Dan Roth

Description

Address

Biography

Dan Roth received his Ph.D. from Harvard University in 1995. He is a professor in the Department of Computer Science at the U of I and a part-time faculty member in the Beckman Institute Artificial Intelligence group. Dan is a Fellow of the ACM and AAAI for his contributions to the foundations of machine learning and inference and for developing learning centered solutions for natural language processing problems.

Honors

Fellow, AAAS, 2014; Fellow, the Association for Computing Machinery (ACM), 2011; University Scholar (Illinois), 2010; Fellow, the Association for the Advancement of Artificial Intelligence (AAAI), 2009

Research

Dan Roth's research is in machine learning, natural language processing, knowledge representation and reasoning and learning theory, and he has developed advanced machine learning based tools for natural language applications that are being used widely by the research community. He  has given keynote talks in major conferences, including AAAI, The Conference of the American Association Artificial Intelligence; EMNLP, The Conference on Empirical Methods in Natural Language Processing, and ECML & PKDD, the European Conference on Machine Learning and the Principles and Practice of Knowledge Discovery in Databases. He has also presented several tutorials in universities and conferences including at ACL and the European ACL and has won several teaching and best paper awards.

Roth was the program chair of AAAI'11 and was the program chair of CoNLL'02 and of ACL'03; he has served as an area chair and senior program committee member on all major conferences in his research areas, and has been on the editorial board of several journals in his research areas. He is currently an associate editor for the Journal of Artificial Intelligence Research and the Machine Learning Journal.

Publications

  • 2016
    • Conde, A.; Larranaga, M.; Arruarte, A.; Elorriaga, J. A.; Roth, D., Litewi: A Combined Term Extraction and Entity Linking Method for Eliciting Educational Ontologies from Textbooks. Journal of the Association for Information Science and Technology 2016, 67, (2), 380-399.
    • Garg, P.; Neider, D.; Madhusudan, P.; Roth, D., Learning Invariants Using Decision Trees and Implication Counterexamples. ACM Sigplan Notices 2016, 51, (1), 499-512.
    • Plastaras, C.; McCormick, Z.; Nguyen, C.; Rho, M.; Nack, S. H.; Roth, D.; Casey, E.; Carneiro, K.; Cucchiara, A.; Press, J.; McLean, J.; Caldera, F., Is Hip Abduction Strength Asymmetry Present in Female Runners in the Early Stages of Patellofemoral Pain Syndrome? American Journal of Sports Medicine 2016, 44, (1), 105-112.
  • 2015
    • Vydiswaran, V. G. V.; Zhai, C.; Roth, D.; Pirolli, P., Overcoming Bias to Learn About Controversial Topics. Journal of the Association for Information Science and Technology 2015, DOI:10.1002/asi.23274.

    • Roy, S.; Viera, T.; Roth, D., Reasoning About Quantities in Natural Language. Transactions of the Association for Computational Linguistics 2015, 3, 1-13.

    • Kordjamshidi, P.; Roth, D.; Moens, M. F., Structured Learning for Spatial Information Extraction from Biomedical Text: Bacteria Biotopes. BMC Bioinformatics 2015, 16, DOI:ARTN 129 DOI:10.1186/S12859-015-0542-Z.

    • Connor, M.; Gertner, Y.; Fisher, C.; Roth, D., Starting from Scratch in Semantic Role Labeling, Proceedings of the 48th Annual Meeting Association for Computational Linguistics, 2010, 989-998.

    • Connor, M.; Fisher, C.; Roth, D., Starting from Scratch in Semantic Role Labeling: Early Indirect Supervision, In Cognitive Aspects of Computational Language Acquisition 2013, Villavicencio, A., Poibeau, T., Korhonen, A., Alishahi, A., Eds.; Springer-Verlag, New York.

