Arvind Karunakaran is an Assistant Professor at Stanford University in the Department of Management Science and Engineering. His research draws on organizational theory and sociology of work and occupations/professions to examine authority and accountability in the workplace, especially in the context of technological change. He received his Ph.D. from the MIT Sloan School of Management.
His current research focuses on understanding (a) tensions among the overlapping strands of authority in organizations (e.g., line authority, staff authority, professional authority), and how it shapes consequential outcomes such as exclusion/inclusion in the workplace, perceptions of powerlessness, workplace harassment, employee voice and change implementation; (b) mechanisms for enforcing accountability during periods of organizational and technological changes (e.g., introduction of algorithmic evaluation tools, social media platforms, diversity & sustainability initiatives).
He specializes in ethnographic and field-based methods (e.g., participant observations, interviews), examining the empirical and theoretical puzzles discovered during fieldwork that existing research cannot fully explain. He complements these methods with comparative-historical analysis of primary archival data and quantitative/computational analysis of large-corpus of textual data.
His research has been published in journals such as Administrative Science Quarterly, Academy of Management Journal, Organization Science, and Research Policy, and recognized with awards from professional associations, including the American Sociological Association (ASA), Academy of Management (AOM), Industry Studies Association (ISA), Institute for Operations Research and the Management Sciences (INFORMS), and Labor and Employment Relations Association (LERA).
Her research focuses on the intersection of technology strategy and organizational learning by using machine learning, statistical analysis, and mixed methods. She is an expert on innovation, competition, and entrepreneurship in large firms. Her current research centers on responsible and inclusive innovation initiatives.
Brad Larsen joined the Department of Economics at Stanford University in 2014. Prior to this, he obtained a BA in Economics and BS in Mathematics from Brigham Young University and a PhD in Economics from MIT, and spent one year as postdoctoral researcher at eBay Research. He is also a faculty research fellow at the National Bureau of Economic Research and a faculty fellow at the Stanford Institute for Economic Policy Research. He is currently a W. Glenn Campbell and Rita Ricardo-Campbell National Fellow at the Hoover Institution.
His primary area of research is Industrial Organization, with specific emphasis on bargaining and occupational licensing. His recent research projects study large datasets of alternating-offer-negotiation settings to analyze behavioral patterns and efficiency in bargaining. He also studies the effects of occupational licensing regulations on market outcomes such as prices, competition, and the distribution of service quality. Other recent projects study auctions, consumer search, digital copyright law and grey-market activity, changes in US wage inequality due to increased import competition with China, the effects of laws legitimizing arbitrage (parallel importation) across international markets, and applied econometric methods.
Danial Lashkari holds the chair of White Family Assistant Professor of economics and international studies at Boston College, and is currently based at the Stanford Institute for Economic Policy Research (SIEPR) as a visiting assistant professor (2021-2022).
He completed his PhD degree at the Harvard economics department in 2017 and was a Cowles Foundation postdoctoral associate at the Yale University department of economics (2017-2018). His research interests are at the intersection of economic growth, innovation, and international trade.
Prior to Harvard, he obtained a PhD degree at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he worked on a number of applications of machine learning techniques in neuroimaging and cognitive neuroscience. He received MSc and BSc degrees from the University of Tehran, Iran.
She served as the Director of Stanford’s AI Lab from 2013 to 2018.
During her sabbatical from Stanford from January 2017 to September 2018, Fei-Fei was vice president at Google and served as chief scientist of AI/ML at Google Cloud.
Fei-Fei’s current research interests include cognitively inspired AI, machine learning, deep learning, computer vision, and AI+healthcare—particularly ambient intelligent systems for healthcare delivery. Her past research focused on cognitive and computational neuroscience.
She is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in deep learning and AI. She is a national leading voice for advocating diversity in STEM and AI, and is co-founder and chairperson of the national non-profit AI4ALL, which aims to increase inclusion and diversity in AI education.
Fei-Fei has published more than 200 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS, ECCV, ICRA, IROS, RSS, IJCV, IEEE-PAMI, New England Journal of Medicine, and Nature Digital Medicine.
Fei-Fei received her B.A. degree in physics from Princeton in 1999 with high honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006).
HAI co-director Fei-Fei Li talks with Rhode Island Governor Gina Raimondo at the AI & The Future of Work Conference, October 2020.
Percy Liang is an associate professor of computer science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
He previously held a faculty position in the department of Political Science at Emory University. His research focuses on political marketplaces, including the market for political news, the political media consulting industry, and the allocation of grant funding by legislatures. Gregory earned his Ph.D. in political economics at Stanford GSB and an SB in economics from the Massachusetts Institute of Technology.
In 2020, Paul was named a Distinguished Fellow of the American Economic Association. According to the Distinguished Fellow citation, he “is the world’s leading auction designer, having helped design many of the auctions for radio spectrum conducted around the world in the last thirty years, including those conducted by the U.S. Federal Communications Commission (ranging from the original simultaneous multiple round auction with activity rules, to the recent incentive auction for repurposing broadcast spectrum for modern uses). His applied work in auction design and consulting has established new ways for economists to interact with the wider world. He is also a theorist of extraordinary breadth, who has provided (and still continues to provide) foundational insights not only into the theory of auctions (including his 1982 paper with Weber), but across the range of modern microeconomic theory.”
Continuing, the citation notes that “His work has been widely recognized. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. He has received major prizes, including the 2008 Nemmers Prize, the 2012 BBVA Foundation Frontiers of Knowledge Award, the 2014 Golden Goose Award (with McAfee and Wilson), the 2018 CME Group-MSRI Prize in Innovative Quantitative Applications, and the 2018 John J. Carty Award for the Advancement of Science (with Kreps and Wilson). He is the dissertation advisor of many successful economists.”
His work examines the use of broad-based stock option plans and how firms use non-cash benefits and respond to limits on their ability to displace workers. He also explores how labor market conditions affect their entire careers when MBAs and PhD economists leave school.
Paul’s current projects include studies of the gig economy and a study of how people’s backgrounds determine their decision to become an entrepreneur, as well as the success of ventures that they pursue.
Diyi Yang is an assistant professor in the Computer Science Department at Stanford, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). She is interested in socially aware natural language processing. Her research goal is to better understand human communication in social context and build socially aware language technologies to support human-human and human-computer interaction.
