Focus on Authors
Tommaso Bondi (“Alone, Together: A Model of Social (Mis)Learning from Consumer Reviews”) is an assistant professor of marketing at Cornell University. His research focuses on the economics of digitization, especially online reviews and privacy. He has studied how consumer heterogeneity can skew average online ratings; how CEO political activism affects corporate perception; and how, independent retailers adapt to the rise of large chains. Dr. Bondi holds a PhD in economics from New York University’s Stern School of Business.
Haozhe Chen (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is an incoming PhD student in computer science at Princeton University. He is interested in efficient, human-friendly, and capable artificial intelligence. He received his BS in computer science from Columbia University.
Doug J. Chung (“Time Dependence and Time Preference: Implications for Compensation Structure”) is the CBA Foundation Advisory Council Centennial Fellow, Associate Professor of Marketing at the McCombs School of Business, the University of Texas at Austin, and a senior external advisor for McKinsey & Company. He has focused research on sales management/strategy and incentive compensation. He received his PhD from Yale University and his BA from Korea University. Prior becoming an academic, he served as a platoon commander in the South Korean Special Warfare Command.
Haitao (Tony) Cui (“Behavior-Based Pricing Under Informed Privacy Consent: Unraveling Autonomy Paradox”) is the Ecolab-Pierson Grieve chair in international marketing at the Carlson School of Management and an affiliated professor at the Industrial Engineering Department, University of Minnesota. He is the Department Editor of OM-Marketing Interface Department at the journal of Production and Operations Management.
Runshan Fu (“Unequal Impact of Zestimate on the Housing Market”) is an assistant professor of marketing at New York University’s Stern School of Business. Her research focuses on studying the economic and business implications of machine learning algorithms, particularly their unintended disparate impacts across demographic groups. She employs machine learning techniques, analytical modeling, and structural modeling methods to investigate how algorithms influence decisions and behaviors and their potential role in social inequalities.
George Z. Gui (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is an assistant professor at Columbia Business School. He develops causal-inference and generative artificial intelligence methods to study marketing topics such as advertising, consumer engagement, and expectation management. He received his PhD in marketing from the Stanford Graduate School of Business.
Yan Huang (“Personalization, Consumer Search, and Algorithmic Pricing”) is a tenured Associate Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University. Prior to joining Tepper, she was an assistant professor at the Ross School of Business, University of Michigan. She received her bachelor’s degree from Tsinghua University and her PhD from Carnegie Mellon University.
Yan Huang (“Unequal Impact of Zestimate on the Housing Market”) is a tenured associate professor of business technologies at Carnegie Mellon University’s Tepper School of Business. Her research focuses on examining the economic and social impacts of technologies and identifying effective designs and policies. She employs economic theories, structural modeling, statistical modeling, and machine learning in her work. She is an early advocate for using structural econometric models to study design and policy questions in information systems.
Lalit Jain (“Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve”) is an assistant professor at the Foster School of Business at the University of Washington. His work focuses on efficient data collection intersecting with topics such as multiarmed bandits and recommendation systems.
Byungyeon Kim (“Time Dependence and Time Preference: Implications for Compensation Structure”) is an assistant professor of marketing at the Carlson School of Management, University of Minnesota. His research focuses on sales management, specifically in the domains of personal selling and B2B”) is a marketing PhD candidate at the Carlson School of Management, University of Minnesota. Before joining the PhD program, he worked as a senior researcher at the Korean Broadcasting System, the largest terrestrial broadcasting and television station company in South Korea, with an educational background in computer science and industrial engineering.
Yunhyoung Kim (“Behavior-Based Pricing under Informed Privacy Consent: Unraveling Autonomy Paradox”) is a Marketing PhD candidate at the Carlson School of Management, University of Minnesota. Before joining PhD program, he worked as a senior researcher at Korean Broadcasting System, the largest terrestrial broadcasting and television station company in South Korea, with an educational background in computer science and industrial engineering.
Vineet Kumar (“Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve”) is a faculty member at the Yale School of Management and an expert on digital technologies and artificial intelligence. His substantive focus areas include digital business models and monetization models. He develops models of consumers and firms based on microfoundations to help understand how they would respond to different policies. His work has been published in top academic and practice journals, won awards, and been featured in national and international media.
