Dan Roth

Dan Roth is the Eduardo D. Glandt Distinguished Professor of Computer and Information Science at the University of Pennsylvania.[4]

Dan Roth
Dan Roth, 2011
Born
Haifa, Israel
Alma materHarvard University
Known forJoint Learning and Inference: ILP formulations of NLP tasks...,[1] Machine Learning for NLP, Probabilistic Reasoning
AwardsACM Fellow; IJCAI John McCarthy Award [2] [3]
Scientific career
FieldsComputer Science, Machine Learning, Natural Language Processing, Automated reasoning, Information Extraction.
InstitutionsUniversity of Illinois at Urbana-Champaign, University of Pennsylvania
Doctoral advisorLeslie Valiant
Websitewww.cis.upenn.edu/~danroth

Biography

Roth got his B.A summa cum laude in Mathematics from the Technion, Israel and his Ph.D in Computer Science from Harvard University in 1995.[5] He taught at the University of Illinois at Urbana-Champaign from 1998 to 2017 before moving to the University of Pennsylvania.[6]

Professional career

Roth is a Fellow of the American Association for the Advancement of Science (AAAS),[7] the Association of Computing Machinery (ACM),[8] the Association for the Advancement of Artificial Intelligence (AAAI),[9] and the Association of Computational Linguistics (ACL).[10]

Roth’s research[11] focuses on the computational foundations of intelligent behavior. He develops theories and systems pertaining to intelligent behavior using a unified methodology, at the heart of which is the idea that learning has a central role in intelligence. His work centers around the study of machine learning and inference methods to facilitate natural language understanding. In doing that he has pursued several interrelated lines of work that span multiple aspects of this problem - from fundamental questions in learning and inference and how they interact,[12] to the study of a range of natural language processing (NLP) problems and developing advanced machine learning based tools for natural language applications.[13]

Roth has made seminal contribution to the fusion of Learning and Reasoning [14], Machine Learning with weak, incidental supervision [15], and to machine learning and inference approaches to natural language understanding. Roth has worked on probabilistic reasoning (including its complexity[16] and probabilistic lifted inference [17]), Constrained Conditional Models (ILP formulations of NLP problems) and constraints-driven learning,[18][19] part-based (constellation) methods in object recognition,[20] response based Learning,[21] He has developed NLP and Information extraction tools that are being used broadly by researchers and commercially, including NER, coreference resolution, wikification, SRL, and ESL text correction.[13]

Roth is the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR).[22]

gollark: hahahahahahno
gollark: > "stop being a teenager"Precisely.
gollark: Destroy it.
gollark: Am I to read "we've been trying to keep things more PG than in the past" as a request to CEASE this conversement?
gollark: I mean, we really only just decided to enforce R4.

References
  1. Constrained Conditional Models
  4. "Penn Engineering - Research Directory Profile". www.seas.upenn.edu. Retrieved 2017-08-29.
  5. Dan Roth's Webpage
  6. "Dan Roth - Main Page". l2r.cs.uiuc.edu. Retrieved 2017-08-29.
  7. AAAS List of Fellows Archived July 27, 2014, at the Wayback Machine
  8. ACM Fellows
  9. AAAI List of Fellows
  10. ACL Fellows
  11. Dan Roth's Publication Page
  12. R. Khardon and D. Roth,Learning to Reason, Journal of the ACM (1997)
  13. Cognitive Computation Group Demo Page
  14. D. Roth,Learning to Reason: The Approach, (1996)
  15. D. Roth,Incidental Supervision, AAAI (2017)
  16. D. Roth, D. Roth, On the hardness of approximate reasoning, Artificial Intelligence (1996)
  17. R. de Salvo Braz, E. Amir and D. Roth, Lifted First-Order Probabilistic Inference, IJCAI, 2005.
  18. M. Chang and L. Ratinov and D. Roth, Structured Learning with Constrained Conditional Models, Machine Learning (2012)
  19. D. Roth and W. Yih, A Linear Programming Formulation for Global Inference in Natural Language Tasks, CoNLL (2004)
  20. S. Agarwal and A. Awan and D. Roth, Learning to Detect Objects in Images via a Sparse, Part-Based Representation, IEEE Transactions on PAMI (2004)
  21. J. Clarke and D. Goldwasser and M. Chang and D. Roth, Driving Semantic Parsing from the World's Response, CoNLL (2010)
  22. JAIR Masthead
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