Jon Kleinberg
Cornell University · Computer Science
Active 1956–2025
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About
Jon Kleinberg is the Tisch University Professor at Cornell University, affiliated with both the Department of Computer Science and the Department of Information Science. His research centers on algorithms and networks, particularly their roles in large-scale social and information systems and the broader societal implications of these technologies. Kleinberg's work has received significant recognition and support, including prestigious awards such as the NSF Career Award, ONR Young Investigator Award, MacArthur Foundation Fellowship, Packard Foundation Fellowship, Simons Investigator Award, Sloan Foundation Fellowship, and Vannevar Bush Faculty Fellowship. His research has also been funded by major technology companies and foundations including Facebook, Google, Yahoo, the MacArthur and Simons Foundations, as well as government agencies like AFOSR, ARO, and NSF. He is a distinguished member of several elite organizations, including the National Academy of Sciences, the National Academy of Engineering, the American Academy of Arts and Sciences, and the American Philosophical Society. Kleinberg has contributed to education through teaching and authoring books, including "Networks, Crowds, and Markets: Reasoning About a Highly Connected World" and "Algorithm Design," which are used in undergraduate and graduate courses. Since Spring 2021, he has co-taught an introductory course on the ethical, societal, and policy implications of computing and information. His academic…
Research topics
- Computer Science
- Artificial Intelligence
- Data science
- Machine Learning
- Natural Language Processing
- Data Mining
- Risk analysis (engineering)
- Management science
- Mathematics
- Psychology
Selected publications
Mitigating bias in algorithmic hiring
2020 · 593 citations
There has been rapidly growing interest in the use of algorithms in hiring, especially as a means to address or mitigate bias. Yet, to date, little is known about how these methods are used in practice. How are algorithmic assessments built, validated, and examined for bias? In this work, we document and analyze the claims and practices of companies offering algorithms for employment assessment. In particular, we identify vendors of algorithmic pre-employment assessments (i.e., algorithms to scr…
Integrating explanation and prediction in computational social science
Nature · 2021 · 357 citations
Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 52 citations
We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted…
Measuring the Completeness of Economic Models
Journal of Political Economy · 2021 · 51 citations
Economic models are evaluated by testing the correctness of their predictions. We suggest an additional measure, “completeness”: the fraction of the predictable variation in the data that the model captures. We calculate the completeness of prominent models in three problems from experimental economics: assigning certainty equivalents to lotteries, predicting initial play in games, and predicting human generation of random sequences. The completeness measure reveals new insights about these mode…
Using Large Language Models to Promote Health Equity
NEJM AI · 2025-01-13 · 16 citations
article
Recent grants
NSF · $2.6M · 2009–2015
III: Small: Collaborative Research: Mining Information Propagation on the Web
NSF · $80k · 2010–2013
NSF · $1.0M · 2017–2020
Frequent coauthors
- 77 shared
Sendhil Mullainathan
University of Chicago
- 66 shared
David Hutchison
Lancaster University
- 66 shared
Doug Tygar
University of California, Berkeley
- 66 shared
Friedemann Mattern
- 66 shared
Bernhard Steffen
TU Dortmund University
- 66 shared
Gerhard Weikum
- 65 shared
Moni Naor
- 65 shared
Oscar Nierstrasz
Labs
Research on algorithms and networks, their roles in large-scale social and information systems, and broader societal implications.
Education
- 1996
Ph.D., Computer Science
Cornell University
- 1991
B.S., Computer Science
Princeton University
Awards & honors
- NSF Career Award
- ONR Young Investigator Award
- MacArthur Foundation Fellowship
- Packard Foundation Fellowship
- Sloan Foundation Fellowship
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