2025 ADEFT-XueShuo Summer Institute on
"Financial Economics Meets Data Science"
July 7-9, 2025
Xiamen, China
Online & Onsite
In collaboration with various research institutes and centers around the globe leading research in the area of AI, Digital Economics, and Financial Technology (ADEFT), XueShuo, a premium academic sharing platform incubated by Tsinghua University, is proud to co-organize the 2nd Summer Institute online and onsite in Xiamen, together with School of Economics, Wang Yanan Institute for Studies in Economics (WISE), and Paula and Gregory Chow Institute for Studies in Economics, Xiamen University. The theme this year is "Financial Economics Meets Data Science," which aims to integrate both theory and empirical research, industrial practice, and policymaking in the field of financial economics and data science, and to disseminate cutting-edge knowledge to a global audience of researchers and scholars. The 2025 Summer Institute will be held in July 7-9.
This summer institute is part of the ADEFT Academy Initiative spearheaded by the Xueshuo Platform and world-renowned research institutes and centers, which leverages digital technology to build a one-stop platform to introduce the latest research from world-leading scholars to researchers and students around the world, promote exchanges among scholars across regions and borders, and to organize knowledge of research and programs, all in the area of digital economics and financial technology. In particular, it aims to facilitate peer interactions, discussions, and intellectual exchanges among researchers in academia and in industry. The summer institute primarily takes the format of a research camp, where participants will study and interact with leading researchers in these areas, receive timely advice and feedback on their research, and network with fellow researchers of shared academic interests.
Co-Organizers
XueShuo Platform
School of Economics, Xiamen University
Wang Yanan Institute for Studies in Economics (WISE), Xiamen University
Paula and Gregory Chow Institute for Studies in Economics, Xiamen University
Host Institution
Department of Finance, School of Economics, Xiamen University
Academic Support
FinTech at Cornell Initiative
Stanford Advanced Financial Technologies Laboratory
The Center for Digital Finance and Technologies at Columbia University
Fudan-Stanford Institute of Financial Technology & Risk Analytics
Program Co-chairs
Agostino Capponi, Columbia University IEOR and Center for Digital Finance & Technologies
Markus Pelger, Management Science & Engineering, Stanford University & NBER
Yinggang Zhou, Xiamen University, School of Economics & Wang Yanan Institute for Studies in Economics
Summer Institute Programs
There will be an opening keynote by Prof. Jianqing Fan, a special sharing session by editors of leading journals, a closing keynote by Prof. Andrey Malenko, in addition to an industry symposium, campus visit, and lectures and sessions taught by various speakers.
The topics covered include applied economic theory, financial data science, business big data, asset pricing, financial intermediation, corporate finance, digital platforms, technology and entrepreneurship, among others.
Paper Presentations
A small number of papers submitted by participants will be selected for short presentations, and receive expert comments and feedback.
Summer Institute Practice
We provide the opportunity for onsite participants to an industry symposium with leading AI/FinTech firms during the summer institute.
Speakers Introduction (alphabetical order)
Agostino Capponi
Columbia University
Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His research has been recognized with the 2018 NSF CAREER award, a JP Morgan AI Research Faculty award, the UBRI Innovator award, and the Presidential Early Career Award for Scientists and Engineers award (PECASE). His research has also been covered by various media outlets, including Bloomberg, the Financial Times, Vox, and Politico. Agostino is a fellow of the crypto and blockchain economics research forum, and an academic fellow of Alibaba's Luohan academy. He serves as an editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and area editor of Operations Research. He has held editorial positions at several major journals of his field, including Stochastic Systems, Stochastic Models, the SIAM Journal on Financial Mathematics, Mathematical Finance, and Finance and Stochastics. Agostino is the former Chair of the SIAG/FME Activity Group and of the INFORMS Finance Section, and is currently a member of the Council of the Bachelier Finance Society. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, published in 2023 by the Cambridge University press.
Research interests: Financial technology, machine learning in finance, market microstructure, energy markets, climate finance, financial and supply chain networks.
（More information：https://www.engineering.columbia.edu/faculty-staff/directory/agostino-capponi）
Lin William Cong
Cornell University SC Johnson College of Business
Rudd Family Professor of Management and tenured Professor of Finance at the Johnson Graduate School of Management at Cornell University SC Johnson College of Business. He is also the founding faculty director for the FinTech Initiative at Cornell, a faculty scientist at the Initiatives for Cryptocurrencies and Contracts (IC3), and a research associate at the National Bureau of Economic Research. His research has been published in top economics and finance outlets, including Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Journal of Economic Theory, Journal of Accounting and Economics, Management Science, etc. He currently serves as a Finance Editor for Management Science, and have served on the editorial and advisory boards for the Journal of Financial Intermediation, Journal of Portfolio Management, Journal of Corporate Finance, and Journal of Banking and Finance, among others.
