RAG & Agentic RAG
Expert
Hello, I'm
AI Architect & Machine Learning Engineer
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4+ years
AI & Software Development
B.Sc. Biomedical Engineering
M.Sc. Artificial Intelligence
Iām an AI engineer passionate about building intelligent systems that move ideas from research to real-world impact. As a recent graduate from the prestigious MSAI program atNorthwestern University, I design cloud-native, agentic AI solutions spanning RAG architectures, multi-agent systems, and ML Ops pipelines. My work integrates large language models with scalable infrastructure to power human-centered innovation across education, healthcare, and global development. Iām driven by one goal ā to transform how the world learns, builds, and collaborates with AI.
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Browse My Recent
Deep learning project leveraging Variational Autoencoders to analyze facial symmetry and detect stroke symptoms through facial asymmetry reconstruction.
Advanced language generation model fine-tuned on 990K+ products to generate natural-language food descriptions with nutritional content and global availability data.
Full-stack maintenance management platform for healthcare institutions featuring predictive maintenance, real-time analytics, and inventory management.
Cloud-based analytics system for the Global Poverty Research Lab at Kellogg, processing socioeconomic data for 2,000+ participants with real-time insights.
Deep learning system using U-Net architecture to reconstruct high-quality CT images from low-dose scans, reducing radiation exposure while maintaining diagnostic clarity.
Investigating persuasive impact of Large Language Models across diverse demographic contexts at Northwestern University's C3 Lab.
Led development of generative AI curriculum and workshops for 100+ educators, achieving 75% improvement in faculty AI proficiency at Northwestern University.
Full-stack application with Neo4j graph database, automated CI/CD pipeline, and member attendance tracking improving pastoral care efficiency by 40%.
Ensemble machine learning models (Random Forest & XGBoost) for drug-target interaction prediction using molecular fingerprints from SMILES data.
Reinforcement learning agent using Deep Q-Networks to master the traditional African board game Oware through self-play.
Advanced document processing pipeline with hybrid RAG system achieving 93% information retrieval accuracy and 67% improved answer relevance.
Computer vision model for automated eye disease classification using deep learning techniques for medical diagnosis support.
AI-powered tool for research paper analysis, summarization, and knowledge extraction to accelerate academic research workflows.
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š Chicago, IL | (773) 200-7411