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`Jaime G. Carbonell -- Curriculum Vita
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`Business Address
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`Language Technologies Institute
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`Carnegie Mellon University
`Pittsburgh, Pennsylvania 15213 USA
`Telephone: (412) 268-7279
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`Email: jgc@cs.cmu.edu
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`Fax: (412) 268-6298
`http://www.cs.cmu.edu/~jgc/
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`Citizenship: USA
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`Academic and Professional Positions Held
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`2012 - Present
`1996 - Present
`1995 - Present
`2006 - Present
`2001 – Present
`1995 - 2003
`1986 - 1996
`1987 - 1995
`1983 - 1987
`1983 - 1996
`1979 - 1983
`1975 - 1978
`1975 - 1977
`1974 - 1975
`1971 - 1975
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`Education
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`University Professor, School of Computer Science, Carnegie Mellon University
`Director, Language Technologies Institute, Carnegie Mellon University
`Allen Newell Professor of Computer Science, Carnegie Mellon University
`Adjunct Faculty, Dept of Computational Biology, U Pittsburgh Medical School.
`Co-Founder, Board chairman, Carnegie Speech Corporation
`Co-Founder, Board chairman, Wisdom Technologies Corporation
`Director, Center for Machine Translation, Carnegie Mellon University
`Professor of Computer Science, Carnegie Mellon University
`Associate Professor of Computer Science, Carnegie Mellon University
`Co-Founder, Director, Scientific advisor, Carnegie Group Inc.
`Assistant Professor of Computer Science, Carnegie Mellon University
`Research Fellow in Computer Science, Yale University
`Teaching Assistant in Computer Science, Yale University
`Research Assistant, Center for Space Research, MIT
`Research Programmer, Artificial Intelligence, BBN, Cambridge, MA
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`1975 - 1979 Yale University
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`PhD 1979 – Computer Science
`MPhil 1977 - Computer Science
`MS 1976 - Computer Science
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`1971 - 1975 Massachusetts Institute of Technology
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`SB 1975 - Physics
`SB 1975 - Mathematics
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`Professional Organizations and Activities
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`Member: ACM (elected Chair of SIGART 1983-85), ACL, AAAI (AAAI Fellow 1988-present, AAAI executive
`committee 1990-92), Cognitive Science Society, Sigma Xi, AMTA.
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`Government and other Committees: NSF/CISE Scientific Advisory Committee (2010-2014), NIH Human
`Genome Scientific Advisory Committee, aka "Watson Committee" (1988-1992). DFKI (National German AI Lab)
`Scientific Advisory Board (1988-2003). National Institute of Standards and Technology (NIST/IAD), Scientific
`Advisory Committee (1997-2001), DOE Oakridge National Laboratories Scientific Advisory Committee (1985-
`1987), GMD/IPSI Information Sciences (Germany) Scientific Advisory board (1990-2001), Citigroup Technology
`Advisory committee (1987-1995).
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`Book Series co-Editor: Lecture Notes in Artificial Intelligence North American editor, Springer (1996-2008).
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`Editorial Boards: Machine Learning Journal (1984-2000, editor-in-chief 1988-1993), Machine Translation Journal
`(1980’s), Artificial Intelligence Journal (1984-2008).
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`Other Accomplishments
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`Research: Invented multiple well-known algorithms and methods in statistical machine learning including:
`Proactive Machine Learning (with Donmez) for multi-source cost-sensitive active learning, Linked Conditional
`Random Fields (L-SCRF, with Liu) for predicting tertiary and quaternary protein folds, new regularization-based
`transfer learning methods (with Kshirsagar), Maximal Marginal Relevance (MMR, with Goldstein) for information
`novelty, retrieval and summarization, topic-conditioned modeling for novelty detection, symmetric optimal phrasal
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` IPR2018-01079, Exhibit 2006
`Patent Owner, AGIS Software Development
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`alignment method for trainable example-based and statistical machine translation, series-anomaly modeling for
`financial fraud detection and syndromic surveillance, knowledge-based interlingual machine translation (and later
`extended it with colleagues: Tomita, Nirenburg, Nyberg, and Mitamura), transformational analogy for case-based
`reasoning, derivational analogy for reconstructive justification-based reasoning (with Veloso), robust case-frame
`parsing (with Hayes), Seeded Version-Space Learning (with polynomial complexity), and developed
`improvements to several other machine learning algorithms. Current research foci include robust statistical
`learning and mapping 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.
