95% retrieval accuracy
AskNEU — RAG Assistant
A question-answering assistant built over 500+ university web pages, so students can ask in plain language and trace an answer back to its source.

I build machine learning models and the data pipelines that power them; focusing on scalable cloud architecture, and how systems actually hold up under real-world usage.
With a foundation in computer science and data engineering, my early work focused on building resilient backend architectures, scalable data pipelines, and high-reliability systems. At Mercedes-Benz R&D, I applied these core principles to engineer production data pipelines for high-volume vehicle data.
I expanded into data science during my Master’s at Northeastern, bridging software engineering with modern AI to build intelligent, end-to-end ML systems. During my time there, I worked as a research assistant training vision models for Skin Cancer detection alongside developing other ML systems.
95% retrieval accuracy
A question-answering assistant built over 500+ university web pages, so students can ask in plain language and trace an answer back to its source.
0.94 F1-score
A Streamlit app that matches user profiles against 26K+ financial products and generates personalized explanations through a RAG pipeline.
54% → 94% JSON pass rate
Llama-3.2-3B fine-tuned with LoRA on 300 domain-specific audit records to extract structured JSON metadata from unstructured banking logs.
95% sensitivity · 0.96 AUC-ROC
Four hybrid CNN–Vision Transformer architectures benchmarked for melanoma detection on a severely imbalanced medical image dataset.
Northeastern University
2024–2026 · GPA 3.6/4.0
Visvesvaraya Technological University
2017–2021 · CGPA 8.5/10