ÖMER FARUK KOÇ

Legal AI Assistant: Processes thousands of legal documents with OCR + semantic chunking and integrates them into a RAG pipeline. Provides instant answers to user queries with relevant laws and case precedents; pipeline auto-updates when new documents are added.

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LLM-Powered Dashboard: This LLM-powered dashboard analyzes uploaded Excel/CSV datasets, generating meaningful summaries and visualizations. Users can interact with the data through natural language queries, receiving instant insights and actionable reports. Designed to make data-driven decision-making accessible to non-technical users.

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Brain MRI Tumor Detection with Transfer Learning: Built a high-accuracy brain tumor classification system using EfficientNetB3 with Mixup augmentation, label smoothing, and a 2-seed ensemble. Achieved ~99.5% test accuracy with explainable results.

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CLTV Modeling with BG-NBD & Gamma-Gamma: Developed a Customer Lifetime Value (CLTV) model using BG-NBD and Gamma-Gamma frameworks. Modeled purchase frequency and monetary value to generate actionable insights for marketing strategies.

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CIFAR-10 CNN with Mixup & Label Smoothing: Implemented a CNN-based image classifier on CIFAR-10 with Mixup augmentation and Label Smoothing to reduce overfitting. Achieved robust generalization across classes with improved accuracy.

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Amazon Reviews Sentiment-Based Ranking: Designed a sentiment-driven ranking system for Amazon reviews. Applied time-weighted average scoring and Wilson Lower Bound to surface the most reliable reviews, improving recommendation quality.

I work in artificial intelligence, machine learning, and deep learning, focusing on modeling, data preprocessing, and system development. My experience includes local model training, RetrievalAugmented Generation (RAG), GPU optimization, and MLOps, with an emphasis on building practical and scalable AI solutions.

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