I'm an AI Research Engineer at LG CNS and currently pursuing my Master's in AI at University of Pennsylvania (Fall 2025 ~ ). With experience spanning from GPU architecture optimization at Intel Corporation to deploying enterprise-scale generative AI solutions, I'm passionate about bridging the gap between cutting-edge research and real-world applications.

• 🔬 Current Focus: Generative AI, RAG Systems, GPU Computing, Distributed ML
• 🎓 Education: MSE AI @ UPenn (2025-2027), MS CS @ UC Davis (2022), BS CS @ UC Santa Cruz (2017)
• 🌍 Languages: Korean (Native), English (Fluent), Mandarin (Advanced), Spanish (Elementary)
• 🎯 Interests: Large Language Models, Computer Vision, GPU Architecture, MLOps

• 🤖 Deploying in-house generative AI solutions on GitLab at LG CNS
• 📚 Building RAG applications with LangChain, Azure OpenAI, and vector databases
• 🎓 Mastering advanced AI coursework at UPenn (NLP, Deep Learning, Computer Vision)
• ⚡ Optimizing GPU workloads for ML training and inference
• 🔬 Contributing to Apache ResilientDB blockchain research

🏢 LG CNS - AI Research Engineer (2023 - Present)

• Engineering RAG applications with Python, LangChain, and Azure OpenAI
• Developing Custom Copilot Solutions using Microsoft Copilot Studio
• Designing scalable training environments for large models (Phind CodeLlaMA 34B-v2)
• Leading Azure OpenAI architecture demonstrations to executives

🔧 Intel Corporation - GPU Solutions Architect / AI Research Engineer (2022)

• Optimized cloud gaming workloads and GPU performance analysis
• Developed automation tools for performance benchmarking using PowerShell
• Researched Graph Neural Networks with PyTorch Geometric
• Enhanced deep learning model performance achieving 5x training speedup

📄 Recent Publications:

• "Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet" (ArXiv, 2022)
• "A Deep Learning Technique using Follow Up X-Rays for Disease Classification" (ArXiv, 2022)
• "Influence of Communication Among Shared Developers on OSS Productivity" (ArXiv, 2022)

🏆 Notable Achievements:

• Apache ResilientDB Initial Contributor - Listed on Apache Incubator Project (2023)
• Best Contributor - UC Davis CS Graduate Entrepreneurship Club (2022)
• Google TensorFlow Developer Certified

Python, LangChain, Azure OpenAI, Vector Databases

• Built production-ready RAG application for document extraction
• Optimized PDF parsing performance comparing PyPDFLoader vs UnstructuredLoader
• Integrated Chroma and FAISS vector databases for efficient retrieval

React, TypeScript, Cardano APIs, Firebase

• Developed decentralized financial transaction web application
• Integrated Cardano blockchain with BlockFrost.io APIs for security
• Implemented user authentication and real-time transaction processing

Intel oneAPI, DPC++, Performance Optimization

• Migrated NVIDIA CUDA applications to Intel oneAPI DPC++
• Achieved 2.5x runtime performance improvement
• Optimized for Intel CPU architecture using Intel libraries

🔮 Currently Learning:

• Advanced GPU Computing for ML Systems
• Distributed Systems Architecture
• LLM Fine-tuning and RLHF
• Kubernetes & Container Orchestration

📜 Certifications:

• Google TensorFlow Developer Certificate
• Intel OpenCL & OpenVINO Specializations
• Deep Learning Specialization (Deeplearning.ai)
• Google Data Analytics Professional Certificate

I'm always excited to collaborate on AI research, discuss the latest in machine learning, or explore opportunities in GPU computing and distributed systems. Feel free to reach out!

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