Focus on Authors
Xinyu Cao (“A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection”) is vice-chancellor associate professor of marketing at the Chinese University of Hong Kong (CUHK) Business School. Prior to that, she was a visiting scholar at CUHK (2022–2023) and assistant professor of marketing at New York University Stern School of Business (2018–2023). She received her PhD in management (with a concentration in marketing) from the MIT Sloan School of Management. She also holds an MS degree in industrial engineering and operations research from University of California Berkeley and a BS degree in mathematics and physics from Tsinghua University. Her research focuses on the area of quantitative marketing, with emphases on digital marketing, social media, platform management, and market research methodology. Her research works have appeared in Marketing Science, M&SOM, and Marketing Letters. She has won the John D. C. Little Award for the best marketing paper published in INFORMS journals. She was selected as an MSI Young Scholar (2023). She is on the editorial board for Marketing Science.
Yijun Chen (“Exploring Peer Effects Associated with User Retention in a Socially Connected Business”) is an assistant professor in marketing at Imperial College Business School, Imperial College London. She received her PhD degree in marketing at John M. Olin Business School, Washington University in St. Louis. Her research focuses on peer effect and networks. Her research provides implications on how business and organizations could benefit from the understanding of peer interactions.
Yan Cheng (“A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection”) is an assistant professor in the College of Business at Shanghai University of Finance and Economics. Her research explores the intersection of operations management, platform economics, and data-driven decision-making, focusing on challenges in the creator economy, platform design, and online consumer behavior. She holds a PhD in management science and engineering from Tsinghua University, a master of environmental management from Duke University, and dual bachelor’s degrees in communication and economics from Tsinghua University.
Junhong Chu (“Bargaining and Network Effects in Two-Sided Platforms: Evidence from Online Healthcare”) is a professor of marketing at the HKU Business School. She holds a PhD in business administration from the University of Chicago and a PhD in demography from Peking University. As an empirical modeler, she focuses on big data, and her research interests include platform businesses, the sharing economy, e-commerce, and social media. Her research has been published in leading marketing, management, and general science journals. She is a 2011 Marketing Science Institute Young Scholar.
Alexandre de Cornière (“A Model of Information Security and Competition”) is a professor at the Toulouse School of Economics, where he is also the director of the TSE Digital Center. His research tackles questions related to the industrial organization of digital markets, competition policy, and regulation.
Aron Culotta (“Safety Reviews on Airbnb: An Information Tale ”) is a Professor of Computer Science at Tulane University and Director of the Tulane Center for Community-Engaged Artificial Intelligence. His research focuses on machine learning, natural language processing, and social network analysis, with emphasis on their societal implications.
Dante Donati (“The End of Tourist Traps: The Impact of Review Platforms on Quality Upgrading”) is an assistant professor of Marketing at Columbia Business School, specializing in the empirical analysis of firm and consumer behavior on online platforms. He is also the co-founder of Virtual Lab, an open-source tool for online survey recruitment and the evaluation of advertising campaigns. Before joining Columbia, he received a PhD in Economics from Pompeu Fabra University and collaborated with the World Bank on policy-oriented research.
Justin Huang (“Novelty in Content Creation: Experimental Results Using Deep Learning–Based Image Recognition on a Large Social Network”) is a LEO Lecturer of Marketing at the Ross School of Business at University of Michigan. His work examines platform design, online content creation, and the broader intersection of marketing and society, using econometrics, experiments, machine learning, and causal inference to study how content creators, consumers, advertisers, and technology platforms interact. His research aims to improve digital marketplace outcomes by uncovering the incentives and behaviors that shape them.
Yan Huang (“Algorithmic Lending, Competition, and Strategic Provision of Preapproval Tools”) is a tenured associate professor of business technologies at the Tepper School of Business, Carnegie Mellon University. Prior to joining Tepper, Huang was an assistant professor at the Ross School of Business, University of Michigan. Huang has received several prestigious awards, including the AIS senior scholar best publication of 2023 award and the INFORMS ISS Sandy Slaughter Early Career Award.
