🎓 MSCS @ Northeastern University (Boston, MA)
💻 Software Engineer | AI & Full Stack Enthusiast
I am a passionate engineer who loves building intelligent, scalable, and user-centric systems, whether that’s crafting a full stack app, optimizing database design, or deploying machine learning models in production.
Currently pursuing my Master’s in Computer Science at Northeastern University, my work spans software engineering, AI.
I have previously contributed to research in medical AI and mental health analytics, published in IEEE and Springer, and worked on projects that bridge data-driven intelligence with robust backend engineering.
⚡ Fun fact: I love turning research ideas into real-world systems, from chatbots powered by GPT models to multi-calendar scheduling tools built from scratch.
Languages
Python | Java | C++ | JavaScript | TypeScript | SQL
Web Technologies
React.js | Express.js | Node.js | Next.js | HTML | CSS | Bootstrap
Databases
MySQL | PostgreSQL | MongoDB | SQL
Cloud & DevOps
AWS (EC2, Lambda, Bedrock) | Docker | Kubernetes | Jenkins | Terraform | CI/CD
AI & ML
TensorFlow | PyTorch | scikit-learn | Hugging Face | LangChain | RAG | OpenCV | NLP | LLMs
🗓️ Calendar Application — Java, Swing, MVC
Built a modular multi-calendar system with timezone-aware scheduling, recurring events, and Google Calendar-compatible CSV import/export. Designed an interactive Swing GUI with event dialogs and achieved 90% mutation test coverage.
🌾 Crop & Fertilizer Recommendation System — Python, Flask, MySQL
Developed a normalized MySQL schema with 30+ stored procedures and triggers for automated crop/fertilizer recommendations. Designed a secure Flask web interface and reduced manual classification by 40%.
👣 MLOps Pipeline for Pedestrian Detection — Python, YOLOv5, DVC, Streamlit
Built an automated MLOps pipeline handling data versioning, model registry, and inference. Deployed a real-time detection app cutting manual testing by 80%.
- BGKnow–Medical Chatbot: A Hybrid Approach using GPT-2 and Knowledge Graphs — Springer ADCIS 2023
- Performance Comparison of ML Techniques for Early Detection of Postpartum Depression — IEEE CICN 2023
MLOps, backend design, and AI system integration
⭐ "Bridging intelligent systems with reliable software — one project at a time."