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Chris Callison-Burch: CV

(Last updated July 26, 2017)

Employment

Aravind K. Joshi Term Assistant Professor
University of Pennsylvania, Philadelphia, PA
September 2013-present
Associate Research Professor
Johns Hopkins University, Baltimore, MD
June 2010-August 2013
Assistant Research Professor
Johns Hopkins University, Baltimore, MD
June 2007-June 2010
Lead of Machine Translation Research Division
JHU Human Language Technology Center of Excellence (HLTCOE), Baltimore, MD
May 2010-August 2013
Founder / Director
Linear B, Ltd., Edinburgh, UK
October 2002-January 2009
Computational Linguist / Software Engineer
Amikai, Inc., San Francisco, CA
June 2000-September 2001

Education

PhD in Informatics
University of Edinburgh, Edinburgh, UK
Thesis:Paraphrasing and Translation.
Advisors:Miles Osborne and Mark Steedman.
February 2008
M.S. with Distinction in Computer Science
University of Edinburgh, Edinburgh, UK
Thesis:Co-Training for Statistical Machine Translation.
Advisors:Miles Osborne.
October 2002
B.S. with Honors in Symbolic Systems
Stanford University, Palo Alto, CA
Thesis:A Computer Model of a Grammar for English Questions.
Advisors:Ivan Sag.
June 2000

Teaching

Data Structures and Algorithms (CIS 121)
University of Pennsylvania
Fall 2016
This course introduces undergraduates to the fundamental algorithms and data structures that are used in the field of computer science. We introduce methods for analyzing the complexity of an algorithm and predicting the running time of software. We describe data structures like stacks, queues, lists, trees, priority queues, maps, hash tables and graphs, and we discuss how to implement them efficiently and how to use them effectively in software. Course assignments include of weekly assignments consisting of problem sets and programming exercises in Java, and a larger term project.
Crowdsourcing and Human Computation (NETS 213)
University of Pennsylvania
Spring 2016
Crowdsourcing and human computation are emerging fields that sit squarely at the intersection of economics and computer science. They examine how people can be used to solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms. Online marketplaces like Mechanical Turk provide an infrastructure that allows micropayments to be given to people in return for completing human intelligence tasks. This opens up previously unthinkable possibilities like people being used as function calls in software. We will investigate how crowdsourcing can be used for computer science applications like machine learning, next-generation interfaces, and data mining. Beyond these computer science aspects, we will also delve into topics like prediction markets, how businesses can capitalize on collective intelligence, and the fundamental principles that underly democracy and other group decision-making processes.
Data Structures and Algorithms (CIS 121)
University of Pennsylvania
Fall 2015
Machine Translation (CIS 526)
University of Pennsylvania
Spring 2015
Google translate can instantly translate between any pair of over fifty human languages (for instance, from French to English). How does it do that? Why does it make the errors that it does? And how can you build something better? Modern translation systems like Google Translate and Bing Translator learn how to translate by reading millions of words of already translated text, and this course will show you how they work. The course covers a diverse set of fundamental building blocks from linguistics, machine learning, algorithms, data structures, and formal language theory, along with their application to a real and difficult problem in artificial intelligence.
Crowdsourcing and Human Computation (NETS 213)
University of Pennsylvania
Fall 2014
Machine Translation (CIS 526)
University of Pennsylvania
Spring 2014
Crowdsourcing and Human Computation (CIS 399)
University of Pennsylvania
Fall 2013
Machine Translation (600.466) co-taught with Adam Lopez and Matt Post
Johns Hopkins University
Spring 2012
Seminar on the Meaning, Translation and Generation of Text (600.466) co-taught with Ben Van Durme
Johns Hopkins University
Fall and Spring 2011 and 2012
Seminar in Machine Translation (600.466)
Johns Hopkins University
Fall and Spring 2007-2010

Teaching Reviews at Penn

Quality scale (0-4): 0=Poor, 1=Fair, 2=Good, 3=Very Good, 4=Excellent

Term Course Title (Number) Students Enrolled Course Quality Instructor Quality
Fall 2016 Data Structures and Algorithms (CIS 121) 219 2.5 2.2
Spring 2016 Crowdsourcing and Human Computation (NETS 213) 113 2.4 2.8
Fall 2015 Data Structures and Algorithms (CIS 121) 174 2.2 2.2
Spring 2015 Machine Translation (CIS 526) 51 2.9 3.2
Fall 2014 Crowdsourcing and Human Computation (NETS 213) 48 3.2 3.6
Spring 2014 Machine Translation (CIS 526) 25 3.3 3.5
Fall 2013 Crowdsourcing and Human Computation (CIS 399) 26 3.1 3.5

Grants

Current grants

Grant Title Awarding Body Amount Dates PI Info
CI-NEW: NIEUW: Novel Incentives and Workflows in Linguistics Data Collection NSF $1,218,465 July 14, 2017-June 30, 2020 co-PI with Christopher Cieri and Mark Liberman
DEFT Extension DARPA $116,000 2017-2017 PI with Ben Van Durme
Amazon Web Services supplimental grant to Amazon Academic Research Award Amazon $40,000 2016 PI
Low Resource Machine Translation via Matrix Factorization (Amazon Academic Research Awards) Amazon $68,000 2016-2017 PI
Learning translations from monolingual texts (LORELEI) DARPA $478,000 2015-2019 PI at Penn
SIREN-IL: Specialized Intra/Interlingual Resources for Emergent News - Incident Language DARPA $3,031,412 2015-2017 co-PI with Stephanie Strassel
Large-scale Paraphrasing for Natural Language Understanding (DEFT) DARPA $1,600,000 2012-2017 PI with Ben Van Durme

Pending grants

Grant Title Awarding Body Amount Dates PI Info
AIDA Populating Knowledge Bases Using Streaming Multi-Modal Information (submitted) DAPRA $4,069,241 2017-2022 PI

