AI that Benefits Organizations, Businesses, People, and Society
My research advances AI with the motivation to benefit people and organizations and to promote well-being overall.
Much of my work focuses on AI advances to improve business and organizational goals through improved decision-making that benefit a variety of critical practical goals. This agenda includes new organizationally- or business-aware AI frameworks that aim to enhance organizational value from AI, human-AI collaboration towards low- or high-stakes decision-making involving both predictions and course of action choices, trustworthy AI, and AI systems that cost-effectively learn from or complement imperfect or biased humans.
AI Grounded in Organizational and Societal Contexts
My research pioneered and established the need for organizationally-grounded AI methods to improve decision-making in business and organizations. Our work has shown how general-purpose AI methods applied to improve decisions in organizations might not only fail to create organizational value but can cause harm. We developed frameworks to produce organizationally-grounded AI and have demonstrated their value across contexts, including high-risk decision-making, such as in health care, fraud/non-compliance, and marketing campaigns.
My research brings to bear human, organizational, and societal goals, opportunities, and constraints to catalyze AI systems’ positive impact in the world.
My research has been informed and inspired by active collaborations with organizations, businesses, and domain experts. Over the years, I addressed challenges in a wide variety of domains, including health care, the future of work, renewable energy, audit, and finance.
My work was supported by government and industry. I lead the University of Texas at Austin’s Translational AI initiative and am an academic board member of the university’s Machine Learning Lab.
I serve as Senior Editor at MISQ and at the INFORMS Journal of Data Science, and as an Associate Editor at Management Science. I am also an Editorial board member of the Machine Learning journal.
Ph.D students and postdocs:
If you are interested to work with me, please reach out to me at email@example.com
Below are descriptions of some of my works. See more details in my CV and Google Scholar profile.
In 2014 I co-founded Sweetch, a platform for large-scale prediction, prevention and outcome improvement of chronic diseases.
Upcoming & Recent Talks
New York University, Center for Urban Science and Progress, Seminar, May 2024.
University of Florida, The Information Systems and Operations Management Workshop. 2024
University of Tulane, Seminar, Spring 2024
Harvard Business School, Seminar, November 2023
University of Pittsburgh, Seminar 2023
Keynote: INFORMS Workshop on Data Mining and Decision Analytics, October 2023.
Keynote, National University of Singapore, July 2023.
Keynote, Workshop on Frontiers of AI in Business and Society, University of Illinois Urbana-Champaign, 2023.