Nanda Kambhatla has nearly 17 years of research experience in the

  areas of Natural Language Processing (NLP), text mining, information

  extraction, dialog systems, and machine learning. He holds 6 U.S

  patents and has authored over 30 publications in books, journals, and

  conferences in these areas. Nanda holds a B.Tech in Computer Science

  and Engineering from the Institute of Technology, Benaras Hindu

  University, India, and a Ph.D in Computer Science and Engineering from

  the Oregon Graduate Institute of Science & Technology, Oregon, USA.


  Currently, Nanda is the manager of the Data Analytics Group at IBM's

  India Research Lab (IRL), Bangalore. The group is focused on research

  on machine translation, Natural Language Processing, text analysis and

  machine learning techniques for developing analytics solutions to help

  IBM's services divisions. Most recently, Nanda was the manager of the

  Statistical Text Analytics Group at IBM's T.J. Watson Research Center,

  the Watson co-chair of the Natural Language Processing PIC, and the

  task PI for the Language  Exploitation Environment (LEE) subtask for

  the DARPA GALE project. He has been leading the development of

  information extraction tools/products and his team has achieved top

  tier results in successive Automatic Content Extraction

  (ACE) evaluations conducted

  by NIST for extracting entities, events and relations from text from

  multiple sources, in multiple languages and genres.


  Earlier in his career, Nanda has worked on natural language web-based

  and spoken dialog systems at IBM. Before joining IBM, he has worked on

  information retrieval and filtering algorithms as a senior research

  scientist at WiseWire Corporation, Pittsburgh and on image compression

  algorithms while working as a postdoctoral fellow under Prof. Simon

  Haykin at McMaster University, Canada.


  Nanda's research interests are focused on NLP and technology solutions

  for creating, storing, searching, and processing large volumes of

  unstructured data (text, audio, video,

  etc.) and specifically on applications of statistical learning

  algorithms to these tasks.