Past grants

Grant Title Awarding Body Amount Dates PI Info
CI-P: Planning for Scalable Language Resource Creation through Novel Incentives and Crowdsourcing NSF $100,000 2016-2017 co-PI with Christopher Cieri and Mark Liberman
Natural Logic Solver for Aristo Allen Institute for Artificial Intelligence (AI2) $95,000 2015-2016 PI
EAGER: Simplification as Machine Translation NSF $100,000 2014-2015 PI
Unsolicited Gift Facebook $50,000 2014
Sloan Research Fellowship Alfred P. Sloan Foundation $50,000 2014
Google Faculty Research Award (Learning Paraphrases from Large, Diverse Data Sets) Google $62,000 2013
EAGER: Combining natural language inference and data-driven paraphrasing NSF $100,000 2012-2013 co-PI with Ben Van Durme
Crowdsourcing Translation (Computer Science Study Panel phase 3) DARPA $500,000 2012-2015 PI
Improved Arabic dialect translation through Crowdsourcing DARPA $176,000 2012-2013 PI
Acquisition and use of paraphrases in a knowledge-rich setting Vulcan $260,000 2011-2013 co-PI with Ben Van Durme
Google Faculty Research Award (Translate the World: A Unified Framework for Crowdsourcing Translation) Google $150,000 2011 co-PI with Philip Resnik and Ben Bederson
Crowdsourcing Translation Microsoft $25,000 2011
RI:Medium: Semi-supervised Discriminative Training of Sequence Transduction Model NSF $800,000 2011-2015 co-PI with Sanjeev Khudanpur, Brian Roark, Damianos Karakos, Richard Sproat
Translation of informal texts via Mechanical Turk BBN Technologies $144,000 2010-2011 PI
BABEL: Bayesian Architecture Begetting Every Language (Computer Science Study Panel phase 2) DARPA $500,000 2010-2012 PI
Google Faculty Research Award (The Babel Challenge: Translating all the World’s Languages) Google $45,000 2010 co-PI with Miles Osborne
EuroMatrixPlus: Bringing Machine Translation for European Languages to the User European Union Framework 7 Programme €4,950,000 2009-2012 PI at JHU (JHU Amount: €516,000)
Global Autonomous Language Exploitation (GALE) project, Periods 3 and 4 DARPA $575,000 2009-2011 co-PI with Sanjeev Khudanpur and Damianos Karakos
Computer Science Study Panel DARPA $93,000 2008-2009 PI
RI:SMALL: Multi-Level Modeling of Language and Translation NSF $400,000 2007-2012 co-PI with David Yarowsky
SMART:Scotland Technology Innovation Grant British Government £45,000 2002-2005

Publications

Refereed conference papers (most have acceptance rates ≈ 25%)

