Innovation Scholar, Computer Science and Artificial Intelligence Laboratory; Research Scientist, MIT Initiative on the Digital Economy
Neil Thompson is an innovation scholar in MIT’s Computer Science and Artificial Intelligence Laboratory, a research scientist at the MIT Initiative on the Digital Economy, and an associate member of the Broad Institute. His research interests include computer performance and economic outcomes, tools and innovation, patenting and licensing, and executing on innovation and strategy. Previously, he was an assistant professor of innovation and strategy at the MIT Sloan School of Management and a visiting professor at Harvard University’s Laboratory for Innovation Science. Before joining academia, he worked at the Lawrence Livermore National Laboratories, Bain and Co., the United Nations, World Bank, and Canadian Parliament. He earned a BS in physics from Queen’s University, an MS in economics from the London School of Economics, and MS degrees in computer science and statistics, and a PhD in business and public policy, from the University of California, Berkeley.
- Thompson, N., Greenewald, K., Lee, K., Manso, G., (2023). The Computational Limits of Deep Learning. Computing within Limits (LIMITS).
- Thompson, N.C., Ge, S., Sherry, Y.M. (2021). Building the Algorithm Commons: Who discovered the algorithms that underpin computing in the modern enterprise? Global Strategy Journal.
- Leiserson, C. E., Thompson, N. C., Emer, J. S., Kuszmaul, B. C., Lampson, B. W., Sanchez, D., Schardl, T. B. (2020). There’s plenty of room at the Top: What will drive computer performance after Moore’s law? Science.
- Thompson, N. C., Bonnet, D., Ye, Y. (2020). Why innovation’s future isn’t (just) open. MIT Sloan Management Review, Vol. 61, Iss. 4, 55-60.
- August 7, 2020: MIT News, Shrinking deep learning’s carbon footprint.
- June 5, 2020: MIT News, If transistors can’t get smaller, then coders have to get smarter.
- Feb. 4, 2020: MIT Technology Review, We’re not prepared for the end of Moore’s Law.