Shiri Azenkot is an associate professor of information science at Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology.

Claire Cardie is the John C. Ford Professor of Engineering in the Departments of Computer Science and Information Science. She was the founding chair of the Department of Information Science and led the development of its academic programs. Cardie works in the area of Natural Language Processing (NLP) on topics ranging from information extraction, text summarization, and noun phrase coreference resolution, to the automatic analysis of opinions, argumentation, and deception in text. 

Abe Davis is an assistant professor of computer science who specializes in computer graphics, computer vision, and human-computer interaction (HCI). His group brings these areas together to work on new problems at the intersection of different disciplines. Davis completed postdoctoral research at Stanford University and Cornell Tech, and his M.A. and Ph.D. at the Massachusetts Institute of Technology, studying electrical engineering and computer science. He earned his undergraduate degree in computer science at Stanford.

Nicola Dell is an associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and the Cornell Ann S. Bowers College of Computing and Information Science. She advises Ph.D. and M.S. students in the fields of information science and computer science.

François Guimbretière is a professor of information science. Guimbretière's research explores how hardware/software co-design can deliver novel interactions in a broad set of application domains. Blending system building and design, Guimbretière and his students strive to turn future visions of seamless interactions into today’s reality. His prior work explored pen-based interfaces on paper and screen. He currently focuses on novel fabrication techniques, such as interactive printing and yarn-based fabrication. He is also interested in the intersection of HRI and sustainability, including the design of immersive and engaging remote collaboration interfaces that limit our reliance on physical travel. For more information, visit Guimbretière's ACM webpage for the most up-to-date description of his work.

Thorsten Joachims is the Jacob Gould Schurman Professor of computer science and information science at Cornell University. He is also the Vice Provost for Artificial Intelligence Strategy at Cornell, where he leads the Cornell AI Initiative and the AI Radical Collaboration. He previously served as the interim dean for Cornell Bowers, as associate dean for research for Cornell Bowers, and as chair of the Department of Information Science. He has also served as program chair of the ICML, KDD, and RecSys conferences, and he is a member of the IMLS Board and the SIGKDD Executive Committee. Thorsten Joachims joined Cornell in 2001 after finishing his Ph. D. as a student of Prof. Morik at the University of Dortmund, from where he also received a Diplom in computer science in 1997.

David Mimno is a professor and chair of the Department of Information Science. He holds a Ph.D. from UMass Amherst and was previously the head programmer at the Perseus Project at Tufts and a researcher at Princeton University. His work has been supported by the Sloan foundation, the National Endowment for the Humanities, and the National Science Foundation.

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.

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.

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.

Cheng Zhang is an associate professor of information science. His research examines how to solicit information on and around the human body to address real-world challenges in various application areas, such as interaction, health sensing, and activity recognition. He usually builds novel sensing systems from the bottom to the top, including understanding the physical phenomenon, building hardware prototypes, designing form factors, processing data, and designing algorithms (machine learning or physics-based modeling).