People / Students / Class of 2027
Jiahui (Andy) Hu graduated with distinction from Xi'an Jiaotong-Liverpool University with a Bachelor of Science in Information Management and Information Systems, earning a 3.92/4.00 GPA and ranking in the top 1% of his major. He received the University Academic Excellence Award and Academic Achievement Award in consecutive years, and holds the DeepLearning.AI TensorFlow Developer Professional Certificate. Through coursework and independent projects, he built a strong technical foundation across Python, SQL, R, Java, and MATLAB, which he has applied extensively to data-driven decision-making problems in both industry and academic research.
Andy has gained progressively deeper data science experience across e-commerce, fintech, and healthcare technology. As a Machine Learning Intern on TikTok Shop's Europe and UK business line, he participates in designing ML-based user–coupon matching models that personalize discount offers to drive new-customer conversion and retention. At JD.com's Retail Marketing Operations team, he built pricing-elasticity models using segmented regression and ABC classification that contributed to a ~10% average monthly GMV uplift for bundled products. At Dewu (Poizon), he led multivariate root-cause analysis on 100K+ merchant transaction records and designed a grayscale/AB-testing framework that improved bid-matching accuracy by ~20%. At Philips Healthcare, he built an XGBoost-based failure early-warning model achieving ~95% detection accuracy. Beyond his internships, Andy has also pursued independent research projects to deepen his technical expertise and explore emerging methods, applying TF-IDF and LDA topic modeling with a difference-in-differences design in a study with Xi'an Jiaotong-Liverpool University's School of International Business to quantify how AI exposure reshapes mutual fund disclosure strategy.
Andy is currently pursuing his Master's in Machine Learning and Data Science at Northwestern University, where he aims to deepen his expertise in deep learning, generative AI, and causal inference through coursework and hands-on projects, while strengthening the statistical rigor needed to connect business strategy with technical model design. His long-term goal is to grow into a data scientist who combines technical depth with business acumen, translating cutting-edge machine learning methods into solutions that deliver real, measurable value for organizations.
