now L. Ron
Hubbard Way, three
miles from the Hollywood
Sign, Haym Hirsh
the first quarter-century
of his life in California, receiving his BS degree in 1983 from
the Mathematics and Computer Science departments at UCLA and his MS in 1985 and PhD in 1989
from the Computer Science
Department at Stanford
with the weather, he moved to Pittsburgh when he found a way to spend his final
year at Stanford at Carnegie Mellon University and the University of Pittsburgh. The following year he achieved his life-long dream of living in New Jersey by joining the faculty of the Computer Science Department at
Rutgers University. As part of his never-ending spiritual quest, he has also spent
as visiting faculty
at Carnegie Mellon University (in
their School of Computer
(in various combinations
of the Artificial Intelligence
Laboratory, Laboratory for
Computer Science, the Sloan School
of Management, and the Center for
Collective Intelligence), and NYU
(in the Information Systems
Department at the Stern School
of the Rutgers
University Computer Science
Department, and from 2006
of the Division of
Information and Intelligent Systems at
the National Science
Foundation. However, still unsatisfied
to reach new levels
he subsequently moved
to Cornell University
of Computing and Information
Science. More recently, he returned
his secular life
as a Professor in
of Computer Science
and Information Science
When he is not teaching
conducting research, or designing mechanical puzzles
he writes silly biographies with lots of gratuitous pointers
(You can also see a more stodgy
short bio or a long-winded
For most of my career my research focused on foundations and
applications of machine learning, data mining, information retrieval,
and artificial intelligence, especially targeting questions that
integrally involve both people and computing. However, in recent years
these interests have turned to complementary questions in
crowdsourcing, human computation, and social computing.
At the undergraduate and master's level I most commonly teach a senior-level
introductory course in Artificial Intelligence, "Foundations of
Artificial Intelligence", and its associated "Practicum in Artificial
Intelligence". At the graduate level I typically teach "Crowdsourcing and Human Computation", a version of which I first
taught in 2013, making it one of the first courses ever taught on this
topic (and it may now be the largest, often constrained by classroom
capacity, peaking at 120 students in one recent offering).
Although I first became a professor because I thought a career
doing research and teaching would be very cool, at some point I
resigned myself to the fact that I seem to be appreciated in various
service roles as well. Some examples:
- Professional: In 1994 I served as co-chair
(with William Cohen) of
the International Conference on Machine Learning (ICML), colocating it
for the first time with the Conference on Computational Learning
Theory (COLT) (a practice that continues on and off to this day). At various
times I have also been an elected Councilor of the Association for the
Advancement of Artificial Intelligence (AAAI), an action editor of both
the Journal of Machine Learning Research and Machine
Learning Journal, and Director of the Division of Information and
Intelligent Systems at the U.S. National Science Foundation (and
helped start such programs
- Academic: I spent much of my career at Rutgers University,
where at various times I served as Chair of the Department of Computer
Science (twice), Chair of the New Brunswick Faculty Council, and
member of the Executive Committees of both the Rutgers Center for
Cognitive Science and the Center for Discrete Mathematics and
Theoretical Computer Science (DIMACS). Later at Cornell I served as
Dean of the Faculty of Computing and Information Science, and am now
serving as faculty director of the CS Master of Engineering, chair of
the CS Diversity, Equity, and Inclusion Commmittee, and member of the
advisory committees for Cornell's Cognitive Science program and the
Cornell Center for Data Science for Enterprise and Society, as well as
serving on the University's Research Advisory Commmittee.
- Popular papers:
- Y Gil, M Greaves, J Hendler, H Hirsh (2014):
Amplify scientific discovery with artificial intelligence.
Science 346 (6206), 171-172.
- Seyda Ertekin, Cynthia Rudin, and Haym Hirsh (2014):
Approximating the crowd.
- A Borgida, TJ Walsh, H Hirsh (2005):
Towards Measuring Similarity in Description Logics.
- Chumki Basu, Haym Hirsh, and William W. Cohen (1998):
Recommendation as Classification:
Using Social and Content-Based Information in Recommendation.
- Brian D. Davison and Haym Hirsh (1998):
Predicting Sequences of User Actions.
Predicting the Future: AI Approaches to Time Series Analysis.
- Gary M. Weiss and Haym Hirsh (1998):
Learning to Predict Rare Events in Event Sequences.
- Ronen Feldman and Haym Hirsh (1996):
in Text in the Presence of Background Knowledge.
- William W. Cohen, Alex Borgida, and Haym Hirsh (1992):
Computing Least Common Subsumers in Description Logics.
- Haym Hirsh (1992):
Polynomial-Time Learning with Version Spaces.
Other Personal Links
A version of this page has been on the Internet since 1994.
No animals were harmed in the creation of this web page.