Department Directory

Phoebe Sengers is a professor in the Departments of Information Science and Science & Technology Studies (STS), a graduate field member in computer science, associate faculty with the Department of Art, and a member of the Cornell Initiative for Digital Agriculture. Her primary research fields are human-computer interaction and science & technology studies. Her work integrates ethnographic and historical analysis of the social implications of technology with design methods to suggest alternative future possibilities. At Cornell, she leads the Culturally Embedded Computing research group, which explores rural, working-class and global South experiences of technologies, traces the emerging entanglements between people and data, and uses design to speculate about alternative pasts and futures.

Benjamin Shestakofsky is a sociologist and assistant professor in the Department of Information Science at Cornell University. His research centers on the relationship between work, technology, organizations, and political economy in the age of AI. Shestakofsky's book, Behind the Startup: How Venture Capital Shapes Work, Innovation, and Inequality (University of California Press), investigates the role of financiers in shaping our technological future. Benjamin's academic articles have been published in journals including Socio-Economic Review, Theory and Society, Big Data & Society, Work and Occupations, Socius, International Journal of Communication, and Teaching Sociology. 

David Shmoys obtained his Ph.D. in Computer Science from the University of California at Berkeley in 1984, and held postdoctoral positions at MSRI in Berkeley and Harvard University, and a faculty position at MIT before joining the Cornell faculty. He is Co-Chair of the Academic Planning Committee for Cornell Tech and Associate Director of the Institute of Computational Sustainability at Cornell University.

He is a Fellow of the ACM, INFORMS, and of SIAM, was an NSF Presidential Young Investigator, and has served on numerous editorial boards, including Mathematics of Operations Research (for which he is currently an Associate Editor), Operations Research, ORSA Journal on Computing, Mathematical Programming, and the SIAM Journals of both Computing and Discrete Mathematics, where for the latter he also served as Editor-in-Chief. He has been the advisor for 21 graduated Ph.D. students, and his former students are currently on the faculties of many leading universities and research labs, including MIT, Waterloo, Brown, Maryland, Georgetown, and D-Wave.

Shmoys' research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, and most recently, comptuational sustainability. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems; his recent book, co-authored with David Williamson), The Design of Approximation Algorithms, was awarded the 2013 Lanchester Prize by INFORMS.
 

Ranjit Singh is a Researcher at the AI on the Ground Initiative of Data & Society Research Institute. His research covers a range of topics from envisioning algorithmic impact assessments as a regulatory tool for AI-based systems to investigating the diverse ethics, politics, and everyday experiences of living with data and AI in/from the majority world. 
 

Slayton's research and teaching examine the relationships between and among risk, governance, and expertise, with a focus on international security and cooperation since World War II. Her first book, Arguments that Count: Physics, Computing, and Missile Defense, 1949-2012 (MIT Press, 2013), shows how the rise of a new field of expertise in computing reshaped public policies and perceptions about the risks of missile defense in the United States. In 2015, Arguments that Count won the Computer History Museum Prize.
 

Benjamin Soltoff is an associate teaching professor in information science. He teaches courses on data science, machine learning, research design, and data communication, and enjoys working with individuals from all backgrounds interested in applying computational methods to extract knowledge and inferences from a wide range of domains.

Andrea Stevenson Won directs the Virtual Embodiment Lab. The lab's research focuses on how mediated experiences change people’s perceptions, especially in immersive media. Research areas include the clinical applications of virtual reality, and how nonverbal behavior as rendered in virtual environments affects collaboration and teamwork. Andrea Stevenson Won completed her PhD in the Virtual Human Interaction Lab in the Department of Communication at Stanford University. She also holds an MS in Biomedical Visualization from the University of Illinois at Chicago.
 

Daniel Susser is an associate professor of information science. His research focuses on ethics, politics, and policy in computing. A philosopher by training, he studies governance problems raised by new and emerging data-driven technologies and the underlying conceptual and normative problems that make it difficult to understand and tackle them. He has broad interests in technology ethics and policy, philosophy of technology, and science and technology studies, especially critical questions about data, privacy, and the ethics of automation.

Éva Tardos is the Jacob Gould Schurman Professor of Computer Science. Her research interests include algorithms and algorithmic game theory, the subarea of theoretical computer science theory of designing and analyzing systems, and algorithms for selfish users. Her research focuses on algorithms and games on networks and simple auctions. She is most known for her work on quantifying the efficiency of selfish routing. Tardos received her Dipl.Math. and Ph.D. from Eötvös University, in Hungary. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences. She is the recipient of a number of fellowships and awards including the Packard Fellowship and the IEEE von Neumann Medal.