  1. Derry Wijaya, Brendan Callahan, John Hewitt, Jie Gao, Xiao Ling, Marianna Apidianaki and Chris Callison-Burch (2017). Learning Translations via Matrix Completion. EMNLP. 12 pages.
  2. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2017). Mapping the Paraphrase Database to WordNet. STARSEM.
  3. Sneha Rajana, Chris Callison-Burch, Marianna Apidianaki and Vered Shwartz (2017). Learning Antonyms with Paraphrases and a Morphology-aware Neural Network. STARSEM.
  4. Ann Cocos and Chris Callison-Burch (2017). The Language of Place: Semantic Value from Geospatial Context. EACL short papers. 5 pages.
  5. Ellie Pavlick, Heng Ji, Xiaoman Pan and Chris Callison-Burch (2016). The Gun Violence Database: A new task and data set for NLP. EMNLP short papers. 6 pages.
  6. Ellie Pavlick and Chris Callison-Burch (2016). Tense Manages to Predict Implicative Behavior in Verbs. EMNLP short papers. 5 pages.
  7. Ellie Pavlick and Chris Callison-Burch (2016). So-Called Non-Subsective Adjectives. STARSEM. Best Paper Award. 6 pages.
  8. Ellie Pavlick and Chris Callison-Burch (2016). Most babies are little and most problems are huge: Compositional Entailment in Adjective-Nouns. ACL. 11 pages.
  9. Ellie Pavlick and Chris Callison-Burch (2016). Simple PPDB: A Paraphrase Database for Simplification. ACL short papers. 6 pages.
  10. Anne Cocos and Chris Callison-Burch (2016). Clustering Paraphrases by Word Sense. NAACL. 10 pages.
  11. Courtney Napoles, Chris Callison-Burch, and Matt Post (2016). Sentential Paraphrasing as Black-Box Machine Translation. NAACL short papers. 5 pages.
  12. Ellie Pavlick, Johan Bos, Malvina Nissim, Charley Beller, Benjamin Van Durme, and Chris Callison-Burch (2015). Adding Semantics to Data-Driven Paraphrasing. ACL. 10 pages.
  13. Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevich, Ben Van Durme, Chris Callison-Burch (2015). PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. ACL short papers. 6 pages.
  14. Ellie Pavlick, Juri Ganitkevich, Tsz Ping Chan, Xuchen Yao, Ben Van Durme, Chris Callison-Burch (2015). Domain-Specific Paraphrase Extraction. ACL short papers. 6 pages.
  15. Ellie Pavlick, Travis Wolfe, Pushpendre Rastogi, Chris Callison-Burch, Mark Drezde, Ben Van Durme (2015). FrameNet+: Fast Paraphrastic Tripling of FrameNet. ACL short papers. 6 pages.
  16. Mingkun Gao, Wei Xu, and Chris Callison-Burch (2015). Cost Optimization for Crowdsourcing Translation. NAACL. 9 pages.
  17. Heba Elfardy, Mona Diab and Chris Callison-Burch (2015). Ideological Perspective Detection Using Semantic Features. STARTSEM. 10 pages.
  18. Ann Irvine and Chris Callison-Burch (2014). Hallucinating Phrase Translations for Low Resource MT. CoNLL. 11 pages.
  19. Rui Yan, Mingkun Gao, Ellie Pavlick, and Chris Callison-Burch (2014). Are Two Heads are Better than One? Crowdsourced Translation via a Two-Step Collaboration between Translators and Editors. ACL. 11 pages.
  20. Jonathan Weese, Juri Ganitkevitch, and Chris Callison-Burch (2014). PARADIGM: Paraphrase Diagnostics through Grammar Matching. EACL. 10 pages.
  21. Ellie Pavlick, Rui Yan, and Chris Callison-Burch (2014). Crowdsourcing for Grammatical Error Correction. CSCW Poster. 4 pages.
  22. Juri Ganitkevitch and Chris Callison-Burch (2014). The Multilingual Paraphrase Database. LREC. 8 pages.
  23. Ann Irvine, Joshua Langfus, and Chris Callison-Burch (2014). The American Local News Corpus. LREC. 4 pages.
  24. Ryan Cotterell and Chris Callison-Burch (2014). A Multi-Dialect, Multi-Genre Corpus of Informal Written Arabic. LREC. 5 pages.
  25. Xuchen Yao, Ben Van Durme, Chris Callison-Burch and Peter Clark (2013). Semi-Markov Phrase-based Monolingual Alignment. EMNLP. 11 pages.
  26. Xuchen Yao, Peter Clark, Ben Van Durme and Chris Callison-Burch (2013). A Lightweight and High Performance Monolingual Word Aligner. ACL short papers. 6 pages.
  27. Travis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu and Xuchen Yao (2013). PARMA: A Predicate Argument Aligner. ACL short papers. 6 pages.
  28. Jason Smith, Herve Saint-Amand, Magdalena Plamada, Philipp Koehn, Chris Callison-Burch and Adam Lopez (2013). Dirt Cheap Web-Scale Parallel Text from the Common Crawl. ACL. 10 pages.
  29. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2013). PPDB: The Paraphrase Database. NAACL short papers. 7 pages.
  30. Ann Irvine and Chris Callison-Burch (2013). Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals. NAACL short papers. 6 pages.
  31. Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch and Peter Clark (2013). Answer Extraction as Sequence Tagging with Tree Edit Distance. NAACL. 10 pages.
  32. Xuchen Yao, Benjamin Van Durme and Chris Callison-Burch (2012). Expectations of Word Sense in Parallel Corpora. NAACL short papers. 5 pages.
  33. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch (2012). Monolingual Distributional Similarity for Text-to-Text Generation. STARSEM. 9 pages.
  34. Rabih Zbib, Erika Malchiodi, Jacob Devlin, David Stallard, Spyros Matsoukas, Richard Schwartz, John Makhoul, Omar F. Zaidan and Chris Callison-Burch (2012). Machine Translation of Arabic Dialects. NAACL. 11 pages.
  35. Alex Klementiev, Ann Irvine, Chris Callison-Burch, and David Yarowsky (2012). Toward Statistical Machine Translation without Parallel Corpora. EACL. 11 pages.
  36. Juri Ganitkevitch, Chris Callison-Burch, Courtney Napoles, and Benjamin Van Durme (2011). Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation. EMNLP. 12 pages.
  37. Omar Zaidan and Chris Callison-Burch (2011). The Arabic Online Commentary Dataset: An Annotated Dataset of Informal Arabic with High Dialectal Content. ACL short papers. 5 pages.
  38. Omar Zaidan and Chris Callison-Burch (2011). Crowdsourcing Translation: Professional Quality from Non-Professionals. ACL. 10 pages.
  39. Lane Schwartz, Chris Callison-Burch, William Schuler and Stephen Wu (2011). Incremental Syntactic Language Models for Phrase-based Translation. ACL. 12 pages.
  40. Omar Zaidan and Chris Callison-Burch (2010). Predicting Human-Targeted Translation Edit Rate via Untrained Human Annotators. NAACL short papers. 4 pages.
  41. Kathryn Baker, Michael Bloodgood, Chris Callison-Burch, Bonnie Dorr, Scott Miller, Christine Piatko, Nathaniel W. Filardo, and Lori Levin (2010). Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach. AMTA. 10 pages.
  42. Ann Irvine, Alex Klementiev, and Chris Callison-Burch (2010). Transliterating From All Languages. AMTA. 8 pages.
  43. Michael Bloodgood and Chris Callison-Burch (2010). Large-Scale, Cost-Focused Active Learning for Statistical Machine Translation. ACL. 11 pages.
  44. Abby Levenberg, Chris Callison-Burch, and Miles Osborne (2010). Stream-based Translation Models for Statistical Machine Translation. NAACL. 9 pages.
  45. Scott Novotney and Chris Callison-Burch (2010). Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription. NAACL. 9 pages.
  46. Chris Callison-Burch (2009). Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk. EMNLP. 10 pages.
  47. Omar Zaidan and Chris Callison-Burch (2009). Feasibility of Human-in-the-loop Minimum Error Rate Training. EMNLP. 10 pages.
  48. Yuval Marton, Chris Callison-Burch and Philip Resnik (2009). Improved Statistical Machine Translation Using Monolingually-Derived Paraphrases. EMNLP. 10 pages.
  49. Nikesh Garera, Chris Callison-Burch and David Yarowsky (2009). Improving Translation Lexicon Induction from Monolingual Corpora via Dependency Contexts and Part-of-Speech Equivalences. CoNLL. 9 pages.
  50. Chris Callison-Burch (2008). Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. EMNLP. 10 pages.
  51. Chris Callison-Burch, Trevor Cohn, Mirella Lapata (2008). ParaMetric: An Automatic Evaluation Metric for Paraphrasing. CoLing. 8 pages.
  52. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst (2007). Moses: Open source toolkit for statistical machine translation. ACL.
  53. Chris Callison-Burch, Philipp Koehn and Miles Osborne (2006). Improved Statistical Machine Translation Using Paraphrases. NAACL.
  54. Chris Callison-Burch, Miles Osborne and Philipp Koehn (2006). Re-evaluating the Role of Bleu in Machine Translation Research. EACL. 8 pages.
  55. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). Scaling Phrase-Based Statistical Machine Translation to Larger Corpora and Longer Phrases. ACL.
  56. Colin Bannard and Chris Callison-Burch (2005). Paraphrasing with Bilingual Parallel Corpora. ACL.
  57. Chris Callison-Burch, David Talbot and Miles Osborne (2004). Statistical Machine Translation with Word- and Sentence-Aligned Parallel Corpora. ACL.
  58. Chris Callison-Burch and Raymond Flournoy (2001). A program for automatically selecting the best output from multiple machine translation engines. MT Summit.

