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Created page with "'''Home * People * Jaime Carbonell''' FILE:JCarbonell.jpg|border|right|thumb| Jaime Carbonell <ref>[https://en.wikipedia.org/wiki/Jaime_Carbonell Jaime C..."
'''[[Main Page|Home]] * [[People]] * Jaime Carbonell'''
[[FILE:JCarbonell.jpg|border|right|thumb| Jaime Carbonell <ref>[https://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell from Wikipedia]</ref> ]]
'''Jaime Guillermo Carbonell''',<br/>
an American computer scientist and [[Artificial Intelligence|AI]] researcher with focus on [[Learning|machine learning]] and [https://en.wikipedia.org/wiki/Machine_translation machine translation].
He holds a Ph.D. in computer science in 1979 from [https://en.wikipedia.org/wiki/Yale_University Yale University] and is [[Allen Newell]] professor at [[Carnegie Mellon University]], co-founder and chairman, of ''Carnegie Speech Incorporated'' and ''Wisdom Technologies Corporation'' <ref>[https://franz.com/success/customer_apps/optimization/wisdom.lhtml Franz Inc Customer Applications: Wisdom Technologies, Inc.]</ref>.
Jaime Carbonell invented multiple well-known algorithms and methods, including [https://en.wikipedia.org/wiki/Proactivity proactive] [[Learning|machine learning]] for multi-source cost-sensitive [https://en.wikipedia.org/wiki/Active_learning active learning],
linked [https://en.wikipedia.org/wiki/Conditional_random_field conditional random fields] (L-SCRF) for predicting tertiary and quaternary [https://en.wikipedia.org/wiki/Protein_folding protein folds],
[https://en.wikipedia.org/wiki/Automatic_summarization maximal marginal relevance] (MMR) for information novelty, retrieval and summarization, topic-conditioned modeling for [https://en.wikipedia.org/wiki/Novelty_detection novelty detection],
symmetric optimal [https://en.wikipedia.org/wiki/Phrase phrasal] alignment method for trainable example-based and statistical machine translation, series-anomaly modeling for [https://en.wikipedia.org/wiki/Predictive_analytics#Fraud_detection financial fraud detection]
and [https://en.wikipedia.org/wiki/Clinical_surveillance syndromic surveillance], knowledge-based[https://en.wikipedia.org/wiki/Interlingual_machine_translation interlingual machine translation],
transformational analogy for [https://en.wikipedia.org/wiki/Case-based_reasoning case-based reasoning], [https://en.wikipedia.org/wiki/Derivation_%28linguistics%29 derivational] analogy for reconstructive [https://en.wikipedia.org/wiki/Theory_of_justification justification-based] reasoning,
robust case-frame parsing, seeded[https://en.wikipedia.org/wiki/Version_space version-space learning], and developed improvements to several other machine learning algorithms.
Current research foci include robust statistical learning and mapping [https://en.wikipedia.org/wiki/Peptide_sequence protein sequences] to 3D structure and inferring functional properties, automated transfer-rule learning for machine translation, enriched active transfer learning context-based machine translation, and machine translation for very rare languages <ref>[http://www.cs.cmu.edu/~jgc/CVs/Carbonell%20CV%202013-March.pdf Jaime G. Carbonell - Curriculum Vita] (pdf)</ref>.
=Selected Publications=
<ref>[http://www.cs.cmu.edu/~jgc/publications.html Jaime/Publications]</ref> <ref>[https://dblp.uni-trier.de/pers/hd/c/Carbonell:Jaime_G= dblp: Jaime G. Carbonell]</ref>
==1979==
* [[Jaime Carbonell]] ('''1979'''). ''Subjective Understanding: Computer Models of Belief Systems''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/Yale_University Yale University]
==1980 ...==
* [[Jaime Carbonell]] ('''1980'''). ''Learning and Problem Solving by Analogy''. Preprints of the CMU Machine Learning Workshop-Symposium
* [[Jaime Carbonell]] ('''1981'''). ''[https://www.aaai.org/ojs/index.php/aimagazine/article/view/97 Artificial Intelligence Research at Carnegie-Mellon University]''. [[AAAI#AIMAG|AI Magazine]], Vol. 2, No. 1
* [[Jaime Carbonell]] ('''1981'''). ''Counterplanning: A Strategy-Based Model of Adversary Planning in Real-World Situations''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_(journal) Artificial Intelligence], Vol. 16, [https://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Counterplanning_a_StrategyBased_Model_of_Adversary_Planning_in_RealWorld_Situations_1981.pdf pdf}
* [[Jaime Carbonell]] ('''1982'''). ''Learning by Analogy''. Technical report, Carnegie-Mellon University
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1983'''). ''[https://link.springer.com/book/10.1007%2F978-3-662-12405-5 Machine Learning: An Artificial Intelligence Approach]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
: [[Jaime Carbonell]] ('''1983'''). ''Learning by Analogy: Formulating and Generalizing Plans from Past Experience''.