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`Software artifacts: Protein structure prediction software (with Liu), active and proactive learning algorithms
`(with Donmez), PRODIGY general-purpose planner (with Minton and Veloso), KANT knowledge-based machine
`translation (with Nyberg and Mitamura), JAVELIN Question-Answering system (with Nyberg et al), CONDOR
`search engine (with Callan et al), MMR summarizer engine (with Goldstein), POLITICS simulated reasoning
`engine, DIPLOMAT speech-MT (with Frederking et al), EBMT example-based general purpose MT (with
`Brown), SMOKEY distributed sensor-based fire detection and suppression system, and several others.
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`Education: Created the PhD program at CMU on Language Technologies with 100’s of PhD graduates. Designed
`new courses in Language Technologies, Machine learning, Data Mining and eCommerce. Edited 3 books on
`Machine Learning. Taught undergraduate and graduate courses in Artificial Intelligence, Machine Learning,
`Software Engineering, Natural Language Processing, Language Technologies, Data Mining, Information Retrieval,
`Web-Based Architectures, Machine Translation, Algorithms, and related Computer Science topics.
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`Other initiatives: Created the Center for Machine Translation at CMU in 1986, and the Language Technologies
`Institute in 1996. Co-creator & co-director of the Universal Library and million-book project (US, China, India).
`MT Summit chair 1991, Co-designer of interactive pinpointing speech tutor. Co-creator & co-PI of new
`Computational Biolinguistics Initiative at CMU. Helped create the CMU-Pitt Computational Biology Program.
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`Industrial Consulting: Meaningful Machines (participated in designing the context-based machine translation
`methods, 2002-2009), Industrial Scientific (data mining to improve workplace safety, 2008-2017), Carnegie
`Speech Inc. (business and technology strategy for intelligent language tutoring, 2002-present) Carnegie Group Inc
`(expert systems & financial data mining: 1984-1997), Citicorp (financial data mining, real time transactions, new
`IT technology insertion: 1990-1998), Lycos Inc. (launching the internet search engine 1998-1999), Vivisimo
`(2003-2006, scientific advisory board), Searchline (launched the new search engine, 2002-2004), Wisdom
`Technologies (financial optimization and data mining for corporate treasuries, large banks, etc. 1995-2003),
`Dynamix Technologies (designed their large-scale data mining engine with applications to Homeland Security,
`AR/AP financial optimization, transactional optimization in bond trading and in re-insurance, pattern discovery in
`network-wire-transfer streams, syndromic surveillance, etc. 2000-present), Peak Strategy (financial analytics,
`investment, mining, 2005-2007), Boeing (designed their intensive data mining course), Citibank (technology
`advisory board for 10 years, focusing on text and data mining, fraud detection, optimization methods, etc.), plus
`about 10 other shorter-term engagements in data mining in industry, including financial data mining: transactional
`fraud detection,
`instant-credit bad-dept minimization, collection effectiveness, cash-flow optimization
`(sweep/invest/float), data mining for risk/cost tradeoff minimization (FX, interest rate, counterparty, debt, etc), and
`so on. During these engagements process I designed or co-designed three data mining engines, and evaluated and
`help improve numerous other data mining engines, including the machine learning algorithms at their core.
`Consultant for UPMC, Precyse and Nthrive leading ICD9, ICD10 automated coding of EMRs vis NLP and ML.
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`Miscellaneous: Gave over 500 invited or refereed-paper presentations (colloquia, seminars, panels, addresses,
`conferences, key-notes, etc.). Received the IJCAI Best Paper Award in 1997 for translingual information retrieval.
`Received the CMU Computer Science Department’s teaching award (1987), the Sperry Fellowship for excellence
`in AI research (1986), unsolicited gifts from Alcoa and Hughes corporations for research in machine learning, and
`a "recognition of service" award from the ACM for the SIGART presidency (1983-1985). Provided congressional
`testimony on machine translation (1990). Leader of the NSF/JTEC study group on Japanese machine translation
`science and technology (1990-91). Principal investigator or major participant in research grants and contracts
`totaling over 100 million dollars since 1979 at CMU..
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`Expert witness: Provided expert testimony and depositions, expert reports, advice, consulting , etc. in several
`cases involving intellectual property including: software design and copyrights, patents for data mining, patents for
`search engine algorithms and methods, speech recognition, integrated software systems, computational and
`statistical applications in finance, statistical machine learning, text mining, text processing, natural language, etc.
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`Patents: Inventor in several issued patents, provided advice on patentability of new software and methods, helped
`draft software-related patents, conducted prior art searches, composed responses to patent examiners, evaluated
`validity of issued patents As expert witness provided opinions and reports on patent infringement, patent validity
`(anticipation, obviousness), claim constructions, Inter Partes Reviews, etc.
