Her research examines how emerging information technologies, such as artificial intelligence and analytics, affect innovation, business strategy, and productivity. Specifically, her work follows three streams. In the first stream, she examines how data analytics and artificial intelligence affect firm innovation, business strategy, labor demand, and productivity for both large firms and startups. In her second stream, she studies how enterprise social media and online platforms affect work performance, career trajectories, entrepreneurship success, and the formation of new type of biases that arise from using technologies. In her third stream of research, Lynn leverages fine-grained nanodata available through online digital traces to predict economic indicators such as real estate trends, labor trends and product adoption.
Lynn has published articles in economics, management and computer science. Her work has been widely covered by media outlets, including, NPR, the Wall Street Journal, Businessweek, New York Times, Forbes, and The Economist. She has won numerous awards such as Early Career awards from INFORMS and AIS, best paper awards from Information System Research, AIS, ICIS, HICSS, CHITA, and Kauffman. She has also won the Dean’s teaching award.
Lynn received her undergraduate degrees from MIT (Finance and Computer Science), her master’s degree from MIT (Computer Science) and her Ph.D. from MIT Sloan School of Management (Management Science). Lynn has experiences working with a variety of firms in the technology industry (e.g. IBM, SAP, Google, Facebook etc), government agencies and think tanks (e.g the World Bank, the Russel Sage Foundation). She has also consulted and advised several startups. Prior to academia, she was a software engineer and a research scientist at MIT AI lab and IBM.
Victor is a research scientist at Opendoor Technologies and an affiliate at Boston University. Victor’s research focuses on computational economics and large-scale, micro-founded simulation methods, with applications in housing economics, public finance, macroeconomics, and the economics of AI. Victor received his Ph.D in Economics from Boston University, and an M.S. in Statistical and Economic Modeling from Duke University, where he also received a B.S. in Economics and a B.A. in Philosophy.
Victor’s ongoing projects with other Stanford DEL researchers focus on the intergenerational, macroeconomic, and fiscal policy implications of advanced AI and skill-biased technological change. Victor’s publications have been covered by major media outlets including CNBC, The Wall Street Journal, Bloomberg, The Hill, The New York Times, Barron’s, and Forbes. He has also been invited to present his research at the NBER, the National Tax Association, the Federal Reserve Bank of Atlanta, Boston, and Kansas City, the Gaidar Institute, the Congressional Budget Office, and the Hong Kong Monetary Authority.
