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Staff Research Scientist Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA Email: ravi DOT sujith AT gmail DOT com |
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My main research interests span various problems and theory related to the fields of machine learning, natural language processing (NLP), data mining and computational decipherment (cracking codes with computers). I am specifically interested in large-scale unsupervised and semi-supervised methods and their applications to structured prediction problems in NLP, information extraction, multi-modal learning for language/vision, user modeling in social media, graph optimization algorithms for summarizing noisy data, computational decipherment and computational advertising.
Check out our work on multimodal learning using computer vision and NLP for Photo Reply, unveiled at Google I/O 2016 conference
Our work on Allo (smart messaging powered by machine learning and NLP) announced at Google I/O 2016 conference
KDD 2016 paper on Smart Reply: Automated response suggestion for email
ICML 2016 (MVRL) paper on Semantic Video Trailers
NAACL 2016 paper on Conversational flow in Oxford-style debates
Our work on Smart Reply (automated email reply using machine learning) announced: Google Research blog, Gmail blog, WIRED, TechCrunch, NYTimes, USA Today and other news media
AISTATS 2016 paper on Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation
WSDM 2016 paper on Hierarchical Label Propagation and Discovery for Machine Generated Email
Sujith Ravi is a Staff Research Scientist at Google. His main research interests span various problems and theory related to the fields of machine learning, large-scale structured prediction and natural language processing (NLP). He recently won the SIGKDD 2014 Best Research Paper Award. He is specifically interested in large-scale unsupervised and semi-supervised methods and their applications to structured prediction problems in NLP, information extraction, multi-modal learning for language/vision, user modeling in social media, graph optimization algorithms for summarizing noisy data, computational decipherment and computational advertising.
Prior to joining Google, he spent a year at Yahoo! Research, Santa Clara working as a Research Scientist. He completed his PhD at University of Southern California/Information Sciences Institute working with Prof. Kevin Knight.
NAACL HLT Workshops (2013)
Second Joint NAACL/ICML Symposium on NLP and Machine Learning (NAACL/ICML 2013)
MM - First Workshop on Multilingual Modeling (ACL 2012)
CIKM (2015)
IJCAI (2011)
NIPS (2014)
ICML (2014, 2013)
KDD (2015, 2014)
AAAI (2014)
WSDM (2015)
ICWSM (2015)
ACL (2015, 2014, 2013, 2012)
EMNLP (2014, 2012)
NAACL (2012)
EACL (2012)
IJCNLP (2011)
BUCC - Workshop on Building and Using Comparable Corpora (ACL 2015, LREC 2012, ACL 2011, LREC 2010)
IEKA - Workshop on Information Extraction and Knowledge Acquisition (RANLP 2011)
NAACL/HLT Student Research Workshop (2010)
NIPS 2014, ICML 2014, KDD 2014, AAAI 2014, ACL 2014, EMNLP 2014,
ICML 2013, ACL 2013, ACL 2012, EMNLP 2012, NAACL 2012, EACL 2012,
IEEE TKDE - Transactions on Knowledge and Data Engineering (2012),
Journal on Pattern Recognition (2011),
IJCNLP (2011), AAAI (2011), ACL (2011), IJCAI (2011),
ACM TIST - Transactions on Intelligent Systems and Technology Journal (2011),
AMTA (2010), NAACL (2010), LREC (2010), ...
My main research interests lie in Artificial Intelligence areas, specifically machine learning, large-scale data mining, natural language processing (NLP), multimodal learning, and other areas such as computational decipherment. Here are a few (selected) research topics that I have worked on.
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I have been a TA and/or taught lectures for the following graduate courses in the Computer Science Department at University of Southern California:
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| 2006 - 2011 : | Phd in Computer Science, |
| 2004 - 2006 : | M.S in Computer Science, |
| 2000 - 2004 : | B.Tech. in Computer Science, |