Ziv Epstein is a PhD candidate at the MIT Media Lab. At the intersection of computational social science and design, his research explores ways to conceptualize and navigate attention online by developing interventions against misinformation, as well as exploring the role of algorithms such as newsfeed recommenders and generative AI in social interactions online.

He has published papers in venues such as the general interest journals Nature, PNAS and iScience, as well as top-tier computer science proceedings such as CHI and CSCW. His research has been featured in the New York Times, Scientific American, and Fast Company and his artwork has been featured in Ars Electronica, the MIT Museum, and Burning Man.

James is an MBA and MS Sustainability student at Stanford. Prior to joining the Lab, James co-founded Fingo – the first digital-only bank in Kenya, backed by Y Combinator. James started his career at McKinsey where he was an Engagement Manager focused on corporate business building and advanced analytics with McKinsey’s AI arm, Quantum Black.

James has been recognised as a Forbes 30 Under 30 and MIT Innovator Under 35. James studied Economics and Econometrics at the University of Warwick and the University of Hong Kong; he is particularly interested in understanding and measuring the economic potential of artificial intelligence in emerging markets.

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.

Alia Braley is a PhD candidate in the Department of Political Science at UC Berkeley, and a Visiting Scholar at the SNF Agora Institute at Johns Hopkins University. She was recently the Director of Research and Education at the Albert Einstein Institution, a consultant at the World Bank Development Data Group, and a visiting researcher at the MIT Media Lab Human Dynamics Group​.

She specializes in the study of democratic resilience in cases of political polarization and autocratic threat as well as civil resistance strategies in acute political conflict. The intervention featured in her recent publication, The Subversion Dilemma: Why Voters Who Cherish Democracy Participate in Democratic Backsliding, was the most effective at reducing polarization and strengthening democratic attitudes in Stanford University’s Strengthening Democracy Challenge mega-study.

She was previously a research assistant at the UC Berkeley Violence and Intervention Lab, at the United States Institute of Peace Nonviolent Action Program, and at the Harvard Kennedy School of Government Evidence for Policy Design Program.

She holds a Masters in Divinity from Harvard University, where her research focused on religion, ethics, and politics. During her time as a student, she served as an intern at the Center for Applied Nonviolent Action and Strategies in Belgrade, Serbia, and presented the findings of her master’s thesis on nonviolent action in Iraq and Syria at TEDx Salem.

José Ramón Enríquez is a postdoctoral fellow at the Stanford Digital Economy Lab (Stanford HAI) and the Golub Capital Social Impact Lab (Stanford GSB).

José Ramón obtained his Ph.D. in Political Economy and Government (PEG) from Harvard University in May 2023.
 
José Ramón studies the political economy of economic and political development with a focus on political accountability. Specifically, he has worked on understanding the role of information in improving political accountability, with a specific emphasis on misinformation, political polarization, and corruption; the causes and effects of criminal-political violence on democratic representation; and the effects of the lack of coordination across levels of government.

In his research, José Ramón relies significantly on quasi-experimental and experimental methods. He uses original fine-grained data, which he gathers and structures, originating from a variety of sources, such as administrative records, social media platforms, and online media. In some instances, he also uses formal analytic models to build theoretical frameworks and formulate original predictions to test empirically.
 
Before his doctoral studies, José Ramón obtained a B.A. in Economics and a B.A. in Political Science from Instituto Tecnológico Autónomo de México (ITAM) in Mexico City. He was raised in Durango, Mexico.

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.

He is interested in technology, innovation, productivity, and the workforce. His prior work experience includes program evaluation and R&D project management in federal government at the National Institute of Standards and Technology, and in public-private partnership programs for early-stage R&D, where he interacted with both start-ups and large corporate R&D centers.

Andrew received a BA in history and economics from the University of California, Berkeley, and a PhD in economics from Harvard University.

Ruyu Chen’s research interest lies at the intersection of economics of innovation, information systems, and business strategy. In her current research, she explores factors that influence technology adoption by firms, and how technology adoption influences follow-on innovations. She’s also interested in the power law distribution of wages within each occupation and how digitization might have affected it over time.

Before joining Stanford, Ruyu received her PhD in Managerial Economics from Cornell SC Johnson College of Business, and a graduate minor in Computer Science from Cornell University. She also holds a bachelor’s degree from Renmin University of China, and spent one exchange semester at the University of Copenhagen.

Gabriel Unger is an economics PhD student at Harvard. He jointly completed a JD at Yale Law School, and completed his undergraduate education at Harvard. ​ His main field of is macroeconomics. His research broadly attempts to understand how technological changes, like the IT Revolution, change our understanding of important macroeconomic questions, like the mechanics of productivity growth, the rise of industrial concentration, or the transformation of the business cycle.

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.