About This Website

Hello!

I’m a M.S. Student in the Graduate School of Data Science at KAIST, advised by Prof. Joyce Jiyoung Whang at the Big Data Intelligence Lab (BDI Lab). I have attached my CV in the CV section and organized the projects I have undertaken since my undergraduate studies in the Portfolio section.
Clicking on each card will provide detailed descriptions, along with the corresponding PDF reports and presentation slides.

Recently, I have developed a strong interest in Multimodal Machine Learning, Time-Series Data Analysis, and Explainable AI (XAI).
My focus lies in extracting meaningful insights from the diverse formats of data that are inevitably accumulated across the increasingly digitalized domains of business.
I am particularly motivated by the potential of these approaches to enhance AI models’ ability to understand and learn from complex sociocultural phenomena.

Thank you for visiting.

📘 Summary of Coursework (Undergraduate)

🖥️ Computer Programming and Systems

• Data Structures — Fundamentals of Data Structures in C (Horowitz)
• Discrete Structures — Discrete Mathematics and its Applications (Rosen)
• Design and Analysis of Algorithms — Introduction to Algorithms (CLRS)
• Database System — Database System Concepts (Silberschatz)
• Computer Networks — Computer Networking: A Top-Down Approach (Kurose)
• Introduction to Digital Circuits — Introduction to Logic and Computer Design (Marcovitz)
• Operating Systems — Operating Systems: Three Easy Pieces (Arpaci-Dusseau)
• Computer Science and Engineering Laboratory I — Course materials and Projects(CPP)
• Computer Science and Engineering Laboratory II — Course materials and Projects(Verilog)
• Advanced Applied C Programming — 전공자를 위한 C언어 프로그래밍 (주우석)
• JAVA Language Programming — Java의 정석 (남궁성)
• Introduction to Computer System — Computer Systems: APP

🤖 Principles and Applications of Artificial Intelligence / Machine Learning

• Introduction to Artificial Intelligence — Artificial Intelligence: A Modern Approach (Russell & Norvig)
• Introduction to Machine Learning — Mathematics for Machine Learning (Marc Peter Deisenroth)
• Artificial Intelligence for Business Analytics — Course materials
• Business Data Science — Course materials
• AI and Marketing — Course materials

📊 Mathematics and Statistics

• Business Statistics — Business Statistics: A Decision Making Approach (Groebner et al.)
• Statistical Data Analysis for Business — 데이터사이언스 통계학 (이군희)
• Time Series Data Analysis and Forecasting — Course materials
• Introduction to Linear Algebra — Introduction to Linear Algebra (Gilbert Strang)
• College Mathematics — College Mathematics (Kim et al.)
• Analytic Geometry and Calculus I — Calculus 9e (Stewart)

💼 Business Administration (Marketing, Accounting, Strategy, etc.)

• Principles of Accounting — Financial Accounting (IFRS) (Kimmel)
• Managerial Accounting — Managerial Accounting (Brewer et al.)
• Financial Management — Corporate Finance (Ross et al.)
• Strategic Management — Strategic Management (Jay Barney)
• Principles of Marketing — Principles of Marketing (Kotler)
• International Business — International Business (Hill)
• Production and Operations Management — Operations Management (Stevenson)
• Management Science — Introduction to Management Science (Taylor)
• Theories of Organizational Behavior — Understanding and Managing Organizational Behavior (George)
• Digital Transformation and Business Model Innovations — Course materials
• Metaverse Virtual Markets and Marketing — Course materials
• Current Issues and Cases in Marketing — Papers
• Principle of Economics I/II — Principles of Economics (Mankiw)