Teaching

Team-based capstone project — reinforcement learning for rocket guidance, satellite data applications, computer vision, and AI agent systems in aerospace domains.

Designing, building, and validating AI models as operational systems — AIOps, agent systems engineering, and deployment.

Knowledge representation and AI fusion — ontology-first thinking, decision science, semantic modeling, and generative AI critical analysis. AI Aerospace specialization track.

Latest NLP and LLMs — multimodal models, state space models, advanced RAG, alignment, and responsible AI.

Designing and managing ML systems in production — DevOps, MLOps, Docker, Kubernetes, CI/CD, and Weights & Biases.

Deep learning for NLP using NVIDIA NeMo and DGX H100 — training, optimization, and deployment of LLMs.

Fundamentals of NLP and language models — prompt engineering, text processing, and NLP application development.

Programming collaborative robots — system installation, operation, pick & place, and palletizing workflows.

ML systems in production — DevOps, MLOps, security practices with Docker, Kubernetes, and CI/CD.

OS design and management — process management, memory, file systems, and Linux through hands-on labs.

DevOps principles and tools — cloud computing, containerization, CI/CD, and agile methodologies.

Data science in economics and finance — ML, text mining, and network analysis for forecasting.

Architecture, application paradigms, and ethics of LLMs — hands-on training, paper analysis, and practical development.

Transformers, attention mechanisms, tokenization, and LLM fine-tuning with hands-on exercises.

Software development lifecycle with Python — coding, testing, deployment, and maintenance.

AI and ML applications in banking, insurance, and investment — case studies and industry lectures.

Designing and implementing ML systems — DevOps, MLOps, CI/CD, model deployment and monitoring.

Recent advances in AI and NLP through influential research papers — neural networks, sequence modeling, generative models, and transformers.

Deep learning techniques for NLP — pre-trained language models and fine-tuning with PyTorch and Hugging Face.

Fundamentals of NLP — text pre-processing, syntax, semantics, classification, topic modeling, and embeddings.

High school AI course at Daejeong Girls' HS — AI concepts, ChatGPT/Claude, Python, and inquiry-based problem solving.

NLP techniques for social science researchers — leveraging LLMs to analyze textual data and address research questions.