Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.
Earn your MBA and SM in engineering with this transformative two-year program.
A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.
A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.
Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.
A doctoral program that produces outstanding scholars who are leading in their fields of research.
Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.
Apply now and work for two to five years. We'll save you a seat in our MBA class when you're ready to come back to campus for your degree.
The 20-month program teaches the science of management to mid-career leaders who want to move from success to significance.
A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.
A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.
Non-degree programs for senior executives and high-potential managers.
A non-degree, customizable program for mid-career professionals.
Faculty
Kimberly Crumpton
Danielle Li is the David Sarnoff Professor of Management of Technology and a Professor at the MIT Sloan School of Management, as well as a Faculty Research Fellow at the National Bureau of Economic Research. Her research interests are in economics of innovation and labor economics, with a focus on how organizations evaluate ideas, projects, and people.
Danielle's work has been published in leading academic journals across a range of fields, including the Quarterly Journal of Economics, Science, and Management Science. In addition, her work has been regularly featured in media outlets such as the Economist, New York Times, and Wall Street Journal.
She has previously taught at the Harvard Business School and the Kellogg School of Management. She holds an AB in mathematics and the history of science from Harvard College and a PhD in economics from MIT.
Danielle Li has won the 2019 American Economic Journal Best Paper Award for AEJ: Applied Economics. Li’s winning paper, “Expertise versus Bias in Evaluation: Evidence from the NIH” (AEJ: Applied Economics, 9 (2), April 2017, pp. 60-92), was also featured in the 2017 American Economic Association Research Highlight, “How Can We Make Sure the Best Medical Science Gets Funded?”
Featured Publication
"Discretion in Hiring."Hoffman, Mitchell, Lisa Kahn, and Danielle Li. Quarterly Journal of Economics Vol. 133, No. 2 (2018): 765-800. Download paper.
Featured Publication
"Public R&D Investments and Private Sector Patenting: Evidence from NIH Funding Rules."Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. Review of Economic Studies Vol. 86, No. 1 (2019): 117-152.
Li, Danielle, Lindsey Raymond, and Peter Bergman. Review of Economic Studies. Forthcoming. Accepted Manuscript.
Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond. The Quarterly Journal of Economics Vol. 140, No. 2 (2025): 889-942. arXiv Preprint.
Benson, Alan M., Danielle Li, and Kelly Shue. Academy of Management Proceedings Vol. 2023, No. 1 (2023).
Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond, MIT Sloan Working Paper 6848-23. Cambridge, MA: MIT Sloan School of Management, April 2023. NBER Working Paper 31161.
Artificial intelligence can be useful in the workplace, but humans have to first define what success looks like, according to MIT Sloan’s Danielle Li.
Danielle Li studies how AI impacts work and the workplace. “I’m more interested in how businesses put these tools to use, how they impact the productivity of workers, the type of work they are able to do, and what their careers might look like in an AI-intensive world,” she says.
Professor Danielle Li said there were scenarios in which A.I. could undermine higher-skilled workers more than entry-level workers. For instance, you may no longer have to be an engineer to code, or a lawyer to write a legal brief. "That state of the world is not good for experienced workers," she said. "You're being paid for the rarity of your skill, and what happens is that A.I. allows the skill to live outside of people."
Managers need to work out how "to incentivise, to compensate, to excite people that the nugget of an idea that lives inside you"
Author Diane Hamilton took a class at MIT Sloan where Thomas Malone and Danielle Li shared research that "goes beyond the usual AI hype."
AI could narrow gaps by transferring best practices from talented to less talented employees.
Over six weeks, you’ll explore the technical and strategic considerations for robust, beneficial, and responsible AI deployment. You’ll examine the various stages of a proprietary ML Deployment Framework and unlock new opportunities by investigating the key challenges and their related impact. Guided by leading experts and MIT academics, you’ll build a toolkit for addressing these challenges within your own organization and context.
This online program from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) challenges common misconceptions surrounding AI and will equip and encourage you to embrace AI as part of a transformative toolkit. With a focus on the organizational and managerial implications of these technologies, rather than on their technical aspects, you’ll leave this course armed with the knowledge and confidence you need to pioneer its successful integration in business.