Journal articles

  1. Anne Cocos, Ting Qiana, Chris Callison-Burch, and Aaron J. Masino (2017). Crowd Control: Effectively Utilizing Unscreened Crowd Workers for Biomedical Data Annotation. Journal of Biomedical Informatics. 22 pages.
  2. Wei Xu, Courtney Napoles, Ellie Pavlick, Jim Chen, and Chris Callison-Burch (2016). Optimizing Statistical Machine Translation for Text Simplification. TACL. 15 pages.
  3. Ann Irvine and Chris Callison-Burch (2016). A Comprehensive Analysis of Bilingual Lexicon Induction. Computational Linguistics. 38 pages.
  4. Ann Irvine and Chris Callison-Burch (2016). End-to-End Statistical Machine Translation with Zero or Small Parallel Texts. Journal of Natural Language Engineering. 34 pages.
  5. Wei Xu, Chris Callison-Burch, and Courtney Napoles (2015). Problems in Current Text Simplification Research: New Data Can Help. TACL. 16 pages.
  6. Omar Zaidan and Chris Callison-Burch (2014). Arabic Dialect Identification. Computational Linguistics. 36 pages.
  7. Wei Xu, Alan Ritter, Chris Callison-Burch, William B. Dolan and Yangfeng Ji (2014). Extracting Lexically Divergent Paraphrases from Twitter. TACL. 14 pages.
  8. Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev, and Chris Callison-Burch (2014). The Language Demographics of Amazon Mechanical Turk. TACL. 13 pages.
  9. Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, Lin Yang, and Shang Zhao (2013). Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT. TACL. 13 pages.
  10. Kathryn Baker, Bonnie Dorr, Michael Bloodgood, Chris Callison-Burch, Wes Filardo, Christine Piatko, Lori Levin, and Scott Miller (2012). Use of Modality and Negation in Semantically-Informed Syntactic MT. Computational Linguistics. 28 pages.
  11. Ann Irvine, Mike Kayser, Zhifei Li, Wren Thornton, and Chris Callison-Burch (2010). Integrating Output from Specialized Modules in Machine Translation: Transliteration in Joshua. PBML. 10 pages.
  12. Jonathan Weese and Chris Callison-Burch (2010). Visualizing Data Structures in Parsing-Based Machine Translation. PBML. 10 pages.
  13. Lane Schwartz and Chris Callison-Burch (2010). Hierarchical Phrase-Based Grammar Extraction in Joshua: Suffix Arrays and Prefix Trees. PBML. 10 pages.
  14. Zhifei Li, Chris Callison-Burch, Sanjeev Khudanpur, and Wren Thornton (2009). Decoding in Joshua: Open Source, Parsing-Based Machine Translation. PBML. 10 pages.
  15. Trevor Cohn, Chris Callison-Burch, Mirella Lapata (2008). Constructing Corpora for the Development and Evaluation of Paraphrase Systems. Computational Linguistics. 18 pages.

Book chapters

  1. Wauter Bosma and Chris Callison-Burch (2007). Paraphrase Substitution for Recognizing Textual Entailment. Evaluation of Multilingual and Multimodalformation Retrieval, Lecture Notes in Computer Science, C Peters et al editors.
  2. Chris Callison-Burch and Miles Osborne (2003). Statistical Natural Language Processing. A Handbook for Language Engineers, Ali Farghaly, Editor.

