Rina Foygel-Barber is a Professor in the Department of Statistics at the University of Chicago. Before starting at U of C, she was an NSF postdoctoral fellow during 2012-13 in the Department of Statistics at Stanford University, supervised by Emmanuel Candès. She received her Ph.D. in Statistics at the University of Chicago in 2012, advised by Mathias Drton and Nati Srebro, and an MS in Mathematics at the University of Chicago in 2009. Prior to graduate school, she was a mathematics teacher at the Park School of Baltimore from 2005 to 2007.

Prof. Hartline’s research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic and legal systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation, which is under preparation..He received his Ph.D. in 2003 from the University of Washington under the supervision of Anna Karlin. He was a postdoctoral fellow at Carnegie Mellon University under the supervision of Avrim Blum; and subsequently a researcher at Microsoft Research in Silicon Valley. He joined Northwestern University in 2008, where he is a professor of computer science. He was on sabbatical at Harvard University in the Economics Department during the 2014 calendar year and visiting Microsoft Research, New England for the Spring of 2015. Prof. Hartline was a founding co-director of the Institute for Data, Econometrics, Algorithms, and Learning from 2019-2022 and is a co-founder of the virtual conference organizing platform Virtual Chair.

Ming Hu is the University of Toronto Distinguished Professor of Business Operations and Analytics, a professor of operations management at the Rotman School of Management, and an Amazon Scholar. He was named one of Poets & Quants Best 40 Under 40 business school professors in 2018. His research has been featured in mainstream media, such as the Financial Times. Most recently, his research has focused on operations management in the context of two-sided markets, sharing economy, social buying, crowdfunding, and crowdsourcing, with the goal of making full use of operational decisions to the benefit of society. He recently edited a Springer book titled Sharing Economy: Making Supply Meet Demand on operations management in the age of the sharing economy. He is the recipient of the Wickham Skinner Early-Career Research Accomplishments Award by the POM Society (2016) and the Best Operations Management Paper in Management Science Award by INFORMS (2017). He currently serves as the editor-in-chief of Naval Research Logistics, department editor of Service Science, associate editor of Operations Research, Management Science, and Manufacturing & Service Operations Management, and senior editor of Production and Operations Management. He is a former chair of the Revenue Management and Pricing (RMP) Section at the Institute for Operations Research and the Management Sciences. He received a master's degree in Applied Mathematics from Brown University in 2003 and a Ph.D. in Operations Research from Columbia University in 2009. For more details of his research, please visit http://ming.hu.

Maytal Saar-Tsechansky is the Mary John and Ralph Spence Centennial Professor at the McCombs School of Business, and a co-founder of Sweetch, an AI-based health company. Her research pioneered business-inspired AI, where she challenged traditional AI goals and promoted AI advances that focus on human, organizational, and societal goals and contexts. Maytal focuses on research that advances AI to improve decision-making and to benefit people, organizations, and society more broadly. Her recent work focuses on human-AI collaboration and on trustworthy AI, focusing on developing AI that focuses on human, organizational, and societal goals and that accounts for key aspects of the contexts in which AI advances will be integrated.  Her research also aims to expand our understandings of the trustworthiness of AI used to inform consequential decisions and to advance AI to achieve these goals. Her research has addressed challenges in wide range of domains, including finance, health care, online labor markets, and smart electricity grid. At the university of Texas at Austin, Professor Saar-Tsechansky initiated and lead the Use-Inspired AI initiative.  Her work has been published in leading business, computer science, and health care venues and her research has been supported by government, industry, and private foundations, including the National Science Foundation, the Israeli Science Ministry, and the Kleberg foundation.

D. J. Wu is the Ernest Scheller Jr. Chair in Innovation, Entrepreneurship and Commercialization, Professor of IT Management, and Area Coordinator in IT Management at the Scheller College of Business, Georgia Institute of Technology. He graduated from the Computer Science and Technology Department of Tsinghua University and received his Ph.D. from the Wharton School, University of Pennsylvania. Dr. Wu's current research interests include economics of digital innovation and transformation, digital business model innovations, platform ecosystems, enterprise information technology, and artificial intelligence and machine learning. Dr. Wu's recent work has been published in academic journals, including Management Science, Information Systems Research, Manufacturing and Service Operations Management, and MIS Quarterly. Prof. Wu serves as a Department Editor of Information Systems, Management Science. He also serves as a Co-Editor for the Management Science Special Issue on Human-Algorithm Connection, as well as a Co-Editor for the Information Systems Research Special Issue on Analytical Creativity. He has served as President of INFORMS Information Systems Society (2019-2021).