Hi there! 👋 I’m Naveena, a backend developer and AI enthusiast currently pursuing my Master’s in Software Engineering at the University of Alberta. I’m working on deep learning models for medical image segmentation as part of my graduate research, and I love building secure, scalable backend systems using Python, APIs, and AWS. I’m currently deepening my knowledge of AWS cloud services and actively looking for full-time opportunities where I can contribute to meaningful, impactful tech projects. I’m especially interested in roles involving backend architecture, machine learning, or event-driven systems. Outside of tech, I enjoy painting and playing badminton—they keep me balanced and inspired. Let’s connect, build, and grow together!

Developed and optimized deep learning model EINS-Net for segmenting nuclei in histopathological images. Focused on preprocessing, model tuning, and evaluation using Dice and Jaccard scores.

Enhanced peer-to-peer profile matching algorithms using LLMs for better resume-job alignment. Contributed to the backend infrastructure and scalable API development.

Built secure, serverless backend using AWS Lambda and ReactNative. Integrated AWS Cognito for authentication and improved response time by 20% for a HIPAA-compliant mobile health app.

Automated document processing using AWS Textract and Python. Reduced revenue cycle time by 90% and optimized data ingestion pipelines for large-scale OCR workflows.

Designed a wearable wristband for Alzheimer’s and autism patients that detects wandering behavior and alerts caregivers in real time using sensor integration.

Built an ML classification model to detect COVID-19 from chest X-ray scans. Used data preprocessing, feature extraction, and model tuning techniques for better accuracy.

Explored vision-language transformer models to perform compositional analysis of multi-object scenes in complex visual datasets.