and texture cues. This role connected computer vision, graphics data, and neural rendering into one production-oriented workflow.

I integrated LLM capability into a production search workflow. I improved semantic query interpretation so retrieval could move beyond exact keyword matching. I modeled user intent to help the pipeline understand what a query was trying to accomplish. I contributed personalization logic for more relevant search behavior. The engagement translated NLP research patterns into practical product-search improvements.

I analyzed healthcare text from Electronic Health Record workflows. I mapped clinical language patterns into NLP tasks where context and structure mattered. I handled medical text signals that required domain-aware interpretation. I explored extraction strategies for turning unstructured records into usable information. The internship strengthened my applied NLP judgment in a healthcare setting.

I developed machine-learning experiments for early breast cancer detection. I evaluated deep learning behavior for healthcare image-analysis use cases. I used IBM’s ML ecosystem to improve model performance and experimentation speed. I connected diagnostic-support goals with practical model validation. The project deepened my experience in healthcare AI, model assessment, and applied experimentation.

I investigated Generative Adversarial Networks in a research-exposure setting. I studied how imbalanced training data distorts model behavior. I tested training scenarios where data distribution created failure modes. I sharpened experimental discipline through model comparison and result interpretation. The program strengthened my base in deep learning, generative modeling, and evaluation design.

I explored applied computing problems through a research-oriented lens. I examined ML-adjacent workflows that required both implementation and analysis. I practiced breaking broad technical problems into testable components. I strengthened my ability to connect academic ideas with buildable systems. The exposure helped bridge classroom learning, research thinking, and engineering execution.