Ginger Zhe Jin (“Safety Reviews on Airbnb: An Information Tale ”) is the Neil Moskowitz Professor of Economics at the University of Maryland, College Park and a research associate at the National Bureau of Economic Research. Her research explores information asymmetry and market regulation, with applications in health, e-commerce, consumer protection, scientific innovation, and data policy. She has published in top journals across economics, management, and marketing.
Rupali Kaul (“Novelty in Content Creation: Experimental Results Using Deep Learning–Based Image Recognition on a Large Social Network”) is an assistant professor of marketing at INSEAD. Her research investigates areas such as marketing analytics, technology adoption, and customer centricity. She uses randomized field experiments, machine learning, and econometric analysis to causally study marketing strategies for small-scale firms and creators.
Puneet Manchanda (“Bargaining and Network Effects in Two-Sided Platforms: Evidence from Online Healthcare”) is the Isadore and Leon Winkelman Professor of Marketing at the University of Michigan’s Ross School of Business. He builds empirical models to solve marketing problems in the technology, gaming, media, and pharmaceutical industries. His work has been published in top marketing and econometrics journals.
Amit Mehra (“Frontiers: Content Quality Provision and Welfare Implications of Digital Platform Commission Fees”) is a professor of information systems at the Jindal School of Management, University of Texas at Dallas. He holds a PhD in information systems from the University of Rochester. He currently serves as an associate editor in Management Science and as a senior editor in the Production and Operations Management journal. His research has won several best paper awards. He uses analytical models, econometrics, experiments, and machine learning to examine how technology affects consumer behavior and firm strategies.
Nitin Mehta (“Exploring Peer Effects Associated with User Retention in a Socially Connected Business”) is the Ellison Professor of Marketing and PhD coordinator at the University of Toronto. His research focuses on structural models of consumer search, multi-category choices, imperfect recall, peer effects, patients’ healthcare decisions, binge consumption, economic impact of AI, and the adoption of AI by firms and consumers.
Sridhar Narayanan (“Novelty in Content Creation: Experimental Results Using Deep Learning–Based Image Recognition on a Large Social Network”) is the Sebastian S. Kresge professor of marketing at the Stanford Graduate School of Business. He empirically studies questions in marketing using a mix of field experiments, quasi-experimental approach, machine learning methods, and econometrics methods. He has published in leading journals including Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics, and Journal of Marketing.
Siddhartha Sharma (“Frontiers: Content Quality Provision and Welfare Implications of Digital Platform Commission Fees”) is an assistant professor of operations and decision technologies at the Kelley School of Business, Indiana University. He graduated with an MS in quantitative economics from the Indian Statistical Institute and with a PhD in information systems from Carnegie Mellon University. His research focuses on studying strategies and the implications of digital platforms using a variety of methodologies. He is a recipient of the INFORMS ISS Gordon B. Davis Young Scholar Award.
Zuo-Jun Max Shen (“A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection”) is vice-president and pro-vice-chancellor (research) and chair professor in logistics and supply chain management at The University of Hong Kong and Professor Emeritus of industrial engineering and operations research and civil and environmental engineering at UC Berkeley. His research centers on logistics and supply chain management, data-driven decision-making, and system optimization, with applications across business operations, energy and transportation systems, smart cities, healthcare management, and environmental sustainability. Professor Shen has collaborated extensively with industry and secured more than $10 million in research funding from organizations such as the U.S. National Science Foundation, Department of Energy, Department of Transportation, Caltrans, IBM, GM, Safeway, HP, Bayer, Siemens, Alibaba, and JD.com. He is a fellow of the Institute for Operations Research and the Management Sciences (INFORMS), a fellow and past president of the Production and Operations Management Society (POMS), a fellow of the Hong Kong Academy of Engineering Sciences, and a past president of the Society of Locational Analysis of INFORMS.
