Backend Developer @ SkyIT · MS Computer Science @ NYU · Brooklyn, NY
I'm an NYU MS Computer Science student and backend developer building AI/ML systems, production APIs, and data infrastructure. My work spans computer vision research, edge-native AI wearables, RAG and multi-agent pipelines, and scalable backend services for real-world product teams.
Comfortable moving between theory, implementation, and deployment: from training segmentation models on A100 GPUs to building Django REST, Express, GraphQL, Docker, and database-backed systems across PostgreSQL, MySQL, MongoDB, and Azure workflows.
Edge-native AI wearable on NVIDIA DGX Spark GB10 running all inference locally, no cloud APIs. Won Most Secure Project at NVIDIA Spark Hackathon (Pensar & NVIDIA).
U-Net encoder-decoder with ResNet-34 backbone on CULane (95k+ images, A100 GPU). Achieved 97.1% mean IoU, 22-32% above SOTA, at 67 FPS, 2.2× faster than the 30 FPS real-time requirement.
AI assistant for Meta Ray-Ban Smart Glasses answering real-world queries in under 5 seconds using Google Gemini Live 3.1 Flash Lite. Multi-agent backend with RAG pipeline on GCP, native iOS & Android apps.
Benchmarked YOLOv8, Faster R-CNN, MobileNetV2, and EfficientDet D4 on real video streams. Published in IEEE. YOLOv8 outperformed all others across FPS, accuracy, precision, and confidence.
Unsupervised image restoration using Dark Channel Prior Loss (arXiv:1812.07051). No paired training data required. Extended to video frames for real-time dehazing in autonomous vision. Published in Springer.
Full-stack patient management platform with OTP-based passkey auth, Zod validation, admin dashboard with appointment status management, and real-time error monitoring via Sentry.
Full-stack Flask + PostgreSQL app with normalized relational schema (6 tables), real-time seat availability via complex SQL JOINs, idempotent seeding, and production-ready env-based config.
Food security platform aggregating 14,178+ real-time pantry resources with federal demographic data. ML scoring pipeline for optimal food bank placement. Voronoi coverage maps, heatmaps, AI assistant. Morgan Stanley Hackathon.
Multi-agent pipeline extracting YouTube transcripts, parsing recipe steps, normalizing ingredients, fetching nutritional data, and generating a grocery cart from a single URL. Redis-backed caching and state.
Interactive dashboard analyzing 2,000+ video game titles from 1985–2016. Cross-filtering across genre, platform, region, and publisher. Explores critic score vs. consumer sales divergence.
AI-driven content and visual creation project focused on faster design workflows and smarter creative asset generation.
Python generative AI code lab exploring model prompting, AI workflows, and practical GenAI experimentation.
Jupyter Notebook project analyzing historical traffic patterns to identify trends and recommend optimized smart-city traffic management and infrastructure planning.
Simple Python chatbot project practicing conversational flow, input handling, and lightweight command-line interaction.
Java text analysis tool calculating readability difficulty and estimated reader age using ARI, Flesch-Kincaid, SMOG, and Coleman-Liau metrics.
Pure Java recreation of the Battleship board game with grid state, turn logic, and object-oriented gameplay structure.
Benchmarked YOLOv8, Faster R-CNN, SSD MobileNetV2, and EfficientDet D4 on real video streams. YOLOv8 outperformed all others across FPS, accuracy, precision, and confidence. Dataset included security camera footage and self-recorded highway/campus videos.
Integrated pipeline combining Dark Channel Prior dehazing and MAP-Net video dehazing with YOLOv8 detection. Evaluated on REVIDE dataset using IoU, SSIM, and PSNR. Applications in autonomous driving, surveillance, and environmental monitoring.
Open to AI/ML Engineering, Backend Engineering, Data Science, and Research roles
Brooklyn, NY · Available for full-time & internship opportunities