Kyuseok Nam

Resourceful Graduate Research Assistant passionate about advancing knowledge in Generative AI models through high-quality research. Self-motivated professional with excellent communication, planning and problem solving abilities.

• Computer Science 1: Introduction to basic programming using python.
• Data Structure: Programming concepts: functions, parameter passing, pointers, arrays, strings, structs, classes, templates. Mathematical tools: sets, functions, and relations, order notation, complexity of algorithms, proof by induction. Data structures and their representations. Algorithms: searching and sorting, generic algorithms, iterative and recursive algorithms.
• Database Systems: Apply principles of normalization to design a data model that leads to the development of high-performance data intensive applications.
• Mathematical Statistics: Learned the fundamental concepts in mathematical statistics.
• Linear Algebra: Learned the fundamental concepts of Linear Algebra.
• Introduction to Generative AI models: Studied Deep Learning models used for generative tasks. LLM, Diffusion models, GANs, and flow based models.
• Embedded Control: Engineering laboratory introduction to the microprocessor as an embedded element of engineering systems. Develop hardware and software of one or more target systems.
• Computer Architecture, Network, and Operating system: Quantitative basis of modern computer architecture, processor design, memory hierarchy, and input/output methods. Layered operating system structures, process and storage management. Layered network organization, network protocols, switching, local and wide area networks.