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`Completed PhD Advisees & co-Advisees (and first job after graduation)
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`1. Masaru Tomita, Professor and Dean, Keio University, Computer Science and Biology
`2. Steven Minton, USC-ISI & CTO of Fetch Inc
`3. Manuela Veloso, Professor, CMU Computer Science
`4. Jack Mostow, Research Professor, CMU Robotics
`5. Michael Mauldin, Founder of Lycos Inc. (and former faculty at CMU)
`6. Oren Etzioni, Professor, U of Washington (and founder of Metacrawler)
`7. Robert Frederking, Sr. System Scientist, CMU Language Technologies Institute
`8. Alex Hauptmann, Research Professor, CMU Computer Science
`9. Klaus Gross, Chief Scientist, Haley Enterprises
`10. Wei-Min Shen, USC-ISI and faculty at USC Computer Science
`11. Eric Nyberg, Professor, CMU Language Technologies Inst
`12. Ralf Brown, Sr. System Scientist, CMU Language Technologies Institute
`13. Craig Knoblock, USC-ISI and faculty at USC Computer Science
`14. Akira Ushioda, Fujitsu Labs, Manager Language Technologies
`15. Mark Perlin, CEO Cybergenetics
`16. Daniel Kuokka, Senior researcher, Tektronix
`17. Yolanda Gil, USC-ISI, senior researcher
`18. Xuemei Wang, Lockheed Martin & ISLE Computing
`19. Robert Joseph, Verizon Communication, senior researcher
`20. Jill Fain-Lehman, Founder of Autism Technology, Disney Pittburgh
`21. Eugene Fink, Senior Sys Scientist CMU (formerly Asst Prof, U of Florida)
`22. Angela Kennedy, President Carnegie Speech (formerly CEO Wisdom Technologies)
`23. Alicia Perez, Faculty at Universidad Catolica de Salta, Argentina
`24. Jim Blythe, Researcher, USC-ISI
`25. Alex Franz, Google Inc.
`26. Yan Qu, Senior Researcher, Oracle Corp
`27. Jade Goldstein, US DoD research laboratory
`28. Boyan Onyskevich, US DoD research laboratory, senior manager
`29. Michael Mateas, Faculty, University of CA.
`30. Kathreina Probst, Faculty at Georgia Tech
`31. Yan Liu, Faculty at University of Southern Cal.
`32. Lucian Lita, Siemens Princeton Research Lab
`33. Paul Bennett, Microsoft Research Labs
`34. Chun Jin, AT&T Research
`35. Ariadna Font-Llijtos, IBM, Big Data Labs
`36. Ulas Bardak, Postdoctoral fellow, Tokyo University
`37. Christian Monson, Oregon Graduate Institute
`38. Cenk Gazen, Fetch Inc. Los Angeles
`39. Monica Rogati, Linked-In, manager of R&D
`40. Vasco Pedro, CEO of Bueda Inc.
`41. Meryem Pinar Donmez, Salesforce R&D
`42. Jingrui He, Faculty, Stevens Institute
`43. Jae-Dong Kim, Google Pittsburgh
`44. Rashmi Gangadharaiah, Microsoft Research Labs
`45. Oznur Tastan, Microsoft Research Labs, then Bilkent U. Faculty.
`46. Jonathan Elsas, Google Pittsburgh
`47. Vamshi Ambati, AT&T Research
`48. Bin Fu, Google, NYC
`49. Mehrbod Sharifi, Google Pittsburgh
`50. Ravi Starzl. CMU Faculty
`51. Xi Chen, UC Berkeley Postdoc, then NYU Faculty
`52. Balakrishnan, Sevaraman, UC Berkeley Postdoc, then CMU Faculty
`53. Liu Yang, CMU Postdoc, then Princeton Postdoc
`54. Mohit Kumar, Flipkart Inc.
`55. Selen Uguroglu, Apple Inc.
`56. Luis Marujo, Snapchat.
`57. Meghana Kshirsagar, IBM Research
`58. Andrew Hsi, Bloomberg
`59. Ashiqur, Kurbadash, CMU Postdoc.
`60. Shane Moon, Facebook
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`61. Keerthiriam Murugesan, IBM Research
`62. Jesse Dunietz, MIT
`63. Guoqing Zheng, Google
`64. Jeff Flanigan, UMass
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`Current PhD advisees:
`1. Daegun Won, CMU, LTI
`2. Wan Li, CMU, LTI
`3. George Phillipp, CMU, Comp Sci Dept
`4. Adams Wei-Yu, CMU, Machine Learning Dept.