Research interests: financial economics, information economics, FinTech and Economic Data Science, Entrepreneurship, and China.
(More information：https://www.linwilliamcong.com)
Jianqing Fan
Princeton University
Dean of The School of Artificial Intelligence (SAI) of The Chinese University of Hong Kong, Shenzhen and Foreign member of the Royal Academies for Science and the Arts of Belgium, is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and former Chairman of the Department of Operations Research and Financial Engineering at Princeton University, where he directs both the financial econometrics lab and statistics lab. He previously held professorships at UNC-Chapel Hill and UCLA. He has authored or co-authored over 300 articles on finance, econometrics, statistical machine learning, analysis of Big Data, and various aspects of theoretical and methodological statistics and machine learning. His finance work focuses on the analysis of high-frequency data, empirical asset pricing, option pricing, portfolio theory, risk assessment, high-dimensional data, and time series. He is a joint editor of the Journal of American Statistical Association, and was the joint editor of Journal of Business and Economics Statistics, Journal of Econometrics, and Annals of Statistics, and has served as associate editor of Econometrica, Management Science, and Journal of Financial Econometrics. His published work has been recognized by the 2000 COPSS Presidents’ Award, the 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellowship in 2009, P.L. Hsu prize in 2013, Guy Medal in Silver in 2014, Noether Distinguished Scholar Award in 2018, and IMS Le Cam Award and Lecture in 2021, and IMS Wald Award and Lectures in 2025. He is an Elected Fellow of the American Association for Advancement of Science, the Society of Financial Econometrics, the Institute of Mathematical Statistics, and the American Statistical Association, and a past President of the Institute of Mathematical Statistics.
Research interests: Statistical theory and methods in data science, statistical machine learning, finance, economics, computational biology, biostatistics with particular skills on high-dimensional statistics, machine learning, spectral methods, neural networks, reinforcement learning, nonparametric modeling, longitudinal and functional data analysis, survival analysis, nonlinear time series, wavelets, among others.
（More information：https://fan.princeton.edu/）
Fuwei Jiang
Xiamen University
The tenured full Professor of Finance at the Department of Finance of School of Economics & Wang Yanan Institute for Studies in Economics (WISE), Xiamen University, Xiamen, China. He holds the the MOE's Changjiang Scholar professorship, XMU's Qiu Huabing Young Scholar professorship, and Nanqiang Youth Talent Program (A Level), etc. He is used to be the full Professor of Finance and Department Chair of Financial Engineering at the Central University of Finance and Economics (CUFE), Beijing, China.
Research interests: Artificial intelligence (AI) for finance, FinTech, machine learning and texual analytics, asset pricing, behavioral finance, macro finance, monetary and fiscal policies, and Chinese capital markets.
（More information: https://faculty.xmu.edu.cn/jiangfw/zh_CN/index.htm）
Semyon Malamud
Swiss Finance Institute at EPFL & CEPR
Associate Professor of Finance at the Swiss Federal Institute of Technology in Lausanne and the Director of the Financial Engineering Section. He holds a Senior chair at the Swiss Finance Institute, is a Lamfalussy fellow of the European Central Bank, and a research fellow of the Centre of Economic Policy Research (CEPR) and the Bank for International Settlements. His research has been published in top economics and finance outlets, including Econometrica, American Economic Review, Journal of Finance, Review of Financial Studies, Journal of Financial Economics, and Journal of Economic Theory. His research has also been recognized with several awards, including the joint INQUIRE Europe-INQUIRE UK prize, the Dauphine-Amundi Chair in Asset Management award, the Europlace Institute of Finance award, and the ETF Academy Award. He currently serves as the associate editor of Journal of Finance.
Research Interests: Big Data, Machine Learning, Liquidity and Market Frictions, International Finance, Information Economics, Equilibrium Asset Pricing, Over-the-Counter Markets, Network Economics, Corporate Finance, Optimal Contracting and Security Design, Macroeconomics.
