People / Students / Class of 2027
Lufan (Rebecca) Wang graduated from the University of Washington with a B.S. in Applied Mathematics (Data Science) and minors in Data Science and Informatics. She graduated Magna Cum Laude in the top 3% of her class and was named to the Dean's List every quarter. Through her academic training, she developed a strong foundation in machine learning, statistics, optimization, and large-scale data analysis. She is proficient in Python, SQL, R, and C++ and is passionate about leveraging AI and data science to solve complex real-world problems and develop data-driven products.
Rebecca has gained diverse data science internship experience across insurance, technology, consulting, and AI at GEICO, ByteDance, Accenture, PwC, and two AI startups. She has developed machine learning models, built data pipelines, designed LLM-based applications, and conducted large-scale experimentation to improve model performance and support data-driven decision-making. Her work spans predictive modeling, generative AI, recommendation systems, customer experience, model evaluation, and large-scale analytics, with experience translating data and machine learning research into production-ready solutions and real-world applications.
Beyond industry, Rebecca has been involved in research at the University of Washington and the University of Michigan for over two years. Her work has focused on developing AI and machine learning methods, designing computational algorithms, and analyzing large-scale datasets to solve challenging problems across diverse domains while improving model performance, robustness, and interpretability. Through Northwestern University's MLDS program, she hopes to deepen her expertise in AI and machine learning and pursue a full-time career as a Data Scientist or Machine Learning Engineer, building intelligent systems that create meaningful impact across products and industries.