`5. Petar Stojano, CMU, Computer Sci Dept
`6. Jay-Yoon Lee, CMU, Computer Sci Dept
`7. Zirui (Ed) Wang, CMU, LTI
`8. Jiateng Xie, CMU, LTI
`9. Shruti Rijhwani, CMU LTI
`10. Sanket Mehta, CMU LTI
`11. Hieu Pham, CMU LTI
`12. Aditya Chandrasekar, CMU LTI
`13. Vidhisha Balachandran, CMU LTI
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`Publications
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`1. Lee, J., Sanket, Mehta, S., Wick, M., Tristan, J, and Carbonell, J., “Gradient-based Inference for Networks with
`Output Constraints”, Proc of AAAI, Hawaii, USA, 2019.
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`2. Stojanov, P., Zhang, K., Gong, M., Carbonell, J., “Low-Dimensional Density Ratio Estimation for Covariate Shift
`Correction”, Proc. of AISTATS, 2019.
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`3. Stojanov, P., Zhang, K., Gong, M., Carbonell, J., “Data-Driven Approach to Multiple-Source Domain Adaptation”,
`Proc. of AISTATS, 2019.
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`4. Rijhwani, S., Jiateng Xie, J., Neubig, G., Carbonell, J., “Zero-shot Neural Transfer for Cross-lingual Entity
`Linking”, Proc of AAAI, Hawaii, USA, 2019.
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`5. KhudaBukhsh, A., Carbonell, J., “Expertise Drift in Referral Networks”, In Proc of the International Conference on
`Autonomous Agents and Multiagent Systems (AAMAS), 2018.
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`6. KhudaBukhsh, A., Carbonell, J., and Jansen. J., “Robust Learning in Referral Networks: A Comparative Analysis”,
`Journal of Intelligent Information Systems, June, 2018.
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`7. Yang, L, Hanneke, S., and Carbonell, J. “Bounds on the Minimax Rate for Estimating a Prior over a VC Class from
`Independent Learning Tasks.”, Theoretical Computer Science, Vol. 716, pp. 124-140, 2018 (Extended version of
`ALT 2015 paper.)
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`8. Wang, Z. and Carbonell, J., "Towards More Reliable Transfer Learning", Proc. of ECML/PKDD, 2018.
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`9. Xie, J., Z Yang, Z., Neubig, G., Smith, N., Carbonell, J., “Neural cross-lingual named entity recognition with
`minimal resources”, Proc. of EMNLP, 2018.
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`10. Chaudhary, A., Zhou, C., Levin, L., Neubig, G., Mortensen, D., and Carbonell, J., “Adapting Word Embeddings to
`New Languages with Morphological and Phonological Subword Representations”, Proc. of EMNLP, 2018.
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`11. Metha, S., Lee, J. and Carbonell, J., “Towards Semi-Supervised Learning for Deep Semantic Role Labeling” In Proc
`of Empirical Methods for Natural Language Processing (EMNLP), 2018.
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`12. Dunietz, J., Levin, L., and Carbonell, J. “DeepCx: A Transition-Based Approach for Shallow Semantic Parsing with
`Complex Constructional Triggers”, In Proc of Empirical Methods for Natural Language Processing (EMNLP),
`2018.
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`13. Won, D., Jansen, P., and Carbonell, J., ``Temporal Transfer Learning for Drift Adaptation’’, In Proc. of the 26th
`European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN),
`2018.
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`14. Philipp, G., Song, D., and Carbonell, J. “Gradients explode – Deep Networks and Shallow-ResNet Explained”, ICLR
`workshop, 2018.
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`15. Philipp, G., and Carbonell, J, “The Nonlinearity Coefficient-Predicting Overfitting in Deep Neural Networks”, arXiv
`preprint arXiv:1806.00179, 2018.
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`16. Wang, Z. and Carbonell, J. “Towards more Reliable Transfer Learning”, Preprint in arXiv:1807.02235, 2018.
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`17. Bourai, A., and Carbonell, J., “I Know What You Don't Know: Proactive Learning through Targeted Human
`Interaction.” In Proc of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
`2018.
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`18. KhudaBukhsh, A., Hong, J., and Carbonell , J., “Market-Aware Proactive Skill Posting”, In Proc of the Twenty-
`Fourth International Symposium on Methodologies for Intelligent Systems, 2018.
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`19. Moon, S., and Carbonell, J., “Completely Heterogeneous Transfer Learning with Attention - What And What Not To
`Transfer”, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
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`20. Philipp, G., and Carbonell, J., “Non-Parametric Neural Networks”, Proceedings of the 5th International Conference
`on Learning Representations (ICLR), 2017.
