Yudan Kang graduated from UC Berkeley with a Bachelor's degree in Statistics and Computer Science and is now pursuing the MSCF degree at Carnegie Mellon University. Her academic training in machine learning, stochastic processes, optimization, and data science is complemented by strong programming skills in Python, SQL, R, and MATLAB. Yudan has conducted quantitative research across multiple settings. As a research assistant at UNC, she led data collection and modeling for U.S. housing markets, building hedonic models with fixed effects to estimate fair market value. In industry, Yudan interned at CICC's quantitative department and JoinQuant, where she worked with factor models, conducted portfolio strategy analysis, and assisted the team in developing high-yield investment approaches.
Outside of academics, Yudan began fencing in middle school and continues to enjoy sports, including badminton, running, and activities that foster her discipline and collaborative mindset. She joined the MSCF program to deepen her expertise in quantitative modeling and data-driven decision making, and is excited to pursue a career in quantitative research, trading, or risk management.
