CUNY-NLP Talk by Dr. Dan Bikel (Google) on Nov. 12

[Heng Ji]

Sorry for multiple posts...

We are very excited that Dr. Dan Bikel will speak at our next Friday's
seminar. If you want to join lunch (before the talk) or have
individual meeting (after the talk) with Dan, please let me know by Nov.
10 so I can schedule things.

Heng
---------------------------------------


Time: 2pm-3pm, Friday, Nov. 12
Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str).

Speaker: Dr. Dan Bikel (Google)

Title: Language Technology Research at Google
Abstract:
Google's mission is to organize the world's information and make it
accessible. While this mission statement is somewhat vague, it
certainly includes understanding the information on the web and being
able to present that information in coherent and succinct ways to
users. In this talk, I will give an overview of the various kinds of
language processing technology research that is going on at Google,
including (but not limited to): machine translation, speech
recognition, large-scale language modeling and sentiment analysis. I
will also discuss my current primary research project, which is the
improvement of speech recognition on YouTube videos.

Bio:
Dan Bikel graduated with honors from Harvard in 1993 with a degree
in Classics, after which he spent a year as a graduate student at
Harvard studying engineering, computer science and music. In 1994,
he joined the Speech and Language Processing group at BBN in
Cambridge,Massachusetts, where he co-created the first
state-of-the-art namedentity finder, Nymble (now known as
IdentiFinder(tm)). After spending three years at BBN, he became a
Ph.D. student at the University of Pennsylvania's Computer and
Information Science department, studying under Prof. Mitchell P.
Marcus. At Penn, he built the first extensible syntactic parsing
engine, discovering new and surprising properties of syntactic
parsing models. Dan spent five years as a Research Staff Member at
the IBM T. J. Watson Research Center, where he investigated many
aspects of information extraction, including named entity
coreference, semantic role labeling, quotation attribution detection,
event and topic matching, oblivious computation for NLP applications
and NLP system architectures. Dan is currently a Research Scientist
at Google Research NYC, exploring ways to improve the quality and
robustness of speech recognition, statistical parsing and question
answering systems.