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`21. KhudaBukhsh A., Carbonell, J., Jansen, P., “Incentive Compatible Proactive Skill Posting in Referral Networks.
`Fifteenth European Conference on Multi-Agent Systems (EUMAS), 2017.
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`22. KhudaBukhsh A., Carbonell, J, and Jansen, P., “Robust Learning in Expert Networks: A Comparative Analysis”,
`Proceedings of the Twenty-Third International Symposium on Methodologies for Intelligent Systems (ISMIS), 2017.
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`23. Murugesan, K., and Carbonell, J., “Multitask Multiple Kernel Relationship Learning”, Proceedings of the 17th SIAM
`International Conference on Data Mining (SDM), 2017.
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`24. Murugesan, K., Carbonell, J., “Active Learning from Peers”, In Proc of the 31th Annual Conference on Neural
`Information Processing Systems (NIPS), Long Beach, California, 2017.
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`25. Murugesan, K., Carbonell, J., “Self-Paced Multitask Learning with Shared Knowledge”, In Proc of 26th
`International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, 2017.
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`26. Kshirsagar, M., Murugesan, K., Carbonell, J., Klein-Seetharaman, J., “Multitask matrix completion for learning
`protein interactions across diseases”, Journal of Computational Biology. January 2017, (Journal version of
`RECOMB 2016)
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`27. Dunietz, J., Levin, L. and Carbonell, J., “Automatically Tagging Constructions of Causations and their Slot Fillers”,
`Transactions for Computational Linguistics, 2017.
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`28. Dunietz, J., Levin, L. and Carbonell, J., “The BECauSE Corpus 2.0: Annotating Causality and Overlapping
`Relations”, Proceedings of the 11th Linguistic Annotation Workshop (LAW), 2017.
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`29. Marujo, L., Ribeiro, R, Gershman, A., Martins de Matos, D., Neto, J., and Carbonell, J., “Event-based
`Summarization Using a Centrality-as-Relevance Model. Knowl. Inf. Syst. 50(3): 945-968, 2017.
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`30. Shiang, S., Rosenthal, S., Gershman, A., Carbonell, J., and Oh, J., “Vision-Language Fusion for Object
`Recognition.”, Proc. Of the American Association for Artificial Intelligence (AAAI), pp. 4603-4610, 2018.
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`31. VanHoudnos, N., Casey, W. , French, D., Lindauer, B., Kanal, E., Wright, E., Woods, B., Moon, S., Jansen, P.,
`Carbonell, J., ``This Malware Looks Familiar: Laymen identify Malware Run-time Similarity with Chernoff faces
`and Stick Figures’’, Proc. of the 10th EAI International Conference on Bio-inspired Information and Communication
`Technologies, 2017.
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`32. Hsi, A., Carbonell, J., and Yang, Y., “CMU_CS_Event TAC-KBP2017 Event Argument Extraction System” In
`Proceedings of the Text Analysis Conference, 2017.
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`33. Moon, S. and Carbonell, J., “Proactive Transfer Learning for Heterogeneous Label Spaces”, Proc of the Joint
`European Conference on Machine Learning and Knowledge Discovery, 2016.
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`34. Khudabukhsh. A., Carbonell, J., Jansen, P., “Distributed Interval Estimation Learning in Expert Referral Networks”,
`in Proceedings of the European Conference on AI, (ECAI), 2016.
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`35. Khudabukhsh. A., Carbonell, J., Jansen, P., “Proactive Skill Posting in Referral Networks”, Proc of the Australian
`Joint Conference on Artificial Intelligence, pp 585-596, 2016.
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`36. KhudaBukhsh, A., Carbonell, J., Jansen, P., “Proactive-DIEL in Evolving Referral Networks”, In Proc of the
`Fourteenth European Conference on Multi-Agent Systems (EUMAS), 2016.
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`37. Kshirsagar, M., Carbonell, J., Klein-Seetharaman, J, and Murugesan, K., “Multitask matrix completion for learning
`protein interactions across diseases”. Proc. of the 20th Annual Int. Conf. on Research in Computational Molecular
`Biology (RECOMB), 2016.
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`38. “Liu, H., Ma, W., Yang, Y. and Carbonell, J. “Learning Concept Graphs from Online Educational Data”, Journal of
`Artificial Intelligence Research, vol 55, pp. 1059-1090, 2016.
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`39. Murugesan, K., Liu, H. Carbonell, J., and Yang, Y. “Adaptive Smooth Online Multi-Task Learning” Proceedings of
`NIPS, 2016.