Refereed workshop papers

  1. Anne Cocos, Marianna Apidianaki and Chris Callison-Burch (2017). Word Sense Filtering Improves Embedding-Based Lexical Substitution. Workshop on Sense, Concept and Entity Representations and their Applications. Best Paper Award. 9 pages.
  2. Ellie Pavlick and Chris Callison-Burch (2016). The Gun Violence Database. Bloomberg Data for Good Exchange. 6 pages.
  3. Wei Xu, Chris Callison-Burch, and Bill Dolan (2015). SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter. SemEval. 11 pages.
  4. Anne Cocos, Aaron J. Masino, Ting Qian, Ellie Pavlick, and Chris Callison-Burch (2015). Effectively Crowdsourcing Radiology Report Annotations. Sixth International Workshop on Health Text Mining and Information Analysis. 6 pages.
  5. Courtney Napoles and Chris Callison-Burch (2015). Automatically Scoring Freshman Writing: A Preliminary Investigation. Workshop on Innovative Use of NLP for Building Educational Applications. 10 pages.
  6. Ellie Pavlick and Chris Callison-Burch (2015). Extracting Structured Information via Automatic + Human Computation. HCOMP. 2 pages.
  7. Gaurav Kumar, Yuan Cao, Ryan Cotterell, Chris Callison-Burch, Daniel Povey, and Sanjeev Khudanpur (2014). Translations of the CALLHOME Egyptian Arabic corpus for conversational speech translation. IWSLT. 5 pages.
  8. Quanze Chen, Chenyang Lei, Wei Xu, Ellie Pavlick and Chris Callison-Burch (2014). Poetry of the Crowd: A Human Computation Algorithm to Convert Prose into Rhyming Verse. HCOMP Poster. 3 pages.
  9. Chris Callison-Burch (2014). Crowd-Workers: Aggregating Information Across Turkers To Help Them Find Higher Paying Work. HCOMP Poster. 2 pages.
  10. Ann Irvine and Chris Callison-Burch (2014). Using Comparable Corpora to Adapt MT Models to New Domains. WMT. 8 pages.
  11. Ryan Cotterell, Adithya Renduchintala, Naomi Saphra, and Chris Callison-Burch (2014). An Algerian Arabic-French Code-Switched Corpus. LREC Workshop on Free/Open-Source Arabic Corpora and Corpora Processing Tools. 4 pages.
  12. Matt Post, Gaurav Kumar, Adam Lopez, Damianos Karakos, Chris Callison-Burch and Sanjeev Khudanpur (2013). Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus. IWSLT. 7 pages.
  13. Ondrej Bojar, Christian Buck, Chris Callison-Burch, Christian Federmann, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia (2013). Findings of the 2013 Workshop on Statistical Machine Translation. WMT. 44 pages.
  14. Matt Post, Juri Ganitkevitch, Luke Orland, Jonathan Weese, Yuan Cao, and Chris Callison-Burch (2013). Joshua 5.0: Sparser, better, faster, server. WMT. 7 pages.
  15. Ann Irvine and Chris Callison-Burch (2013). Combining Bilingual and Comparable Corpora for Low Resource Machine Translation. WMT. 9 pages.
  16. Chris Callison-Burch, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut, and Lucia Specia (2012). Findings of the 2012 Workshop on Statistical Machine Translation. WMT. 42 pages.
  17. Matt Post, Chris Callison-Burch, and Miles Osborne (2012). Constructing Parallel Corpora for Six Indian Languages via Crowdsourcing. WMT. 9 pages.
  18. Jonathan Weese, Chris Callison-Burch, and Adam Lopez (2012). Using Categorial Grammar to Label Translation Rules. WMT. 10 pages.
  19. Juri Ganitkevitch, Yuan Cao, Jonathan Weese, Matt Post, and Chris Callison-Burch (2012). Joshua 4.0: Packing, PRO, and Paraphrases. WMT. 9 pages.
  20. Ann Irvine, Jonathan Weese, and Chris Callison-Burch (2012). Processing Informal, Romanized Pakistani Text Messages. the NAACL Workshop on Language in Social Media. 4 pages.
  21. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Omar Zaidan (2011). Findings of the 2011 Workshop on Statistical Machine Translation. WMT. 43 pages.
  22. Charley Chan, Chris Callison-Burch, and Benjamin Van Durme (2011). Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity. GEMS. 10 pages.
  23. Jonathan Weese, Juri Ganitkevitch, Chris Callison-Burch, Matt Post and Adam Lopez (2011). Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor. WMT. 7 pages.
  24. Byung Gyu Ahn, Ben Van Durme and Chris Callison-Burch (2011). WikiTopics: What is Popular on Wikipedia and Why. ACL Workshop on Automatic Summarization for Different Genres, Media, and Languages. 8 pages.
  25. Courtney Napoles, Ben Van Durme (2011). Evaluating sentence compression: Pitfalls and suggested remedies. Workshop on Monolingual Text-To-Text Generation. 7 pages.
  26. Courtney Napoles, Chris Callison-Burch, Juri Ganitevitch, Ben Van Durme (2011). Paraphrastic Sentence Compression with a Character-based Metric: Tightening without Deletion. Workshop on Monolingual Text-To-Text Generation. 7 pages.
  27. Rui Wang and Chris Callison-Burch (2011). Paraphrase Fragment Extraction from Monolingual Comparable Corpora. BUCC. 9 pages.
  28. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Ann Irvine, Lane Schwartz, Wren N. G. Thornton, Ziyuan Wang, Jonathan Weese and Omar F. Zaidan (2010). Joshua 2.0: A Toolkit for Parsing-Based Machine Translationwith Syntax, Semirings, Discriminative Training and Other Goodies. WMT. 5 pages.
  29. Chris Callison-Burch, Philipp Koehn, Christof Monz, Kay Peterson, Mark Przybocki, Omar Zaidan (2010). Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation. WMT. 33 pages.
  30. Chris Callison-Burch and Mark Dredze (2010). Creating Speech and Language Data With Amazon’s Mechanical Turk. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk. 12 pages.
  31. Michael Bloodgood and Chris Callison-Burch (2010). Using Mechanical Turk to Build Machine Translation Evaluation Sets. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk. 4 pages.
  32. Scott Novotoney and Chris Callison-Burch (2010). Crowdsourced Accessibility: Elicitation of Wikipedia Articles. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk. 4 pages.
  33. Rui Wang and Chris Callison-Burch (2010). Cheap Facts and Counter-Facts. NAACL Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk. 5 pages.
  34. Chris Callison-Burch, Philipp Koehn, Christof Monz and Josh Schroeder (2009). Findings of the 2009 Workshop on Statistical Machine Translation. WMT. 28 pages.
  35. Zhifei Li, Chris Callison-Burch, Chris Dyer, Juri Ganitkevitch, Sanjeev Khudanpur, Lane Schwartz, Wren Thornton, Jonathan Weese and Omar Zaidan (2009). Joshua: An Open Source Toolkit for Parsing-based Machine Translation. WMT. 5 pages.
  36. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2008). Further Meta-Evaluation of Machine Translation. WMT. 37 pages.
  37. Delip Rao, David Yarowsky, Chris Callison-Burch (2008). Affinity Measures based on the Graph Laplacian. of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing at CoLing. 8 pages.
  38. Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz and Josh Schroeder (2007). (Meta-) Evaluation of Machine Translation. WMT.
  39. Philipp Koehn, Nicola Bertoldi, Ondrej Bojar, Chris Callison-Burch, Alexandra Constantin, Brooke Cowan, Chris Dyer, Marcello Federico, Evan Herbst, Hieu Hoang, Christine Moran, Wade Shen, and Richard Zens (2007). Open Source Toolkit for Statistical Machine Translation: Factored Translation Models and Confusion Network Decoding. CLSP Summer Workshop Final Report WS, Johns Hopkins University.
  40. Alexandra Birch, Chris Callison-Burch and Miles Osborne (2006). Constraining the Phrase-Based, Joint Probability Statistical Translation Model. WMT.
  41. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2005). A Compact Data Structure for Searchable Translation Memories. EAMT.
  42. Chris Callison-Burch (2005). Linear B System Description for the 2005 NIST MT Evaluation Exercise. Machine Translation Evaluation Workshop.
  43. Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, Chris Callison-Burch, Miles Osborne, and David Talbot (2005). Edinburgh System Description for the 2005 IWSLT Speech Translation Evaluation. IWSLT.
  44. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Searchable Translation Memories. ASLIB Translating and the Computer.
  45. Chris Callison-Burch, Colin Bannard and Josh Schroeder (2004). Improved Statistical Translation Through Editing. EAMT.
  46. Chris Callison-Burch and Miles Osborne (2003). Bootstrapping Parallel Corpora. NAACL workshop Building and Using Parallel Texts. 6 pages.
  47. Chris Callison-Burch and Miles Osborne (2003). Co-training for Statistical Machine Translation. the 6th Annual CLUK Research Colloquium.
  48. Jochen Leidner and Chris Callison-Burch (2003). Evaluating Question Answering Systems Using FAQ Answer Injection. the 6th Annual CLUK Research Colloquium.
  49. Chris Callison-Burch (2001). Upping the Ante for "Best of Breed" Machine Translation Providers. ASLIB Translating and the Computer.
  50. Raymond Flournoy and Chris Callison-Burch (2001). Secondary Benefits of Feedback and User Interaction in Machine Translation Tools. MT Summit Workshop.

