IBM Research - India, Bangalore
Nanda Kambhatla has nearly two decades of research experience in the areas of Natural Language Processing (NLP), text mining, information extraction, dialog systems, and machine learning. He holds 7 U.S patents and has authored over 40 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 senior manager of the Human Language Technologies department at IBM Research - India, Bangalore. He leads a group of over 20 researchers focused on research in the areas of NLP, advanced text analytics (IE, IR, sentiment mining, etc.), speech analytics and statistical machine translation. 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.