Dennis J. Zhang

Professor Zhang works at the intersection of artificial intelligence, digital economics, and platform strategy. He is a professor of operations and marketing at Olin Business School at Washington University in St. Louis and a Senior Consultant with Compass Lexecon. His work combines economic modeling, causal inference, machine learning, large-scale experimentation, and structural econometrics to study how digital platforms use data, algorithms, ranking systems, pricing rules, advertising tools, recommender systems, and marketplace design to shape competition, consumer behavior, supplier incentives, and platform performance.

A distinguishing feature of Professor Zhang’s work is his direct experience with major digital platforms and retailers including Alibaba, Amazon, Airbnb, Cainiao, Kuaishou, NetEase, VIPKid, and Halara. This work has involved recommender system design at NetEase Music, human-AI interaction and algorithm design in Alibaba’s supply-chain and e-commerce operations, AI experimentation on content platforms such as Kuaishou, and large-scale empirical studies of advertising effectiveness, pricing, online education, retail operations, marketplace design, and platform governance.

Professor Zhang’s recent work examines how generative AI and AI agents change digital markets, consumer behavior, and productivity. He has studied how AI search affects content platforms using large-scale field experiments from one of Brazil’s largest content platforms, and how Google’s AI search features affect referral traffic to platforms such as Reddit. He also studies how agentic coding tools, such as Claude Code and OpenAI Codex, affect developer productivity and market competition in the iOS app marketplace. Using large-scale field experiments with platforms, he also studies how privacy features and recommender-system diversity can be designed to optimize user welfare and platform performance.

Professor Zhang’s expertise is relevant to economic and technical questions that arise in matters involving digital platforms, generative AI, recommender systems, online advertising, algorithmic pricing, consumer choice, data-driven decision-making, AI policy, and privacy design. His methods can inform analyses of causality, counterfactual outcomes, common impact, damages, market effects, and welfare where platform data, experiments, machine-learning models, or recommender systems are central to the economic evidence.

Professor Zhang’s scholarship bridges economics, business, and computer science. His work appears in leading economics and business journals and at top machine-learning conferences, including ICML and NeurIPS. He serves as Area Editor for Machine Learning and Data Science at Operations Research and as an Associate Editor at Management Science, Marketing Science, Manufacturing & Service Operations Management, and the Journal of Marketing Research. Media outlets including The New York Times, National Public Radio, and The Globe and Mail have referenced his analyses. Prior to his academic career, Professor Zhang worked at Google.

Professor Zhang’s experience brings an integrated perspective on AI-enabled platform systems and their economic effects, combining technical expertise in machine learning, recommender systems, large-scale experimentation, and causal inference with the economic tools needed to evaluate competition, consumer behavior, market design, productivity, privacy, and damages.