Raymond J. MooneyProfessor of Computer Science, The University of Texas at Austin
Director of the UT Artificial Intelligence Laboratory
B.S. in Computer Engineering, University of Illinois at Urbana/Champaign, 1983
M.S. in Computer Science, University of Illinois at Urbana/Champaign, 1985
Ph.D. in Computer Science, University of Illinois at Urbana/Champaign, 1988
|Research||Teaching||Personal||Contact||Note to Grad-Student Applicants|
For a complete list of areas and publications, see the UT Machine Learning Research Group home page. Also see my profile on Google Scholar.
See my complete vita (in PDF).
See a video of my talk on Deep Learning for Automating Software Documentation Maintenance.
See a video of my invited talk on "The Deep Learning Revolution: Progress, Promise and Profligate Promotion" at Computing in the 21st Century 2017.
See videos of my invited talks on grounded language learning at Cornell Tech (2017), NIPS 2015 Multimodal Machine Learning Workshop, and AAAI-2013.
Also see my research talks on Deep Natural Language Semantics and Generating Natural-Language Video Descriptions Using Text-Mined Knowledge, as well as Powerpoint presentations for some of my older talks.
In the fall of 1979, I went to the University of Illinois in Champaign-Urbana to obtain all of the degrees listed above. In December 1987, I completed my Ph.D. thesis under the direction of Prof. Gerald DeJong and then began as a faculty member here in the Department of Computer Science at the University of Texas at Austin where I am enjoying the beginning of the fourth decade of a hopefully long academic career.
See more information on my academic genealogy, which traces my professorial lineage back through Danish Linguists to German Theologians.
Recently, I have become particularly well known for a certain strongly stated comment, which can be embedded into the following vector: (0.62384789, 0.232328242, 0.2394182754, 0.9234583745, 0.9034527345, 0.2348534598743, 0.789045724387, 0.34750893274895, 0.23475809273485723, 0.23452374958, 0.094358923475823475, 0.908452352348905, 0.024375823785, 0.980459238409582345) (click to decode).