    • Dugel, P. U.; Capone, A.; Singer, M. A.; Dreyer, R. F.; Dodwell, D. G.; Roth, D. B.; Shi, R.; Walt, J. G.; Scott, L. C.; Hollander, D. A.; Grp, S. S., Two or More Dexamethasone Intravitreal Implants in Treatment-Naive Patients with Macular Edema Due to Retinal Vein Occlusion: Subgroup Analysis of a Retrospective Chart Review Study. BMC Ophthalmology 2015, 15.
    • Finkelshtein, A.; Roth, D.; Ben Jacob, E.; Inghamd, C. J., Bacterial Swarms Recruit Cargo Bacteria to Pave the Way in Toxic Environments. Mbio 2015, 6, (3).
    • Kishon-Rabin, L.; Kuint, J.; Hildesheimer, M.; Roth, D. A. E., Delay in Auditory Behaviour and Preverbal Vocalization in Infants with Unilateral Hearing Loss. Developmental Medicine and Child Neurology 2015, 57, (12), 1129-1136.
    • Roth, D. A. E.; Karni, A.; Hildesheimer, M.; Kishon-Rabin, L., Asymmetric Interaural Generalization of Learning Gains in a Speech-in-Noise Identification Task. Journal of the Acoustical Society of America 2015, 138, (5), 2627-2634.
    • Singer, M. A.; Capone, A.; Dugel, P. U.; Dreyer, R. F.; Dodwell, D. G.; Roth, D. B.; Shi, R.; Walt, J. G.; Scott, L. C.; Hollander, D. A.; Grp, S. S., Two or More Dexamethasone Intravitreal Implants as Monotherapy or in Combination Therapy for Macular Edema in Retinal Vein Occlusion: Subgroup Analysis of a Retrospective Chart Review Study. BMC Ophthalmology 2015, 15.
    • Vydiswaran, V. G. V.; Zhai, C. X.; Roth, D.; Pirolli, P., Overcoming Bias to Learn About Controversial Topics. Journal of the Association for Information Science and Technology 2015, 66, (8), 1655-1672.
  • 2014
    • Bordes, A.; Bottou, L.; Collobert, R.; Roth, D.; Weston, J.; Zettlemoyer, L., Introduction to the Special Issue on Learning Semantics. Machine Learning 2014, 94, (2), 127-131, DOI: 10.1007/s10994-013-5381-4.

    • Goldwasser, D.; Roth, D., Learning from Natural Instructions. Machine Learning 2014, 94, (2), 205-232, DOI: 10.1007/s10994-013-5407-y.

  • 2013
    • Goldwasser, D.; Roth, D., Learning from Natural Instructions. Machine Learning Journal, Special Issue on Learning Semantics 2013, DOI: 10.1007/s10994-013-5381-

    • Jindal, P.; Roth, D., Using Domain Knowledge and Domain-Inspired Discourse Model for Coreference Resolution for Clinical Narratives. Journal of the American Medical Informatics Association 2013, 20, (2), 356-362.

    • Srikumar, V.; Roth, D., Modeling Semantic Relations Expressed by Prepositions. Transactions of the Association for Computational Linguistics 2013, 18, (2), 231-242.

    • Jindal, P.; Roth, D., Extraction of Events and Temporal Expressions from Clinical Narratives. Journal of Biomedical Informatics 2013, 46, S13-S19.

  • 2012
    • Chang, M. W.; Ratinov, L.; Roth, D., Structured Learning with Constrained Conditional Models. Machine Learning 2012, 88, (3), 399-431.

  • 2011
    • Mengshoel, O. J.; Roth, D.; Wilkins, D. C., Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks. Journal of Automated Reasoning 2011, 46, (2), 103-160.

    • Mengshoel, O. J.; Wilkins, D. C.; Roth, D., Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks. IEEE Transactions on Knowledge and Data Engineering 2011, 23, (2), 235-247.

  • 2009
    • Roth, D.; Samdani, R., Learning multi-linear representations of distributions for efficient inference. Machine Learning 2009, 76, (2-3), 195-209.

  • 2008
    • Bengtson, E.; Roth, D., Understanding the Value of Features for Coreference Resolution, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), 2008.

    • Connor, M.; Gertner, Y.; Fisher, C.; Roth, D., Baby SRL: Modeling Early Language Acquisition, Proceedings of the Annual Conference on Computational Natural Language Learning (CoNLL), 2008.

    • Daya, E.; Roth, D.; Wintner, S., Identifying Semitic roots: Machine learning with linguistic constraints. Computational Linguistics 2008, 34, (3), 429-448.

    • Punyakanok, V.; Roth, D.; Yih, W. T., The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics 2008, 34, (2), 257-287.

    • Srikumar, V.; Reichart, R.; Sammons, M.; Rappoport, A.; Roth, D., Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution, Proceedings of the Annual Meeting of the ACL  2008.

  • 2007
    • Zeng, Z. H.; Tu, J. L.; Liu, M.; Huang, T. S.; Pianfetti, B.; Roth, D.; Levinson, S., Audio-visual affect recognition. IEEE Transactions on Multimedia 2007, 9, (2), 424-428.

  • 2006
    • Mengshoel, O. J.; Wilkins, D. C.; Roth, D., Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering. Artificial Intelligence 2006, 170, (16-17), 1137-1174.

    • Roth, D.; Small, K., Margin-based active learning for structured output spaces. In Machine Learning: Ecml 2006, Proceedings 2006; Vol. 4212, pp 413-424.

    • Braz, R. D.; Girju, R.; Punyakanok, V.; Roth, D.; Sammons, M., An inference model for semantic entailment in natural language. Machine Learning Challenges 2006, 3944, 261-286.

Press

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