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`40. Zheng, G., Yang, Y., Carbonell, J., “Efficient Shift-Invariant Dictionary Learning”, in Proceedings of the ACM KDD
`Conference, 2016.
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`41. KhudaBukhsh A., Carbonell, J, and Jansen, P., “Proactive-DIEL in Evolving Referral Networks”, Proceedings of the
`Fourteenth European Conference on Multi-Agent Systems (EUMAS), 2016.
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`42. Flanigan, J., Dyer, C., Smith, N. and Carbonell, J., “Generation from Abstract Meaning Representation using Tree
`Transducers”, in SemEval, 2016.
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`43. Flanigan, J., Dyer, C., Smith, N. and Carbonell, J., “Graph-Based AMR Parsing with Infinite Ramp Loss”, in
`Proceedings of NAACL, 2016.
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`44. Chaplot, D., Yang, Y., Carbonell, J., Koedinger, K., “Data-Driven Automated Induction of Prerequisite Graphs”, in
`Proceedings of Educational Data Mining Conference, 2016.
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`45. Marujo, L., Ling, W., Ribeiro, R., Gersman, A., Carbonell J., Martins de Matos, D., Neto, J., “Exploring Events and
`Distributed Representations of Text in Multi-Document Summarization”. Knowledge-Based Systems Journal, 2016.
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`46. Marujo, L., Ribeiro, R, Gershman, A., Martins de Matos, D., Neto, D., Carbonell, J., "Event-Based Summarization
`using a Centrality-As-Relevance Model" Knowledge and Information Systems Journal, 2016.
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`47. Hsi, A.,Carbonell, J., Yang, Y., and Hu, R., “Leveraging Multilingual Training for Limited Resource Event
`Extraction”, In Proceedings of COLING, 2016.
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`48. Hsi, A., Carbonell, J., and Yang, Y., “CMU_CS_Event TAC-KBP2016 Event Argument Extraction System” In
`Proceedings of the Text Analysis Conference, 2016.
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`49. Yang, L, Hanneke, S. and Carbonell, J. “Bounds on the Minimax Rate for Estimating a Prior over a VC Class from
`Independent Learning Tasks”. Proc of the Algorithmic Learning Theory Conf. (ALT), 2015.
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`50. Gupta, D., Carbonell, J., Gershman, A., Klein, S., and Miller, D. “Unsupervised Phrasal Near-Synonym Generation
`from Text Corpora”. In Proc. of the 29th Conference on Artificial Intelligence (AAAI), 2015.
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`51. Kshirsagar, M., Thompson, S., Schneider, N., Carbonell, J., Smith, N., Dyer, C. “Frame-Semantic Role Labeling
`with Heterogeneous Annotations”, In Proc, of the Association of Computational Linguistics (ACL), 2015.
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`52. Kshirsagar, M,, Schleker, S., Carbonell, J., and Klein-Seetharaman, J., “Techniques for transferring host-pathogen
`protein interactions knowledge to new tasks", Frontiers in Microbiology Journal, 2015.
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`53. Yang, Y., Liu, H., Carbonell, J., Ma, W., “Concept-Graph Learning from Educational Data”. Proc. of the 8th Int.
`Conf. on Web Search and Data Mining (WSDM), 2015.
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`54. Dunietz, J., Levin, L., and Carbonell, J., "Annotating Causal Language Using Corpus Lexicography of
`Constructions." Proceedings of LAW IX – The 9th Linguistic Annotation Workshop, 2015.
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`55. Marujo, L.m, Ribeiro, R., Matos, D., Neto, J., Gershamn, A., and Carbonell, J.,, “Extending a Single-Document
`Summarizer to Multi-Documents: A Hierarchical Approach”, arXiv preprint arXiv:1507.02907, 2015.
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`56. Hsi, A., Carbonell, J., and Yang, Y., “Modeling Event Extraction via Multilingual Data Sources” In Proceedings of
`the Text Analysis Conference, 2015.
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`57. Marujo, L., Portelo, J, Ling, W., Martin de Matos, D., Neto, J., Gershman, A., Carbonell, J., Trancoso, I., Raj, B.,
`“Privacy-Preserving Multi-Document Summarization” Proc. of the Privacy Preserving Information Retrieval
`Workshop of the ACM’s Special Interest Group in Information Retrieval Conference (SIGIR), 2014.
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`58. Moon, S., and Carbonell, J. “Proactive Learning with Multiple Class-Sensitive Labelers”, in Proceedings of the
`International Conference on Data Science and Advanced Analytics, Shanghai, 2014.
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`59. Wei-Yu, A. Kılınç-Karzan, F., and Carbonell, J., “Saddle Points and Accelerated Perceptron Algorithms”, Proc. of
`International Conference on Machine Learning (ICML), Beijing, 2014 (Informs Data Mining student best paper).