Theses

  1. Chris Callison-Burch (2007). Paraphrasing and Translation. PhD Thesis, University of Edinburgh.
  2. Chris Callison-Burch (2002). Co-Training for Statistical Machine Translation. Master's thesis, School offormatics, University of Edinburgh.
  3. Chris Callison-Burch (2000). A Computer Model of a Grammar for English Questions. Undergraduate thesis, Symbolic Systems Program, Stanford University. 78 pages.

Invited Talks

  1. NYU. The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences. February 2, 2017
  2. Columbia University. Large-scale Paraphrasing for Natural Language Generation. September 19, 2016
  3. Cornell University. Large-scale Paraphrasing for Natural Language Generation. September 9, 2016
  4. University of Alabama at Birmingham. Crowdsourcing Translation. April 17, 2015
  5. Drexel University. Crowdsourcing Translation. November 8, 2015
  6. CMU. Crowdsourcing Translation. April 7, 2015
  7. UC Berkeley. Large-scale Paraphrasing for Natural Language Generation. March 12, 2015
  8. Stanford. Crowdsourcing Translation. March 11, 2015
  9. Facebook. Crowdsourcing Translation. March 10, 2015
  10. Coursera. Crowdsourcing Translation. March 9, 2015
  11. Google. Large-scale Paraphrasing for Natural Language Generation. March 9, 2015
  12. MIT. Large-scale Paraphrasing for Natural Language Generation. January 14, 2015
  13. CMU. Large-scale Paraphrasing for Natural Language Generation. November 21, 2014
  14. Microsoft Research. Large-scale Paraphrasing for Natural Language Generation. October 3, 2014
  15. University of Washington, Seattle. Large-scale Paraphrasing for Natural Language Generation. October 2, 2014
  16. The Allen Institute for Artificial Intelligence (AI2). Large-scale Paraphrasing for Natural Language Generation. October 1, 2014
  17. Yahoo! Research Labs. Large-scale Paraphrasing for Natural Language Generation. July 29, 2014
  18. US Army Research Labs DARPA Computer Science Study Group Applied Research Series: Text and Video Analytics Workshop. Language Understanding with the Help of Images. July 16, 2014
  19. International Conference on Natural Language Generation. Large-scale Paraphrasing for Natural Language Generation. June 21, 2014
  20. LREC Workshop on Building and Using Comparable Corpora. Crowdsourcing Translation. May 27, 2014
  21. University of Maryland. Large-scale Paraphrasing for Natural Language Understanding and Generation. April 23, 2014
  22. Institute for Research in Cognitive Science, University of Pennsylvania. Large-scale Paraphrasing for Natural Language Understanding and Generation. March 21, 2014
  23. 37th Annual Penn Linguistics Colloquium, University of Pennsylvania. The Wisdom of Crowdsourcing. March 22, 2013
  24. Johns Hopkins University. Advances to machine translation and language understanding. February 15, 2013
  25. Columbia University IGERT distinguished speaker series. The Promise of Crowdsourcing for NLP and other data sciences. March 29, 2013
  26. UT Austin. Large-scale Paraphrasing for Natural Language Understanding and Generation. December 7, 2012
  27. IBM TJ Watson Research Center. Large-scale Paraphrasing for Natural Language Understanding and Generation. November 9, 2012
  28. NSF-sponsored Workshop on summarizing speaker's attitude and opinion in conversational speech. When annotation with MTurk works. October 20, 2012
  29. Linguistics Data Consortium 20th Anniversary Workshop. The Promise of Crowdsourcing. September 6, 2012
  30. Human Language Technology Center of Excellence. Machine Translation of Arabic Dialects. April 3, 2012.
  31. Human Language Technology Center of Excellence. Machine Translation at the HLTCOE. March 29, 2012
  32. University of Pennsylvania. Advances to machine translation and language understanding. February 28, 2012
  33. Carnegie Mellon University. Advances to machine translation and language understanding. February 21, 2012
  34. Microsoft Research. Crowdsourcing Translation: Professional Quality from Non-Professionals. June 16, 2011
  35. Human Language Technology Center of Excellence. Statistical Machine Translation and Crowdsourcing. March 24, 2011.
  36. CrowdFlower Meetup, Washington DC. Crowdsourcing Translation with Amazon's Mechanical Turk. July 25, 2010
  37. Human Language Technology Center of Excellence. Crowdsourcing Translation with Amazon's Mechanical Turk. November 23, 2010
  38. AAAI Panel on Common Sense Knowledge. Automatic versus Manual Construction of Common Sense Knowledge. November 13, 2010
  39. UMass Amherst. Crowdsourcing Translation with Amazon's Mechanical Turk. October 18, 2010
  40. Brown University. Syntactic Parsing and Machine Translation. September 9, 2010
  41. IARPA. Crowdsourcing Translation. August 5, 2010
  42. UMD Workshop on Crowdsourcing Translation. Crowdsourcing Translation with Amazon's Mechanical Turk. June 10, 2010
  43. BBN. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. March 11, 2010
  44. BBN. Improvements to Urdu-English: SCALE Summer Workshop Results. March 11, 2010
  45. University of Washington. Syntactic translation models help for low-resource, verb final languages. February 25, 2010
  46. Microsoft Research. Syntactic translation models help for low-resource, verb final languages. February 24, 2010
  47. University of Pennsylvania. Syntactic translation models help for low-resource, verb final languages. February 8, 2010
  48. NIST. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 18, 2009
  49. University of Maryland. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. December 2, 2009.
  50. OHSU, Center for Spoken Language Understanding. Fast, Cheap and Creative: Evaluating Translation Quality with Amazon’s Mechanical Turk. October 7, 2009
  51. OHSU, Center for Spoken Language Understanding. Improvements to Urdu-English: SCALE Summer Workshop Results. October 5, 2009
  52. University of Pennsylvania. Syntactic Constraints on Paraphrases Extracted from Parallel Corpora. April 13, 2009
  53. University of Maryland. Paraphrasing and Translation. November 28, 2007.
  54. Johns Hopkins University. Improving Statistical Machine Translation With Paraphrases and Generalization. December 5, 2006
  55. Johns Hopkins University. Factored Translation Models. November 28, 2006
  56. University of Pennsylvania. Factored Translation Models. August 23, 2006
  57. Carnegie Mellon University. Statistical Machine Translation Using Semi-Supervised Learning. April 18, 2005.