Ang Li (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is an incoming PhD student in computer science at New York University. His research focuses on artificial intelligence (AI) reasoning, AI agents, and AI alignment and safety. He received his BS and MS degrees in computer science from Columbia University.
Tesary Lin (“Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data”) is an Assistant Professor of Marketing at Boston University and holds the Isabel Anderson Career Development Professorship. Her research focuses on consumer privacy and data analytics, exploring how consumer data influence digital markets. She obtained her PhD from the University of Chicago Lin has been a recipient of the John D.C. Little Best Paper Award, the Alessandro di Fiore Best Paper Award, the Sheth Foundation ISMS Doctoral Dissertation Award, and the MSI Alden G. Clayton Doctoral Dissertation Award.
Nitin Mehta (“Unequal Impact of Zestimate on the Housing Market”) is the Ellison Professor of Marketing at the University of Toronto. His research focuses on structural models of consumer search, imperfect recall and learning, consumers’ healthcare decisions, the economics of binge consumption, the adoption of artificial intelligence (AI) by firms and consumers, and the socioeconomic impact of AI. He received the 2024 Excellence in Doctoral Mentorship Award at the University of Toronto. He is the Vice President of Education at the ISMS.
Daniel J. Merlau (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is a staff associate in the Faculty of Business at Columbia Business School. His research interests center on marketing strategy, including customer lifetime value, brand equity, and managerial applications of emergent technologies and methods. He received his BA and MBA from Columbia University.
Sridhar Moorthy (“Advertising Platforms and Privacy”) is Professor and Manny Rotman Chair in Marketing at the Rotman School of Management. He is a senior consultant at Charles River Associates and a fellow of the INFORMS Society for Marketing Science. He has served as coeditor-in-chief of Quantitative Marketing and Economics and held other editorial positions at Marketing Science and Management Science.
Andrés Musalem (“Consumer Response to Monetary Subsidies: A Structural Demand Analysis of the Supplemental Nutrition Assistance Program”) is an associate professor in the Industrial Engineering Department of the University of Chile. His research interests include the development of empirical methods for the study of consumer behavior and the use of game theory to study marketing strategy. Some areas of application include retailing, customer relationship management, and consumer learning.
Rudolf-Harri Oberg (“Consumer Response to Monetary Subsidies: A Structural Demand Analysis of the Supplemental Nutrition Assistance Program”) is an applied scientist at Uber Technologies Inc. He focuses on modeling and data analysis to advise Uber on how to more efficiently deploy capital across Uber’s Marketplace levers. Before joining Uber, he was an assistant professor at Deakin Business School. He holds a PhD in economics from Duke University.
Byoung G. Park (“Time Dependence and Time Preference: Implications for Compensation Structure”) is an Associate Professor of Economics at the University at Albany, State University of New York. He received his PhD in Economics from Yale University and his BA in Economics from Seoul National University. His research interests lie in econometrics and its applications. His work has been published in leading academic journals, including Management Science, Marketing Science, and the Journal of Econometrics.
Tianyi Peng (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is an assistant professor at Columbia Business School. His research focuses on artificial intelligence (AI) for decision making, spanning agentic simulation, reinforcement learning, and AI system optimization. He received his PhD in statistics and operations from Massachusetts Institute of Technology.
Liying Qiu (“Personalization, Consumer Search, and Algorithmic Pricing”) is a PhD candidate in Business Technologies (minor in Machine Learning) at the Tepper School of Business, Carnegie Mellon University. Prior to PhD, she received her master’s degrees from Duke University and bachelor’s degree from Peking University.
Xianwen Shi (“Advertising Platforms and Privacy”) is a professor of economics at the University of Toronto. His research focuses on mechanism and information design, with contributions to information acquisition, disclosure, transfers, and commitment. He has published in top journals including American Economic Review, Econometrica, Journal of Political Economy, and Review of Economic Studies.
Param Vir Singh (“Personalization, Consumer Search, and Algorithmic Pricing”) is the Carnegie Bosch Professor of Business Technologies and Marketing at Carnegie Mellon’s Tepper School of Business and Associate Dean for Research. He is a Senior Editor for Information Systems Research and an Associate Editor for Management Science. He was recognized as the youngest recipient of the INFORMS Information Systems Society Distinguished Fellow award and named a PhD Distinguished Alumnus by the University of Washington in 2022.
