About

Haym Hirsh is a professor in the Department of Computer Science at Cornell University and serves as the Director of the MEng Program. His research has focused on the foundations and applications of machine learning, data mining, information retrieval, and artificial intelligence, especially targeting questions that integrally involve both people and computing. Recently, his interests have turned to crowdsourcing, human computation, and collective intelligence. He received his B.S. from the Mathematics and Computer Science Departments at the University of California, Los Angeles, and his M.S. and Ph.D. from the Computer Science Department at Stanford University. Prior to moving to Cornell in 2013 to serve as dean of the Faculty of Computing and Information Science, Hirsh spent 24 years on the computer science faculty at Rutgers University, and has held visiting positions at AT&T Labs, Bar-Ilan University, Carnegie Mellon University, MIT, and the University of Zurich. From 2006 to 2010, he served as director of the Division of Information and Intelligent Systems at the National Science Foundation. In 2022, he was elected a Fellow of the American Association for the Advancement of Science.

Research topics

• Political Science
• Artificial Intelligence
• Computer Science
• Machine Learning
• Medicine
• Management science
• Data science
• Knowledge management
• Engineering

Selected publications

• Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning

  Nature Machine Intelligence · 2020 · 62 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Machine Learning
  PublisherDOI

• Postmodern Prometheus <b>Life 3.0: Being Human in the Age of Artificial Intelligence</b> <i>Max Tegmark</i> Knopf, 2017. 360 pp.

  Science · 2017-08-03

  article1st authorCorresponding

  A physicist's guide explores the future of artificial intelligence

  PublisherDOI

• Interactive Consensus Agreement Games for Labeling Images

  Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · 2016-09-21 · 13 citations

  articleOpen access

  Scene understanding algorithms in computer vision are improving dramatically by training deep convolutional neural networks on millions of accurately annotated images. Collecting large-scale datasets for this kind of training is challenging, and the learning algorithms are only as good as the data they train on. Training annotations are often obtained by taking the majority label from independent crowdsourced workers using platforms such as Amazon Mechanical Turk. However, the accuracy of the resulting annotations can vary, with the hardest-to-annotate samples having prohibitively low accuracy. Our insight is that in cases where independent worker annotations are poor more accurate results can be obtained by having workers collaborate. This paper introduces consensus agreement games, a novel method for assigning annotations to images by the agreement of multiple consensuses of small cliques of workers. We demonstrate that this approach reduces error by 37.8% on two different datasets at a cost of $0.10 or $0.17 per annotation. The higher cost is justified because our method does not need to be run on the entire dataset. Ultimately, our method enables us to more accurately annotate images and build more challenging training datasets for learning algorithms.

  PublisherOA PDFDOI

• A U.S. Research Roadmap for Human Computation

  arXiv (Cornell University) · 2015-01-01 · 2 citations

  preprintOpen access

  The Web has made it possible to harness human cognition en masse to achieve new capabilities. Some of these successes are well known; for example Wikipedia has become the go-to place for basic information on all things; Duolingo engages millions of people in real-life translation of text, while simultaneously teaching them to speak foreign languages; and fold.it has enabled public-driven scientific discoveries by recasting complex biomedical challenges into popular online puzzle games. These and other early successes hint at the tremendous potential for future crowd-powered capabilities for the benefit of health, education, science, and society. In the process, a new field called Human Computation has emerged to better understand, replicate, and improve upon these successes through scientific research. Human Computation refers to the science that underlies online crowd-powered systems and was the topic of a recent visioning activity in which a representative cross-section of researchers, industry practitioners, visionaries, funding agency representatives, and policy makers came together to understand what makes crowd-powered systems successful. Teams of experts considered past, present, and future human computation systems to explore which kinds of crowd-powered systems have the greatest potential for societal impact and which kinds of research will best enable the efficient development of new crowd-powered systems to achieve this impact. This report summarize the products and findings of those activities as well as the unconventional process and activities employed by the workshop, which were informed by human computation research.

  PublisherOA PDFDOI

• Approximating the crowd

  Data Mining and Knowledge Discovery · 2014-06-13 · 18 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Antisocial computing

  interactions · 2014-10-30 · 7 citations

  article

  Social media has become globally ubiquitous, transforming how people are networked and mobilized. This forum explores research and applications of these new networked publics at individual, organizational, and societal levels. ---Shelly Farnham, Editor

  PublisherDOI

• Approximating the crowd

  DSpace@MIT (Massachusetts Institute of Technology) · 2014-06-01

  articleOpen accessSenior author
  Publisher

• Amplify scientific discovery with artificial intelligence

  Science · 2014-10-09 · 174 citations

  articleSenior author

  Many human activities are a bottleneck in progress

  PublisherDOI

• Human Computation in the Wild

  2013-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Reports on the 2012 AAAI Fall Symposium Series

  AI Magazine · 2013-03-01 · 19 citations

  articleOpen access

  The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS‐12‐01), Artificial Intelligence of Humor (FS‐12‐02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS‐12‐03), Human Control of Bio‐Inspired Swarms (FS‐12‐04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS‐12‐05), Machine Aggregation of Human Judgment (FS‐12‐06), Robots Learning Interactively from Human Teachers (FS‐12‐07), and Social Networks and Social Contagion (FS‐12‐08). The highlights of each symposium are presented in this report.

  PublisherOA PDFDOI

Frequent coauthors

• William W. Cohen

  The Ohio State University

  13 shared

• Sofus A. Macskassy

  Torch Technologies (United States)

  12 shared

• Brian D. Davison

  10 shared

• Ulrich Junker

  9 shared

• Sarah Zelikovitz

  8 shared

• Arunava Banerjee

  8 shared

• Gary M. Weiss

  8 shared

• Thomas Ellman

  Vassar College

  7 shared

Labs

• Haym Hirsh's LabPI

Education

• B.S., Mathematics and Computer Science

  University of California, Los Angeles

• M.S.

  University of California, Los Angeles

• Ph.D.

  University of California, Los Angeles

Awards & honors

• Fellow of the American Association for the Advancement of Sc…