Academic Service

Current PhD Students and Postdocs

  1. Marianna Apidianaki, Visiting Scholar, Centre national de la recherche scientifique.
  2. Anietie Andy, Postdoc, University of Pennsylvania (PhD from Howard University).
  3. Derry Wijaya, Postdoc, University of Pennsylvania (PhD from CMU).
  4. Jie Gao, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2021
  5. Reno Kriz, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2021
  6. Anne Cocos, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2019
  7. Ellie Pavlick, PhD Student, University of Pennsylvania. Expected graduation date: Summer 2017
  8. Juri Ganitkevitch, PhD Student, Johns Hopkins University. Expected graduation date: Fall 2017
  9. Courtney Napoles, PhD Student, Johns Hopkins University. Expected graduation date: Summer 2017
  10. Jonny Weese, PhD Student, Johns Hopkins University. Expected graduation date: Fall 2017

PhDs Graduated

  1. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
  2. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
  3. Omar Zaidan, Johns Hopkins University (advisor: Chris Callison-Burch), "Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks", April 2012.
  4. Lane Schwartz, University of Minnesota (advisors: William Schuler and Chris Callison-Burch), "An Incremental Syntactic Language Model for Statistical Phrase-based Translation", February 2012.

Thesis Committees

  1. Wang Ling, Carnegie Mellon University (advisors: Alan Black, Isabel Trancoso, Chris Dyer, and Luísa Coheur), "Machine Translation 4 Microblogs", October 2015.
  2. Ann Irvine, Johns Hopkins University (advisor: Chris Callison-Burch), "Using Comparable Corpora to Augment Low Resource Statistical Machine Translation Models", July 2014.
  3. Xuchen Yao, Johns Hopkins University (advisors: Benjamin Van Durme and Chris Callison-Burch), "Feature-Driven Question Answering with Natural Language Alignment", July 2014.
  4. Paramveer S. Dhillon, University of Pennsylvania (advisors: Lyle Ungar and James Gee), "Advances in Spectral Learning with Application to Text Analysis and Brain Imagine", June 2014.
  5. Emily Pitler, University of Pennsylvania (advisors: Mitch Marcus and Sampath Kannan), "Models for Improved Tractability and Accuracy in Dependency Parsing", August 2013.
  6. Hala Almaghout, Dublin City University (advisors: Andy Way and Jie Jiang), "CCG-Augmented Hierarchical Phrase-Based Statistical Machine Translation", August 2012.
  7. Chang Hu, University of Maryland (advisors: Ben Bederson and Philip Resnik), "Monolingual Machine Translation", July 2012.
  8. Emily Tucker Prudhommeaux, Center for Spoken Language Understanding, Oregon Health and Science University (advisor: Brian Roark), "Alignment of Narrative Retellings for Automated Neuropsychological Assessment", July 2012.
  9. Omar Zaidan, Johns Hopkins University (advisor: Chris Callison-Burch), "Crowdsourcing Annotation for Machine Learning in Natural Language Processing Tasks", April 2012.
  10. Lane Schwartz, University of Minnesota (advisors: William Schuler and Chris Callison-Burch), "An Incremental Syntactic Language Model for Statistical Phrase-based Translation", February 2012.
  11. Aaron B. Phillips, Language Technology Institute, Carnegie Mellon University (advisor: Ralf D. Brown), "Modeling Relevance in Statistical Machine Translation: Scoring Alignment, Context, and Annotations of Translation Instances", February 2012.
  12. Zhifei Li, Johns Hopkins University (advisor: Sanjeev Khudanpur), "Discriminative Training and Variational Decoding in Machine Translation Via Novel Algorithms for Weighted Hypergraphs", April 2010.
  13. Nitin Madnani, University of Maryland (advisor: Bonnie Dorr), "The Circle of Meaning: From Translation to Paraphrasing and Back", 2010.
  14. Yuval Marton, University of Maryland (advisor: Philip Resnik), "Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models", October 2009.
  15. Elliott Franco Drabek, Johns Hopkins University (advisor: David Yarowsky), "Translingual Fine-grained Morphosyntactic Analysis and its Application to Machine Translation", October 2009.
  16. Roy Tromble, Johns Hopkins University (advisor: Jason Eisner), "Search and Learning for the Linear Ordering Problem with an Application to Machine Translation", April 2009.