（More information: https://www.epfl.ch/labs/sfi-sm/)
Andrey Malenko
Boston College Carroll School of Management
Professor in the Seidner Department of Finance at the Boston College Carroll School of Management. His recent work has examined the optimal design of securities, regulation of shareholder voting, auctions of companies, and the role of institutional investors in corporate governance. His work has been published in leading academic journals, such as American Economic Review, Review of Economic Studies, Journal of Finance, Journal of Financial Economics, and The Review of Financial Studies. He is the recipient of the 2020 Brattle Prize for a distinguished paper in corporate finance in the Journal of Finance. He is a research fellow at the Centre for Economic Policy and Research, a research member at the European Corporate Governance Institute. He currently serves as the editor of Review of Financial Studies. Research Interests: Corporate Finance, Corporate Governance, Information Economics, Auctions, Organizational Economics
（More information: https://www.amalenko.com/)
Léa H. Stern
Foster School of Business, University of Washington
Assistant Professor in the Finance and Business Economics Department at the Foster School of Business, University of Washington and Carol Batchelder Endowed Finance Faculty Fellow. She is among the earliest to apply machine learning to corporate finance and venture capital research. Her works have been published in Review of Financial Studies, Journal of Financial Intermediation, Journal of Financial and Quantitative Analysis, American Economic Review: Papers and Proceedings, and Journal of Corporate Finance.
Research interests: Her research interests span a variety of areas in corporate finance and private equity. Her current research leverages machine learning prediction methods to study inefficiencies in economic agents’ decision making.
（More information: https://leastern.com/)
Liyan Yang
University of Toronto
Professor of Finance and Peter L. Mitchelson/SIT Investment Associates Foundation Chair in Investment Strategy at the Rotman School of Management, University of Toronto (with a cross-appointment in the Department of Economics). He has received the Bank of Canada Fellowship Award and the Bank of Canada Governor's Award. He is a senior fellow of Asian Bureau of Finance and Economic Research (ABFER), a fellow of the Accounting and Economics Society (AES), a fellow of Bank of Canada, a research fellow of Bank for International Settlements (BIS), a fellow of Cornell FinTech Initiative, a fellow of Luohan Academy, and a fellow of UIUC Office for Futures and Options Research. His research has been published in Journal of Economic Theory, Journal of Financial Economics, Journal of Finance, and Review of Financial Studies, etc. He is serving as a co-editor at Journal of Financial Markets and Journal of Economic Dynamics and Control. He is a current associate editor at Journal of Economic Theory and Management Science, and a former associate editor at Journal of Finance, Journal of Financial Markets, and Journal of Economic Dynamics and Control.
Research Interests: Financial Markets, Financial Institutions, Behavioral Finance, Digital Economy.
（More information: http://www.individual.utoronto.ca/liyanyang/)
Luyao Zhang
Duke Kunshan University
Luyao (Sunshine) Zhang is a tenure-track Assistant Professor of Economics and Senior Research Scientist at the Digital Innovation Research Center at Duke Kunshan University (DKU). Her research appears in leading journals and conference proceedings spanning economics and computational sciences, including Review of Economics and Statistics, Nature Research Scientific Data, Springer Nature Social Indicators Research, Springer Nature Eastern Economic Journal, NeurIPS, ACM CCS, AAAI/ACM AIES, ACM CSCW, IEEE S&P, American Economic Review: Papers and Proceedings, IEEE International Conference on Blockchain (IC), Remote Sensing, Journal of Digital Earth, and Data and Information Management, among others.
Research interests: She conducts interdisciplinary research at the intersection of computational and economic sciences, addressing challenges for humanity, society, and sustainability through groundbreaking technologies, including big data, blockchain, generative AI, innovative computing, and geospatial frontiers. She is deeply passionate about interdisciplinary collaborations, particularly cutting-edge research with both profound insights and practical impacts, such as computational mechanism design, prescriptive machine learning, and human-AI interactions.
（More information: https://scholars.duke.edu/person/luyao.zhang）
Application.
The application is open to all instructors and researchers, including the ones in the industry. The deadline is June 20, 2025. Applicants should send a full CV to ADEFT@51xueshuo.com with email titled “2025 Summer Institute on `Financial Economics Meets Data Science’.”
Students (undergraduate; master’s; or doctoral candidates) are required to submit documentation verifying their current enrollment status as part of the registration process to qualify for the student discount rate.
Applicants are encouraged to submit a working paper related to the theme of the conference. Some papers will be selected based on their quality and topic fit to be presented and discussed at the summer institute.
Registration Fees
Onsite: $410 USD for students and $820 USD for teachers, researchers, and practitioners. The registration fees cover the costs for the courses, materials, reviews and comments, tea breaks during the summer institute. Onsite participants are responsible for their own travel and accommodation costs.
Online：$205 USD for students and $410 USD for teachers, researchers, and practitioners.
Early Bird Registration Discount: 10% reduction applicable for submissions received prior to 20 May 2025.
All participants will have access to a replay of the course, which will be valid for a period of one month. Participants are eligible to receive an invoice and an invitation letter for the summer institute.
Contact Information
Email: ADEFT@51xueshuo.com
Telephone: +86 13263354082 (Ms. Zhang , WeChat: 13263354082);
+86 13070178693 (Mr. Yang , WeChat: yangzhan678)
Conference Website: https://conference.51xueshuo.com/#/index/2025SI