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`60. Wei-Yu, A., Ma, W., Yu, Y., Carbonell, J., and Sra S., “Efficient Structured Matrix Rank Miminimization” In
`Proceedings of the 27th Neural Information Processing Conference (NIPS), 2014.
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`61. Flanigan, J., Thomson, S., Carbonell, J., Dyer, C., and Smith, N. “A Discriminative Graph-Based Parser for the
`Abstract Meaning Representation” In Proc, of the Association for Computational Linguistics Conference (ACL),
`Baltimore, 2014. (Best paper honorable mention).
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`62. Khudbukhsh, A., Jansen, P. and Carbonell, J., “Detecting Non-Adversarial Collusion in Crowd Sourcing” In Proc. of
`the Conference on Human Computation and Crowdsourcing (HCOMP), Pittsburgh, 2014.
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`63. Marujo, L., Carvalho J., Gershman, A., Carbonell, J., Neto, P., Martins de Matos, P. “Textual Event Detection Using
`Fuzzy Fingerprint”, In Proceedings of IEEE Intelligent Systems, Warsaw, Poland, September 2014.
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`64. Tastan, O., Qi, Y., Carbonell, J., and Klein-Seetharaman, J., “Refining Literature-Curated Protein Interactions using
`Expert Opinions”. Proc. of the Pacific Symposium on Biocomputing, 2014.
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`65. Kshirsagar, M., Carbonell, J. and Klein-Seetharaman, J., “Multi-Task Learning for Host-pathogen Protein
`Interactions”, Proc of ISMB 2013 and Bioinformatics, 2013.
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`66. Yang, L., Blum, A., and Carbonell, J. “Learnability of DNF with Representation-Specific Queries.” Innovations in
`Theoretical Computer Science (ITCS), 2013.
`
`67. Yang. L., and Carbonell, J., “Buy-in-Bulk Active Learning” Proc of Advances in Neural Information Processing
`Systems (NIPS), 2013.
`
`68. Kshirsagar, M., Carbonell, J. and Klein-Seetharaman, J., “Multisource Transfer Learning for Host-Pathogen Protein
`Interaction Prediction in Unlabeled Tasks”, NIPS Wkshp on Machine Learning for Computational Biology, 2013.
`
`69. Flanigan, J., Dyer, C., and Carbonell, J., “Large-Scale Discriminative Training for Statistical Machine Translation
`Using Held-Out Line Search”, Proc. of North American AC (NAACL), 2013.
`
`70. Duneitz, J., Levin, L, and Carbonell, J., “The Effects of Lexical Resource Quality on Preference Violation
`Detection”, Proc. of North American ACL (NAACL), 2013.
`
`71. Starzl, R., Brandacher, G., Lee, A., Carbonell, J., Zhang, W. Schnieder, J. Gorantla, B. Schneeberger, S., Zheng, X.
`“Review of the Early Diagnoses and Assessment of Rejection in Vascualrized Composite Allotransplantation”,
`Clinical and Developmental Immunology, Article ID 402980, 2013.
`
`72. Starzl. R., Wolframd, D., Samora, R. Jefferson, B., Barclay, D., Ho, C., Brandacher, G., Schneeberger, S., Lee, A’,
`Carbonell, J., Bodovtz, Y., “Tissue-Specific Patterns of Capase-1 and Cytokines in Excisional Wounds are Altered
`by Shock in Skin and Muscle”, Journal of Critical Care, Vol 28, Issue 1, 2013.
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`73. Ribeiro, R., Marujo, L., Martins de Matos, D., Neto, P., Gershman, A., Carbonell, J., “Self Reinforcement for
`Important Passage Retrieval”, In Proceedings of ACM SIGIR Conference, Dublin, 2013.
`
`74. Chen, X., Liu, H., and Carbonell, J., “Structured Sparse Canonical Correlation Analysis”, International Conf, on AI
`and Statistics (AISTATS), 2012.
`
`75. Yang, L. Hanneke, S. and Carbonell., J,, “A Theory of Transfer Learning with Applications to Active Learning,”
`Machine Learning Journal, 2012, DOI: 10.1007/s10994-012-5310-y
`
`76. Chen, X., He, J., Lawrence, R., and Carbonell, J., “Adaptive Multi-Task Sparse Learning with an Application to
`fMRI Study”, SIAM International Conference on Data Mining (SDM), 2012.