Undergraduate and Masters Advising

Independent Studies and RAships

Summer 2017

  1. Eddie Okon - Undergraduate - RA
  2. Natasha Ter-Saakov - High School / Undergraduate - RA
  3. Maryanne Cosgrove - Undergraduate - RA
  4. Bradley Jackson - Undergraduate - RA

Spring 2017

  1. Grace Arnold - Undergraduate - RA
  2. Jack Liu - Undergraduate - Senior Thesis
  3. Jessica Li - Undergraduate - RA
  4. Jianging Wang - Undergraduate - RA
  5. John Hewitt - Undergraduate - RA
  6. Sara Dwyer - Undergraduate - RA
  7. Veronica Wharton - Undergraduate - Senior Project
  8. Victoria Xiao - Undergraduate - RA
  9. Brendan Callahan - Master's - Independent Study
  10. Devanshu Jain - Master's - Independent Study
  11. Sneha Rajana - Master's - Master's Thesis
  12. Xiao Ling - Master's - Master's Thesis

Fall 2016

  1. John Hewitt - Undergraduate - RA
  2. Nivedita Sankar - Undergraduate - Senior Design
  3. Sierra Yit - Undergraduate - Independent Study
  4. Talia Delijani - Undergraduate - RA
  5. Veronica Wharton - Undergraduate - Senior Project
  6. Victoria Xiao - Undergraduate - RA
  7. Brendan Callahan - Master's - Independent Study
  8. Sneha Rajana - Master's - RA
  9. Xiao Ling - Master's - Independent Study

Summer 2016

  1. John Hewitt - Undergraduate - RA
  2. Parker Stakoff - Undergraduate - RA
  3. Veronica Wharton - Undergraduate - RA
  4. Victoria Xiao - Undergraduate - RA

Spring 2016

  1. Lawrence Chan - Undergraduate - RA
  2. Alexander Ma - Undergraduate - Independent Study
  3. Parker Stakoff - Undergraduate - RA
  4. Veronica Wharton - Undergraduate - RA, Independent Study
  5. Sierra Yit - Undergraduate - RA, TA
  6. Theresa Breiner - Master's - Independent Study
  7. Brendan Callahan - Master's - Independent Study
  8. Shreejit Gangadharan - Master's - RA
  9. Grishma Jena - Master's - Independent Study
  10. Sneha Rajana - Master's - Independent Study
  11. Dhruvil Shah - Master's - RA
  12. Ishan Srivastava - Master's - RA

Fall 2015

  1. Drew Stone - Undergraduate - RA
  2. Anwesha Das - Master's - Independent Study
  3. Deepti Panuganti - Master's - Independent Study
  4. Siyu Qiu - Master's - Independent Study
  5. Shreejit Gangadharan - Master's - RA
  6. Dhruvil Shah - Master's - RA
  7. Sierra Yit - Undergraduate - RA, TA
  8. Veronica Wharton - Undergraduate - Independent Study

Team Projects

Fall 2016

  1. NETS 213 Prize: Crowdsourced Interpretation of Terms of Service
    • Elizabeth Hamp
    • Grace Arnold
    • Mara Levy
    • Yoni Nachmany

2016-2017

  1. Senior Design Project: Know Your Nyms
    • Dean Fulgoni
    • Hannah Cutler
    • Ross Mechanic
  2. Senior Thesis: Discovering Meronyms using Deep Learning
    • Jack Liu
  3. Senior Design Project: Data-Driven Understanding of Adjectives in Yelp Reviews - Honorable Mention in the CIS Senior Design fair
    • Veronica Wharton
  4. Senior Design Project: Trending Topics on Wikipedia - ESE Societal Impact Award
    • Abhiti Prabahar
    • Alice Serfati
    • Cristina Bustamant
    • Sierra Yit

2015-2016

  1. Senior Design Project: Visualizing Gun Violence
    • Fabian Wikstroem
    • Alexandra Selldorff
    • Michael Browne
    • Nina Ilieva
  2. Senior Design Project: Mobile Crowdsourcing - Honorable Mention in the CIS Senior Design fair
    • Kate Miller
    • Alex Whitaker
    • Zach Krasner
    • Laura Kingsley
  3. Senior Design Project: Machine Learning for MeetForCoffee
    • Hong Kim
    • Minsu Kim
  4. Senior Thesis: Natural Language Understanding with Paraphrases
    • Yuqi Zhu

2014-2015

  1. Senior Design Project: Observing Republican and Democrat Behavior through Tweeted Articles
    • Dhruv Maheshwari
    • Ali Altaf
    • Hamza Qaiser
    • Dennis Sell
  2. Senior Design Project: University Depression Monitoring Using Twitter Data
    • Ashwin Baweja
    • Jason Kong
    • Tommy Pan Fang
    • Yaou Wang
  3. Senior Design Project: Generating Music with Machine Translation Algorithms - Honorable Mention in the CIS Senior Design fair
    • Rigel Swavely
    • Nicole Limtiaco

2013-2014

  1. Senior Design Project: Bachify - Machine Learning for Bach chorales - 1st prize in the CIS Senior Design fair
    • Israel Geselowitz
    • David Cerny
    • Jiten Suthar