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1985, 2014'''). ''[https://www.elsevier.com/books/machine-learning/michalski/978-0-08-051054-5?gclid=EAIaIQobChMItc_hsp_34AIVUeR3Ch2l9QcDEAYYASABEgKW4_D_BwEMachine Learning: An Artificial Intelligence Approach, Volume I]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1986'''). ''[https://dl.acm.org/citation.cfm?id=21934 Machine Learning: An Artificial Intelligence Approach, Volume II]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[https://link.springer.com/book/10.1007%2F978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. [https://en.wikipedia.org/wiki/Wolters_Kluwer The Kluwer International Series in Engineering and Computer Science], Vol. 12
==1990 ...==
* [[Mathematician#PHaddawy|Peter Haddawy]], [[Jaime Carbonell]], [[Mathematician#JHSiekmann|Jörg H. Siekmann]] (eds.) ('''1994'''). ''[https://link.springer.com/book/10.1007%2F3-540-57697-5 Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action]''. [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 770, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [https://dblp.uni-trier.de/pers/hd/i/Ishida_0001:Toru Toru Ishida], [[Mathematician#JHSiekmann|Jörg H. Siekmann]], [[Jaime Carbonell]] ('''1998'''). ''[https://link.springer.com/book/10.1007%2F3-540-49247-X Community Computing and Support Systems: Social Interaction in Networked Communities]''. [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 1519, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Jaime Carbonell]], [[Mathematician#JGoldstein|Jade Goldstein]] ('''1998'''). ''The Use of MMR and Diversity-Based Reranking for Reordering Documents and Producing Summaries''. [https://dblp.uni-trier.de/db/conf/sigir/sigir98.html SIGIR '98]
==2000 ...==
* [[Jaime Carbonell]], [https://scholar.google.com/citations?user=MlZq4XwAAAAJ&hl=en Yiming Yang], [[William W. Cohen]] ('''2000'''). ''Special Issue of Machine Learning on Information Retrieval Introduction''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 39, Nos. 2-3, [https://link.springer.com/content/pdf/10.1023/A:1007676028106.pdf pdf]
==2010 ...==
* [[Mathematician#XiChen|Xi Chen]], [[Mathematician#SeyoungKim|Seyoung Kim]], [[Qihang Lin]], [[Jaime Carbonell]], [[Mathematician#EPXing|Eric P. Xing]] ('''2010'''). ''Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso''. [https://arxiv.org/abs/1005.3579 arXiv:1005.3579]
* [[Mathematician#XiChen|Xi Chen]], [[Qihang Lin]], [[Mathematician#SeyoungKim|Seyoung Kim]], [[Jaime Carbonell]], [[Mathematician#EPXing|Eric P. Xing]] ('''2010'''). ''Smoothing proximal gradient method for general structured sparse regression''. [https://arxiv.org/abs/1005.4717 arXiv:1005.4717]
* [[Adams Wei Yu]], [[Lei Huang]], [[Qihang Lin]], [[Ruslan Salakhutdinov]], [[Jaime Carbonell]] ('''2017'''). ''Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network''. [https://arxiv.org/abs/1707.04822 arXiv:1707.04822]
* [[George Philipp]], [[Jaime Carbonell]] ('''2017'''). ''Nonparametric Neural Networks''. [https://arxiv.org/abs/1712.05440 arXiv:1712.05440]
* [[George Philipp]], [[Mathematician#DawnSong|Dawn Song]], [[Jaime Carbonell]] ('''2017'''). ''The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions''. [https://arxiv.org/abs/1712.05577 arXiv:1712.05577]
* [[George Philipp]], [[Jaime Carbonell]] ('''2018'''). ''The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks''. [https://arxiv.org/abs/1806.00179 arXiv:1806.00179]
=External Links=
* [http://www.cs.cmu.edu/~jgc/ Jaime Carbonell's Web Page]
* [https://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell from Wikipedia]
* [https://www.genealogy.math.ndsu.nodak.edu/id.php?id=50062 The Mathematics Genealogy Project - Jaime Carbonell]
* [https://scholar.google.com/citations?user=wlqqttEAAAAJ&hl=en Jaime Carbonell - Google Scholar Citations]
=References=
<references />
'''[[People|Up one level]]'''
[[FILE:JCarbonell.jpg|border|right|thumb| Jaime Carbonell <ref>[https://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell from Wikipedia]</ref> ]]
'''Jaime Guillermo Carbonell''',<br/>
an American computer scientist and [[Artificial Intelligence|AI]] researcher with focus on [[Learning|machine learning]] and [https://en.wikipedia.org/wiki/Machine_translation machine translation].
