💻 Full-Stack Developer | 🤖 AI/ML Engineer | 👁️ Computer Vision

I'm a final-year CS student building AI-powered applications and full-stack systems, with a growing focus on computer vision, retrieval-augmented systems, and multimodal learning. I like taking ML concepts from theory to working, deployable projects that solve real problems.

• 🔭 Recently completed: MedInsight, a retrieval-augmented vision-language pipeline for medical image understanding
• 📄 Independent research: two DOI-archived research preprints on Zenodo (links below)
• 💬 Ask me about: computer vision, full-stack architecture, or retrieval-augmented systems

Languages

AI / Machine Learning

Full-Stack Development

Cloud & Services

Tools

Exploratory project applying retrieval augmentation (CLIP embeddings + FAISS) to medical VQA, investigating whether grounding a vision-language model in retrieved evidence improves answer reliability. Findings published as a preprint (linked in Research below). PyTorchCLIPFAISSBLIP-2Medical VQA

Real-time detection, tracking, and counting pipeline built on YOLOv8 and OpenCV, with configurable zones and counting logic. PythonYOLOv8OpenCV

Productivity monitoring platform with activity tracking, OCR-based screenshot analysis, automated reporting via n8n, and a Supabase-backed analytics dashboard. PythonFlaskSupabaseEasyOCRn8n

Graph database explorer that lets users query and visualize Neo4j graphs using natural language input, translated into structured graph queries. Neo4jGraph DatabasesNLP

MERN-stack project management tool with real-time updates (Socket.io), API documentation (Swagger), automated testing (Jest), and Docker-based deployment. MongoDBExpressReactNode.jsDockerSocket.io

Android app hosting 7 interactive mini-games, built with an MVVM architecture and Firebase backend. KotlinFirebaseMVVM

• MedInsight — Evaluates whether retrieval augmentation (CLIP + FAISS) improves a BLIP-2 vision-language model on medical VQA, benchmarked on VQA-RAD and ROCOv2. DOI-archived on Zenodo: zenodo.org/records/21670123
• AI-Based Depression Detection — Detects depressive symptoms in social media text using a fine-tuned DistilBERT + BiLSTM model with attention, reaching 91.09% accuracy with interpretable predictions. DOI-archived on Zenodo: zenodo.org/records/21265208

_{Intro to AI Concepts · Intro to Generative AI & Agents · Plan & Prepare AI Solutions on Azure · Intro to AI Speech Concepts}

🌐 Portfolio | 💼 LinkedIn | 📧 maryamzaheer2006@gmail.com

Building practical software systems with AI, one project at a time.