Publications
Publications
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- Faculty Publications (213)
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- March 2007 (Revised April 2007)
- Case
The University of Utah and the Computer Graphics Revolution
By: H. Kent Bowen and Courtney Purrington
Computer science departments were new to universities in the 1960s, and the one created at the University of Utah by David Evans and Ivan Sutherland had a research mission to invent the field of computer graphics. Details the research process that led to many of the... View Details
- 1999
- Article
Effects of Instructional Style on Problem-Solving Creativity
By: A. M. Ruscio and T. M. Amabile
This study sought to determine the impact of 2 differing instructional approaches on creative problem-solving performance. Eighty-two college students completed a novel structure-building task after receiving algorithmic instruction (providing a rote, step-by-step... View Details
- Forthcoming
- Article
Branch-and-Price for Prescriptive Contagion Analytics
By: Alexandre Jacquillat, Michael Lingzhi Li, Martin Ramé and Kai Wang
Contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision maker allocates shared resources across multiple segments of a population, each governed by... View Details
- Forthcoming
- Article
Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data
By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of... View Details
- Forthcoming
- Article
Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by... View Details
- 2025
- Chapter
Leapfrog Leaders: Accelerating Systems Leadership Skills
By: Laura Cabrera, Derek Cabrera and Hise O. Gibson
We need leaders who can execute at the Strategic, Operational, and Tactical (SOT) levels. But, research shows it takes time for skills to develop at all three levels—too much time. Why does it take too much time? First, because expertise is borne of experience. An... View Details
Keywords: DSRP; VCML; Strategy; Operations; Leadership Development; Decision Making; Organizational Structure
- Research Summary
Overview
By: Roberto Verganti
Roberto’s research focuses on how to create innovations that are meaningful for people, for society, and for their creators. He explores how leaders and organizations generate radically new visions, and make those visions come real. His studies lie at the intersection... View Details
- Research Summary
Overview
I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:
1. How to build... View Details
1. How to build... View Details
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
- Research Summary
Overview
By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
- Research Summary
Understanding the Limitations of Model Explanations
The goal of this research is to understand how adversaries can exploit various algorithms used for explaining complex machine learning models with an intention to mislead end users. For instance, can adversaries trick these algorithms into masking their racial and... View Details
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