He holds a Ph.D. in computer science in 1979 from [https://en.wikipedia.org/wiki/Yale_University Yale University] and is [[Allen Newell]] professor at [[Carnegie Mellon University]], co-founder and chairman, of ''Carnegie Speech Incorporated'' and ''Wisdom Technologies Corporation'' <ref>[https://franz.com/success/customer_apps/optimization/wisdom.lhtml Franz Inc Customer Applications: Wisdom Technologies, Inc.]</ref>.
Jaime Carbonell invented multiple well-known algorithms and methods, including [https://en.wikipedia.org/wiki/Proactivity proactive] [[Learning|machine learning]] for multi-source cost-sensitive [https://en.wikipedia.org/wiki/Active_learning active learning],
linked [https://en.wikipedia.org/wiki/Conditional_random_field conditional random fields] (L-SCRF) for predicting tertiary and quaternary [https://en.wikipedia.org/wiki/Protein_folding protein folds],
[https://en.wikipedia.org/wiki/Automatic_summarization maximal marginal relevance] (MMR) for information novelty, retrieval and summarization, topic-conditioned modeling for [https://en.wikipedia.org/wiki/Novelty_detection novelty detection],
symmetric optimal [https://en.wikipedia.org/wiki/Phrase phrasal] alignment method for trainable example-based and statistical machine translation, series-anomaly modeling for [https://en.wikipedia.org/wiki/Predictive_analytics#Fraud_detection financial fraud detection]
and [https://en.wikipedia.org/wiki/Clinical_surveillance syndromic surveillance], knowledge-based[https://en.wikipedia.org/wiki/Interlingual_machine_translation interlingual machine translation],
transformational analogy for [https://en.wikipedia.org/wiki/Case-based_reasoning case-based reasoning], [https://en.wikipedia.org/wiki/Derivation_%28linguistics%29 derivational] analogy for reconstructive [https://en.wikipedia.org/wiki/Theory_of_justification justification-based] reasoning,
robust case-frame parsing, seeded[https://en.wikipedia.org/wiki/Version_space version-space learning], and developed improvements to several other machine learning algorithms.
Current research foci include robust statistical learning and mapping [https://en.wikipedia.org/wiki/Peptide_sequence protein sequences] to 3D structure and inferring functional properties, automated transfer-rule learning for machine translation, enriched active transfer learning context-based machine translation, and machine translation for very rare languages <ref>[http://www.cs.cmu.edu/~jgc/CVs/Carbonell%20CV%202013-March.pdf Jaime G. Carbonell - Curriculum Vita] (pdf)</ref>.
=Selected Publications=
<ref>[http://www.cs.cmu.edu/~jgc/publications.html Jaime/Publications]</ref> <ref>[https://dblp.uni-trier.de/pers/hd/c/Carbonell:Jaime_G= dblp: Jaime G. Carbonell]</ref>
==1979==
* [[Jaime Carbonell]] ('''1979'''). ''Subjective Understanding: Computer Models of Belief Systems''. Ph.D. dissertation, [https://en.wikipedia.org/wiki/Yale_University Yale University]
==1980 ...==
* [[Jaime Carbonell]] ('''1980'''). ''Learning and Problem Solving by Analogy''. Preprints of the CMU Machine Learning Workshop-Symposium
* [[Jaime Carbonell]] ('''1981'''). ''[https://www.aaai.org/ojs/index.php/aimagazine/article/view/97 Artificial Intelligence Research at Carnegie-Mellon University]''. [[AAAI#AIMAG|AI Magazine]], Vol. 2, No. 1
* [[Jaime Carbonell]] ('''1981'''). ''Counterplanning: A Strategy-Based Model of Adversary Planning in Real-World Situations''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_(journal) Artificial Intelligence], Vol. 16, [https://www.cs.cmu.edu/~jgc/publication/PublicationPDF/Counterplanning_a_StrategyBased_Model_of_Adversary_Planning_in_RealWorld_Situations_1981.pdf pdf}
* [[Jaime Carbonell]] ('''1982'''). ''Learning by Analogy''. Technical report, Carnegie-Mellon University
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1983'''). ''[https://link.springer.com/book/10.1007%2F978-3-662-12405-5 Machine Learning: An Artificial Intelligence Approach]''. [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
: [[Jaime Carbonell]] ('''1983'''). ''Learning by Analogy: Formulating and Generalizing Plans from Past Experience''.