Param Singh (“Algorithmic Lending, Competition, and Strategic Provision of Preapproval Tools”) is the Carnegie Bosch Professor of Business Technologies and Marketing and associate dean for research at Carnegie Mellon University’s Tepper School of Business. Singh is a recipient of the INFORMS ISS Distinguished Fellow Award. Singh has also received the Don Lehmann Award, AIS Best Paper Award, the John D.C. Little Award, and Don Morrison Long-Term Impact Award.
K. Sudhir (“Entrepreneurs and Investors: Funding-Induced Distortions in Lean Start-up Product Experiments and Innovation”) is James L. Frank ‘32 Professor of Marketing, Private Enterprise and Management and the Founder-Director of the Yale China India Insights Program at the Yale School of Management, Yale University. He is also a professor of economics (by courtesy) at the Yale Economics Department. He leads the academic-industry interface for quantitative marketing at the Yale Center for Customer Insights. He received his PhD from Cornell University.
Yidan Sun (“Safety Reviews on Airbnb: An Information Tale ”) is an assistant professor in the leadership and organization science area at the School of Management, Binghamton University, State University of New York. She earned her PhD in management science from the Illinois Institute of Technology in May 2024. Her research explores the intersections of innovation, competition, and policy.
Greg Taylor (“A Model of Information Security and Competition”) is an industrial economist with a specialism in the economics of technology and competition in digital markets. He is associate professor and senior research fellow at the Oxford Internet Institute, University of Oxford, and associate editor of the Journal of Industrial Economics.
Liad Wagman (“Safety Reviews on Airbnb: An Information Tale ”) is the dean and a professor of economics at Rensselaer Polytechnic Institute’s Lally School of Management. He was formerly the dean and the John and Mae Calamos Endowed Chair at the Illinois Institute of Technology’s Stuart School of Business. He served as Senior Economic and Technology Advisor at the U.S. Federal Trade Commission and is an Academic Affiliate at the International Center for Law and Economics. His research focuses on technology, entrepreneurship, competition, and policy.
Jingbo Wang (“A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection”) is an assistant professor in the department of marketing at the Chinese University of Hong Kong (CUHK) Business School. He received his PhD in economics from the University of Southern California in Los Angeles. His research interests lie at the intersection of marketing analytics, quantitative marketing, causal deep learning, and social network analysis.
Qiaochu Wang (“Algorithmic Lending, Competition, and Strategic Provision of Preapproval Tools”) is an assistant professor of marketing at the Stern School of Business, New York University. He earned his PhD from the Tepper School of Business at Carnegie Mellon University. Wang has received several prestigious awards, including the William W. Cooper Doctoral Dissertation Award and the Paul and James W. Wang - Sercomm Presidential Fellowship, as well as the PNC Presidential Fellowship.
Onesun Steve Yoo (“Entrepreneurs and Investors: Funding-Induced Distortions in Lean Start-up Product Experiments and Innovation”) is an associate professor of operations & technology and marketing & analytics at the University College London School of Management, University College London. He serves as a codirector of the University College London Centre for Sustainable Business. He received his PhD from the Anderson School of Management, University of California, Los Angeles.
Xu Zhang (“Bargaining and Network Effects in Two-Sided Platforms: Evidence from Online Healthcare”) is an assistant professor of marketing at London Business School. Her research centers on information design and pricing strategies for digital platform markets, spanning various industries such as healthcare, freelancing, e-commerce, and online travel. Her work has appeared in Marketing Science and the Journal of Marketing Research.
Yuhui Zhang (“A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection”) received his BS in applied physics from Tianjin University and his PhD in physics from Florida State University. He brings extensive experience in data science and analytics across the technology and industry sectors, having held research and applied positions at global technology firms as well as leading internet and financial organizations in China. His professional interests span large-scale data analysis, recommendation systems, and computational modeling for data-driven decision-making.
Zihao Zhou (“Entrepreneurs and Investors: Funding-Induced Distortions in Lean Start-up Product Experiments and Innovation”) is an assistant professor of marketing & analytics at the University College London School of Management, University College London. He received his PhD from the Haas School of Business, University of California, Berkeley.
