Her research focuses on computational and economic sciences, with special interests in computational mechanism design, prescriptive machine learning, human–AI interactions, examining how groundbreaking technologies including interdisciplinary big data, blockchain, innovative computing, can advance trustworthy, sustainable, and human-centered socio-economic systems.

Her research appears in leading journals and conferences spanning economics and computational sciences, including Review of Economics and Statistics, Nature Scientific Data, NeurIPS, ACM CCS, AAAI/ACM AIES, ACM CSCW, and IEEE S&P among others. Her current project, “Trust Mechanism Design on Blockchain: An Interdisciplinary Approach of Game Theory, Reinforcement Learning, and Human-AI Interactions,” is supported by the National Science Foundation of China (NSFC). Her recognitions include the Kunshan Shortage Industrial Talent Program Award (2021), selection as one of 60 Pioneers in Blockchain Innovation (2022), election as a National Member Representative of the China Computer Federation (2023), and selection for the Suzhou Association for Science and Technology Young Talent Support Program and Senior Membership of the CCF (2024). She serves on the Editorial Board of Nature Scientific Data and has contributed as editor, reviewer, and program committee member across major venues in economics, computational science, and blockchain. She also serves as executive member of the CCF technical committee on Computational Economics and Data Development & Governance, and IEEE SA working group secretary.

She earned her Ph.D. in Economics from The Ohio State University supported by Presidential Fellowship and NSF and holds a B.A. in Economics and a B.S. in Mathematics and Applied Mathematics from Peking University with National Fellowship. She has also completed lifelong learning certifications from Oxford and MIT in blockchain, reinforcement learning, data engineering, generative AI, machine learning, and quantum computing.