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1985, 2014'''). ''[https://www.elsevier.com/books/machine-learning/michalski/978-0-08-051054-5?gclid=EAIaIQobChMItc_hsp_34AIVUeR3Ch2l9QcDEAYYASABEgKW4_D_BwEMachine Learning: An Artificial Intelligence Approach, Volume I]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[Ryszard Michalski]], [[Jaime Carbonell]], [[Tom Mitchell]] ('''1986'''). ''[https://dl.acm.org/citation.cfm?id=21934 Machine Learning: An Artificial Intelligence Approach, Volume II]''. [https://en.wikipedia.org/wiki/Morgan_Kaufmann_Publishers Morgan Kaufmann]
* [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[https://link.springer.com/book/10.1007%2F978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. [https://en.wikipedia.org/wiki/Wolters_Kluwer The Kluwer International Series in Engineering and Computer Science], Vol. 12
==1990 ...==
* [[Mathematician#PHaddawy|Peter Haddawy]], [[Jaime Carbonell]], [[Mathematician#JHSiekmann|Jörg H. Siekmann]] (eds.) ('''1994'''). ''[https://link.springer.com/book/10.1007%2F3-540-57697-5 Representing Plans Under Uncertainty: A Logic of Time, Chance, and Action]''. [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 770, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [https://dblp.uni-trier.de/pers/hd/i/Ishida_0001:Toru Toru Ishida], [[Mathematician#JHSiekmann|Jörg H. Siekmann]], [[Jaime Carbonell]] ('''1998'''). ''[https://link.springer.com/book/10.1007%2F3-540-49247-X Community Computing and Support Systems: Social Interaction in Networked Communities]''. [https://en.wikipedia.org/wiki/Lecture_Notes_in_Computer_Science Lecture Notes in Computer Science], Vol. 1519, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* [[Jaime Carbonell]], [[Mathematician#JGoldstein|Jade Goldstein]] ('''1998'''). ''The Use of MMR and Diversity-Based Reranking for Reordering Documents and Producing Summaries''. [https://dblp.uni-trier.de/db/conf/sigir/sigir98.html SIGIR '98]
==2000 ...==
* [[Jaime Carbonell]], [https://scholar.google.com/citations?user=MlZq4XwAAAAJ&hl=en Yiming Yang], [[William W. Cohen]] ('''2000'''). ''Special Issue of Machine Learning on Information Retrieval Introduction''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 39, Nos. 2-3, [https://link.springer.com/content/pdf/10.1023/A:1007676028106.pdf pdf]
==2010 ...==
* [[Mathematician#XiChen|Xi Chen]], [[Mathematician#SeyoungKim|Seyoung Kim]], [[Qihang Lin]], [[Jaime Carbonell]], [[Mathematician#EPXing|Eric P. Xing]] ('''2010'''). ''Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso''. [https://arxiv.org/abs/1005.3579 arXiv:1005.3579]
* [[Mathematician#XiChen|Xi Chen]], [[Qihang Lin]], [[Mathematician#SeyoungKim|Seyoung Kim]], [[Jaime Carbonell]], [[Mathematician#EPXing|Eric P. Xing]] ('''2010'''). ''Smoothing proximal gradient method for general structured sparse regression''. [https://arxiv.org/abs/1005.4717 arXiv:1005.4717]
* [[Adams Wei Yu]], [[Lei Huang]], [[Qihang Lin]], [[Ruslan Salakhutdinov]], [[Jaime Carbonell]] ('''2017'''). ''Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network''. [https://arxiv.org/abs/1707.04822 arXiv:1707.04822]
* [[George Philipp]], [[Jaime Carbonell]] ('''2017'''). ''Nonparametric Neural Networks''. [https://arxiv.org/abs/1712.05440 arXiv:1712.05440]
* [[George Philipp]], [[Mathematician#DawnSong|Dawn Song]], [[Jaime Carbonell]] ('''2017'''). ''The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions''. [https://arxiv.org/abs/1712.05577 arXiv:1712.05577]
* [[George Philipp]], [[Jaime Carbonell]] ('''2018'''). ''The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks''. [https://arxiv.org/abs/1806.00179 arXiv:1806.00179]
=External Links=
* [http://www.cs.cmu.edu/~jgc/ Jaime Carbonell's Web Page]
* [https://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell from Wikipedia]
* [https://www.genealogy.math.ndsu.nodak.edu/id.php?id=50062 The Mathematics Genealogy Project - Jaime Carbonell]
* [https://scholar.google.com/citations?user=wlqqttEAAAAJ&hl=en Jaime Carbonell - Google Scholar Citations]
=References=
<references />
'''[[People|Up one level]]'''
