Basil Halperin is a postdoc at the Stanford Digital Economy Lab. In fall 2025, he will join the University of Virginia as an assistant professor of economics.
His research focuses on topics in monetary economics, macroeconomic growth, and AI.
Basil received my PhD in economics from MIT in 2024. He previously worked as a data scientist at Uber and as a quant at AQR Capital Management. He completed my undergrad at the University of Chicago.
Andreas Haupt is a postdoctoral fellow at Stanford’s Institute for Human-Centered AI. He completed his PhD in Engineering-Economic Systems at the CS and AI Laboratory and the College of Computing, MIT in fall 2024. He studies how the design of economic institutions that try to learn what participants want is affected by large-scale estimation.
Sophia Kazinnik is a research scholar at the Stanford Digital Economy Lab, where she explores the intersection of artificial intelligence and economics. Prior to joining Stanford, Sophia worked as an economist and quantitative analyst at the Federal Reserve Bank of Richmond, where she was part of the Quantitative Supervision and Research group. While there, she contributed to supervisory projects targeting cyber and operational risks and developed NLP tools for supervisory purposes.
Broadly, her research focuses on applying Natural Language Processing (NLP) methods and Generative AI models to economic research, with particular emphasis on policy and central bank communication, financial stability, and cyber risk.
Sophia holds a bachelor’s degree in economics from Tel Aviv University in Israel and earned her doctoral degree in economics from the University of Houston.
Christina Langer’s research interests cover the fields of empirical labor economics and economics of education. Her research delves into the future of work, focusing on various aspects related to the supply and demand of skills and the evolving hiring practices of firms. She uses big, unstructured data like apprenticeship plan texts and online job postings, as well as administrative data, to estimate returns to skills and to investigate recent labor market trends like remote work or skills-based hiring.
J. Frank Li is an assistant professor at UBC Sauder School of Business, a digital fellow at Stanford University Institute for Human-Centered Artificial Intelligence Digital Economy Lab and a visiting fellow at NYU Stern Center for the Future of Management. His research stands at the nexus of the economics of information technology, focusing on AI and robots, technical skills and future of work, and organizational changes. He also studies how uncertainty shocks and competition affect firm strategic decisions such as investment, innovation, reorganization, and hiring.
Frank received his Ph.D. in business administration from Ross School of Business at the University of Michigan. He holds a BA in math and economics from the University of Michigan and an MA in economics from Duke University.
His current focus is developing new well-being metrics by using massive online choice experiments. He also studies the application of machine learning techniques to measure individual-level latent parameters. Lee
Before coming to Stanford, JJ worked at the MIT Initiative on the Digital Economy as a postdoctoral associate. He received his PhD in economics at Claremont Graduate University in 2019.
Lee has work experience as a business consultant, a financial analyst and a policy analyst in South Korea before his doctoral study. He also holds a bachelor degree in economics from Seoul National University and a master’s degree in public policy from KDI School of Public Policy and Management.
David’s research explores new and better ways to measure the modern and digital economy. He is particularly interested in advancing economic metrics and statistics on economic output and welfare.
Prior to joining the Stanford Digital Economy Lab, David worked as an economist at the OECD in Paris, and as a senior economist at the National Institute of Economic and Social Research (NIESR). As a research associate, he remains affiliated to the London-based Economic Statistics Centre of Excellence (ESCoE). David received his PhD from the London School of Economics.
Jiaxin Pei is currently a postdoctoral fellow at Stanford University working with Alex ‘Sandy’ Pentland, Diyi Yang and Erik Brynjolfsson. He is affiliated with the Digital Economy Lab and the NLP group. Jiaxin obtained my PhD from Blablablab, UMSI (School of Information, University of Michigan) advised by David Jurgens. He also worked with Jun Li at Ross School of Business. Before coming to Michigan, Jiaxin was an undergraduate student at the School of Computer Science, Wuhan University.
Jiaxin has very broad interests in human-centered AI, natural language processing, and computational social science. His overarching research ambition is to make human communications more effective and constructive by developing human-centered AI systems and analyzing large-scale human communication data.
His work has won a Best Student Paper Award at the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), a Best Demo Paper Award at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP), an Honorable Mention Award at the International Conference on Computational Social Science (IC2S2), and a Best Paper Award at the Workshop on Social Influence in Conversations. Jiaxin is the core developer of POTATO, a open-source data labeling system used by institutions around the world.
Dan Sholler studies what happens when organizations and industries rapidly adopt new technologies. He primarily uses qualitative methods (interviewing, observation, surveys, and archival research) to understand why some workers resist new technologies; how management practices influence technology outcomes; and what role organizational and governmental policies play in shaping the decisions organizations and workers make about using new technologies.
Dan’s research aims to inform technology and labor management strategies, technology governance frameworks, and theories of technological change, especially change that happens in the workplace. He presents the results of the research in academic and practitioner-oriented outlets.
Dan is currently working as a project scientist in the Technology Management Program at the University of California, Santa Barbara College of Engineering. He works with Dr. Matt Beane and studies the implementation, management, and labor implications of robotics and automation in the manufacturing and logistics industries.
He is also the principal investigator on a project documenting the histories of two open-source software languages via oral histories and archival research, supported by the Sloan Foundation. He has also worked as a postdoc with the rOpenSci Project at UC Berkeley’s Institute for Data Science; completed the PhD program and did research at the University of Texas at Austin School of Information; and studied at the University of Pennsylvania’s Department of History and Sociology of Science.
Philip Trammell is a postdoctoral fellow at the Stanford Digital Economy Lab, working with Erik Brynjolfsson and Chad Jones on how advanced AI will affect the speed and direction of economic growth.
