The big data revolution has fundamentally reshaped financial markets, generating vast streams of complex, high-dimensional data at unprecedented speed and scale. Extracting meaningful insights from this deluge demands sophisticated tools beyond traditional methods, blending econometrics, statistics, and machine learning. In this transformative landscape, Professor Li Yingying at HKUST is a leading pioneer, developing rigorous methodologies that unlock the potential of financial big data for statistical learning, large portfolio optimization, personalized financial decision-making, volatility estimation, and prediction. 

Professor Li’s research demonstrates exceptional depth and innovation at the intersection of statistics and finance. Her groundbreaking contributions include developing novel models for vast volatility matrix estimation using high-frequency data, enabling robust large-scale portfolio selection. She pioneered frameworks for individualized asset allocation using statistical learning, personalizing wealth management strategies. Her work on high-dimensional factor models, co-jump networks, and microstructure noise correction has redefined volatility measurement and inference under modern market conditions. These influential studies are consistently published in top journals in statistics, finance, and economics, including Econometrica, Review of Financial Studies, Journal of Financial Economics, Annals of Statistics, Journal of American Statistical Association, Journal of Econometrics. Her scholarly impact is further amplified through key editorial roles; she currently serves as Associate Editor for the Journal of the American Statistical Association and the Journal of Econometrics. 

Recognized as one of the foremost scholars in the field, Professor Li has received numerous accolades. She was awarded the highly competitive NSFC Young Scientist Scheme (Type A) / Distinguished Young Scholars (2025), RGC Senior Research Fellowship (2023) and the NSFC Excellent Young Scientist Award (2019). Her visionary projects, funded by the RGC and NSFC, aim to advance the frontier of financial big data research and transform cutting-edge innovations into practical tools for risk management, portfolio optimization, and asset pricing. Her professional honors also include being an elected Fellow of the Society for Financial Econometrics (SoFiE) and serving on its Council since 2019.  

Professor Li received her Ph.D. in Statistics from the University of Chicago (2008) and her B.Sc. in Mathematics from Beijing Normal University (2003). After she teaches and conducted post-doctoral research at Princeton University, she joins HKUST in 2009, and has demonstrated exceptional academic leadership. She formerly spearheaded the integration of machine learning into finance as Acting Head of the FinTech Thrust at HKUST(GZ). Currently, she leads the FinStaR@HKUST Lab, driving research that tackles fundamental challenges in portfolio analytics, high-dimensional inference, high-frequency data utilization, and personalized statistical learning for finance. 

Committed to nurturing future leaders, Professor Li is also a dedicated educator, repeatedly honored with the HKUST Business School’s Dean’s Recognition for Teaching Excellence. Her mentorship has guided over 15 PhD students and postdoctoral fellows to successful careers in academia and prominent financial institutions globally. 

Through her groundbreaking research, transformative leadership, and inspired teaching, Professor Li Yingying continues to shape the frontier of financial econometrics, empowering the finance industry to navigate the complexities and harness the opportunities of the big data era.