`
`77. Chen, X., Lin, Q., Kim, S., Carbonell, J., Xing, E., “Smoothing Proximal Gradient Method for General Structured
`Sparse Learning”, Annals of Applied Statistics, 2012. (Full version.)
`
`78. Ambati, V., Vogel, S. and Carbonell, J., "Collaborative Workflow for Crowdsourcing Translation”, Proc of ACM
`Conference on Computer Supported Cooperative Work, Washington, USA. 2012.
`
`79. Kshirsagar, M., Carbonell, J. and Klein-Seetharaman, J., “Transfer learning based methods towards the discovery of
`host-pathogen protein-protein interactions" Proc of ISMB, 2012.
`
`
`80. Kshirsagar, M., Carbonell, J. and Klein-Seetharaman, J., "Techniques to cope with missing data in host-pathogen
`protein interaction prediction" J of Bioinformatics 2012 (also presented at ECCB 2012).
`
`81. Starzl R, Zhang W, Wang Y, Brandacher G, Schneeberger S, Gorantla V, Carbonell J, Barclay D, Vodovotz Y,
`Zheng XX. “Inflammatory Biomarker Patterns Identify Specific Types of Rat Skin Rejection.” 3rd International
`Conference on Transplantomics and Biomarkers in Organ Transplantation. March 2012.
`
`
`82. Starzl R, Zhang W, Wang Y, Brandacher G, Schneeberger S, Gorantla V, Carbonell J, Barclay D, Vodovotz Y,
`Zheng XX. “Distinctive Immune Signaling Patterns and Factors in Rat Skin Rejection.” American Transplant
`Congress. June 2012.
`
`83. Fu, B., Fink, E., Gibson, G., and Carbonell J., “Indexing and Fast Near-Matching of Billions of Astronomical
`Objects”, Proc. of IASDS, 2012.
`
`84. Fu, B., Fink, E., Gibson, G., and Carbonell J., “Exact and Approximate Computation of a Histogram of Pairwise
`Distances between Astronomical Objects”, Proc of AstoHPC, 2012.
`
`85. Uguroglu, S., M. Doyle, R. Biedermann, J. Carbonell. “Cost-sensitive Risk Stratification in the Diagnosis of Heart
`Disease.” Proc of IAAA, 2012.
`
`86. Yang, L., and Carbonell, J. “Buy-in-Bulk Active Learning.” Tech report CMU-ML-12-110., 2012.
`
`87. Yang, L. Hanneke, S., and Carbonell, J.” Bounds on the Minimax Rate for Estimating a Prior over a VC Class from
`Independent Learning Tasks”. Tech report CMU-ML-12-112., 2012.
`
`88. Marujo, L., Ling, W., Gershman, A., Carbonell, J., Neto, J., Martins de Matos, D., “Recognition of Named-Event
`Passages in News Articles,” In Proceedings of 24th International Conference on Computational Linguistics
`(COLING), Mumbai, 2012.
`
`89. Marujo, L., Ribeiro, R., Martins de Matos, D., Neto, J., Gershman, A., Carbonell, J., “Key Phrase Extraction of
`Lightly Filtered Broadcast News,” In Proceedings of 15th International Conference on Text, Speech and Dialogue
`(TSD), Brno, Czech Republic, 2012.
`
`90. Marujo, L., Gershman, A.,, Carbonell, J., Frederking, R., Neto, J., “Supervised Topical Key Phrase Extraction of
`News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization,” In Proceedings of the 8th
`International Conference on Language Resources and Evaluation (LREC), Istanbul, 2012
`
`91. Balakrishnan, S., Kamisetty, H., Carbonell, J., Lee, S., Langmead, C., “Learning Generative Models for Protein Fold
`
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`Families”, Proteins, Vol 79, Issue 4, pp 1061-1078, 2011,
`
`92. Chen, X., Qi, Y., Bai, Q., Carbonell, J. “Sparse Latent Semantic Analysis”, Proc of SIAM International Conference
`on Data Mining (SDM), 2011.
`
`93. Chen, X., Lin, Q., Kim, S. Carbonell, J. Xing, P. “Smoothing Proximal Gradient Method for Generalized Structure
`Learning” Proc of Uncertainty in Artificial Intelligence Conference (UAI), 2011. (Short version)
`
`94. Yang, L, Hanneke, S., Carbonell, J. “The Identifiability of Priors from Bounded Sample Sizes with Applications to
`Transfer Learning”, Proc of the 24th Proc of the Annual Conference on Learning Theory (COLT), Budapest, 2011.
`
`95. Yang, L. Hanneke, S., Carbonell, J., “The Sample Complexity of Self-Verifying Bayesian Active Learning”, Proc of
`the International Conference on Artificia