Mochen Yang

Mochen Yang is an Associate Professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota.

His main research revolves around the topic of algorithmic decision making and consists of three connected streams. The first stream of work explores the problem of designing theoretically robust and computationally efficient algorithms and strategies to support decision making in information-intensive marketplaces. The second stream of work examines the antecedents of algorithmic decision making as well as its impact on decision quality, fairness, and privacy. The third stream of work explores the design of novel approaches to draw robust statistical inferences with variables generated by machine learning algorithms.

Before joining the Carlson School, Yang was an Assistant Professor in the Department of Operations and Decision Technologies at Kelley School of Business, Indiana University. He received his PhD from the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota. Yang’s dissertation studies user-generated content and associated user engagement behavior on company-managed social media pages. He obtained his Bachelor’s degree in Information Systems Management from the School of Economics and Management at Tsinghua University.

Education:

• Carlson School of Management, University of Minnesota 2013 - 2018
  PhD in Business Administration, Department of Information & Decision Sciences
• School of Economics and Management, Tsinghua University 2009 - 2013
  Bachelor of Information Management and Information System
• Rotman School of Management, University of Toronto 9/2011 - 12/2011
  Exchange Student

Expertise:

• Business Analytics
• Machine Learning
• Social Media

Selected Works & Activities

When Algorithms Err: Differential Impact of Early vs. Late Errors on Users’ Reliance on Algorithms. ACM Transactions on Computer-Human Interaction (TOCHI), forthcoming SSRN

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. INFORMS Journal on Data Science, forthcoming. SSRN

Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. ACM Transactions on Management Information Systems (TMIS), 2022.

Bidder Support in Multi-Item Multi-Unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework. Gediminas Adomavicius, Alok Gupta, Mochen Yang. Information Systems Research, forthcoming

Engagement by Design: An Empirical Study of the “Reactions” Feature on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. ACM Transactions on Computer-Human Interaction (TOCHI), 2020

Designing Real-Time Feedback for Bidders in Homogeneous-Item Continuous Combinatorial Auctions. Gediminas Adomavicius, Alok Gupta, Mochen Yang. MIS Quarterly, 43(3), 721-743, 2019.

Efficient Computational Strategies for Dynamic Inventory Liquidation. Mochen Yang, Gediminas Adomavicius, Alok Gupta. Information Systems Research, 30(2), 595-615, 2019.

Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Mochen Yang, Gediminas Adomavicius, Gordon Burtch, Yuqing Ren. Information Systems Research, 29(1), 4-24, 2018.

Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. Mochen Yang, Yuqing Ren, Gediminas Adomavicius. Information Systems Research, 30(2), 839–855, 2019