Angelique Taylor is the Andrew H. and Ann R. Tisch Assistant Professor at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science.

Aditya Vashistha is an assistant professor in information science at Cornell Bowers, where he advises students in information science and computer science. He leads the Cornell Global AI Initiative – an interdisciplinary, university-wide effort to integrate global perspectives into the design, development, evaluation, and governance of AI technologies.

Gili Vidan is an assistant professor in information science and a graduate field member in the Department of Science and Technology Studies and the Media Studies graduate minor field. Trained as a historian of computing, her work examines how shifts in abstract political notions such as trust and good governance are made concrete and transformed through the digital technologies we develop and use. In particular, Vidan focuses on the overlapping ways political, social, commercial, and epistemic values literally materialize in our payment and communication technologies, from automated check-processing to digital watermarking. Her book, “Technologies of Trust,” traces technical attempts to solve the problems of trust and authentication in the postwar U.S and has been accepted by the Infrastructures series at MIT Press.

Angelina Wang is an assistant professor of information science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. Her research is in the area of responsible AI.

Fei Wang is an associate professor at Department of Population Health Sciences, Weill Cornell Medicine, Cornell University. His major research interest is data mining, machine learning and their applications in health data science. He has published on the top venues of related areas such as ICML, KDD, NeurIPS, AAAI, JAMA Internal Medicine, Annals of Internal Medicine, etc. 
 

Matthew Wilkens is an associate professor of information science. He uses quantitative and computational methods to study large-scale developments in literary and cultural history. His work has focused in particular on literary text mining, geolocation extraction, genre detection, and the cross-pollination of critical and social scientific methods. He also seeks to develop and apply natural language processing and artificial intelligence methods to large-scale datasets in domains including medicine, public health, economics, and the law.

David Williamson was born in Madison, Wisconsin, but grew up in the suburbs of Honolulu, Hawaii. He received his Ph.D. in 1993 from the Massachusetts Institute of Technology.  In 1995 he joined IBM Research, and from 2000-2003 was the Senior Manager of the Computer Science Principles and Methodologies Department at IBM's Almaden Research Center.  In 2004, he joined Cornell University as a professor in the School of Operations Research and Information Engineering, and the Faculty of Computing and Information Science.

Dr. Williamson is well-known for his work on the topic of approximation algorithms, and is a coauthor of the book "The Design of Approximation Algorithms", published by Cambridge University Press. His Ph.D. dissertation on designing low-cost survivable networks was awarded several prizes, including the 1996 SIAM DiPrima Prize and the 1994 Tucker Prize from the Mathematical Programming Society. His work with Michel Goemans on the uses of semidefinite programming in approximation algorithms was awarded the 1999 SIAM Activity Group on Optimization prize, and the 2000 Fulkerson Prize from the Mathematical Programming Society and the American Mathematical Society. He is the editor-in-chief for the SIAM Journal on Discrete Mathematics, and has served as an associate editor on several other journals, including Mathematics of Operations Research and the SIAM Journal on Computing.
 

Qian Yang is an assistant professor of information science. She is a human-AI interaction designer and researcher. With her students, Yang designs novel AI applications and services that demonstrate new ways of bridging together frontier AI technologies, user needs, and societal betterment goals. For this work, she has received many academic and industry awards, including Schmidt Futures' Inaugural AI2050 Fellowship. She co-directs the Cornell Digital and AI Literacy Initiative with Natalie Bazarova. Yang is also a senior fellow at the Cornell Brooks School Tech Policy Institute.

Yian Yin is an assistant professor of information science. His research applies and develops novel computational tools to understand how individual, social, and environmental processes independently and jointly promote (or inhibit) scientific progress and innovation achievements. As a computational social scientist, he has also used science and innovation as a powerful lens to examine broader processes and outcomes in a wide range of complex social processes, from artistic and cultural productions to public policy, from media attention to market competition to human conflict. His research has been published in top general audience venues such as Science, Nature, and Nature Human Behaviour and featured in media outlets such as Forbes, Scientific American, The Atlantic, Harvard Business Review, and MIT Technology Review. In 2023, he was named to Forbes 30 under 30: Science list.

Yuan teaches and conducts research in the area of organizational communication and intercultural communication. Specially, she is interested in studying knowledge management through the development of social capital, and the adoption and usage of information and communication technology. Her recent research has focused on examining cultural differences in cognition and decision making, for instance, how American and Chinese people react differently to climate change initiatives and how they obtain health information differently. She has also explored how differences in communication styles between the East and the West influence the effectiveness of intercultural collaboration and expertise sharing.