People / Students / Class of 2027
Yujing Wang earned his bachelor’s degree in Mathematics and Applied Mathematics from Wenzhou-Kean University, with a minor in Computer Science. During his undergraduate studies, he built a strong foundation in mathematics, statistics, and computing through coursework in matrix and linear algebra, differential equations, numerical analysis, probability and statistics, data structures, discrete mathematics, programming, big data computing, and statistical data mining. Through this academic training, he developed proficiency in Python, Java, SQL, MATLAB, SPSS, and R, with interests in data analysis, machine learning, and applied mathematical modeling.
Prior to joining the MLDS program, Yujing gained experience through both industry internships and research projects related to data analysis and machine learning. As a data analysis intern in the risk control department at Beijing Daokou Jinke Technology Co., Ltd., he wrote and executed SQL queries to extract and analyze enterprise data, collected and organized data from more than 300 companies, and used large language models to identify historical and current company names, improving data processing efficiency and shortening the product delivery cycle. In his part-time role in data analysis at Google, he used Python to clean and process more than 4,000 historical loan records, completed numerical and categorical feature engineering, and applied machine learning models such as logistic regression, decision trees, random forests, support vector machines, Bayesian decision trees, and gradient boosting to support credit risk assessment. He also evaluated model performance using accuracy, precision, recall, and F1-score, and presented his findings through visualizations and an English project report.
Through the MLDS program, Yujing Wang aims to deepen his technical expertise in machine learning, data science, and artificial intelligence while continuing to connect mathematical theory with real-world applications. He has conducted research on the application of physics-informed neural networks, or PINNs, to fluid physics and differential equations, where he explored how machine learning methods can be used to solve complex scientific problems. He looks forward to contributing to a collaborative and interdisciplinary learning environment, where he can further develop as a researcher and data-driven problem solver. Beyond his academic interests, Yujing enjoys playing and watching basketball.