Param Vir Singh (“Unequal Impact of Zestimate on the Housing Market”) is the Carnegie Bosch Professor of Business Technologies and Marketing and Associate Dean for Research at Carnegie Mellon University’s Tepper School of Business. His research focuses on the intersection of economics, machine learning, and artificial intelligence (AI), with an emphasis on algorithmic bias, economic inequality, and AI's societal impacts. He specializes in developing economic-aware machine learning algorithms and studying the economic value of unstructured data.
Kannan Srinivasan (“Personalization, Consumer Search, and Algorithmic Pricing”) has been the H. J. Heinz II Professor of Management, Marketing and Business Technology, at the Tepper School of Business since 1999. He has also taught at the business schools of the University of Chicago and Stanford University. In 2013, he was elected as a Fellow to the Informs Society of Marketing Science, one of the leading societies for data analytics, for his lifetime contribution to the field. He also served as the president of the Society.
Kannan Srinivasan (“Unequal Impact of Zestimate on the Housing Market”) is the H. J. Heinz II Professor of Management, Marketing and Business Technology at Carnegie Mellon University’s Tepper School of Business. He has worked extensively on advanced data analytics models and published over 70 papers in leading management, marketing, and statistical journals. In 2013, he was elected as a Fellow to the INFORMS Society of Marketing Science for his lifetime contribution to the field. He also served as the president of the society.
Avner Strulov-Shlain (“Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data”) research focuses on the behavioral economics of firms. He is interested in how companies interact with consumers who have psychological biases. How should firms respond to these consumers? And how do they respond in practice? To answer these questions, Strulov-Shlain uses observational data, natural experiments, field experiments, and laboratory experiments. Strulov-Shlain earned his PhD in economics from University of California, Berkeley.
Chenshuo Sun (“How Does Prepopulating Search Bars with Keywords Affect Online Consumer Behavior? A Field Experiment”) is an assistant professor at Tsinghua University. He earned his PhD from New York University. His research, published in journals like Management Science, Marketing Science, and Information Systems Research, has earned multiple awards, including the INFORMS ISS Nunamaker-Chen dissertation award runner-up and the JP Morgan PhD fellowship. He is on the editorial review board of Management Science.
Ahmed Timoumi (“Bricks Processing Returns for Clicks: Can Foes Become Friends?”) is an assistant professor of marketing at the Indian School of Business. He holds a PhD in business administration from Koç University. His current research interests include retailing, distribution channels, and sales management. His research has appeared in Production and Operations Management and Journal of Retailing.
Olivier Toubia (“Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions”) is the Glaubinger professor of business at Columbia Business School. His research focuses on various aspects of innovation, including preference measurement and idea generation. Specifically, he combines methods from social sciences and data science to study human processes such as motivation, choice, and creativity. He received his MS in operations research and PhD in marketing from Massachusetts Institute of Technology.
Abhinav Uppal (“Bricks Processing Returns for Clicks: Can Foes Become Friends?”) is an assistant professor of marketing at the Indian School of Business. His research examines how various market settings, structures, and strategic partnerships influence firms’ competitive behavior and marketing decisions. His current research interests include retailing, competitive strategy and pricing. He holds a PhD and an MS in marketing from the Wharton School, University of Pennsylvania and a BTech in computer science from the Indian Institute of Technology (IIT) Delhi.
Ian N. Weaver (“Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve”) is an assistant professor of marketing at the National University of Singapore Business School. He earned his PhD in marketing from the Yale School of Management following earlier degrees in economics and mathematics from the University of Toronto. His research focuses on algorithmic and experimental approaches to pricing, digital platform design, and consumer engagement on online platforms.
Yi Zhu (“Behavior-Based Pricing Under Informed Privacy Consent: Unraveling Autonomy Paradox”) is the Margaret J. Holden and Dorothy A. Werlich endowed professor at Carlson School of Management, University of Minnesota. He has a PhD in business administration and an MA in economics. He received the John D.C. Little Award for best marketing paper published in Marketing Science or Management Science and the Don Morrison Long Term Impact Award for making a significant long run impact on the field of marketing. He serves as an associate editor for Marketing Science.
Ruizhi Zhu (“Advertising Platforms and Privacy”) is a postdoctoral research associate in the marketing department at Northeastern University. He earned his PhD in economics from the University of Toronto in 2023 and was a research member of Simons Laufer Mathematical Sciences Institute in 2023. His research focuses on consumer privacy, the platform economy, and competitive marketing strategy.
