Kaushik Chakrabarti

Kaushik Chakrabarti
RESEARCHER
.

Kaushik Chakrabarti

Data Management, Exploration and Mining Group
Microsoft Research
One Microsoft Way
Redmond, WA 98052
Fax: (425)936-7329
Phone: (425)703-5137
Email: kaushik@microsoft.com

Click here for a short bio

Recent News

(7/2012) I have been invited to give a keynote talk at the Workshop on Entity-oriented and Semantic Search (JIWES) at SIGIR 2012. Here is the title and abstract.

(8/2011) I have been invited to serve as a workshop co-chair for VLDB 2014 to be held at the beautiful Hangzhou, China.

(7/2011) The Distributed and Parallel Databases Journal (Springer’s international journal on database management and information retrieval) is planning a special issue on ranking in databases. I am the editor of this special issue. If you are working on ranking in databases, please consider submitting your work to this special issue. The deadline for the paper submission is October 7, 2011. The call for papers can be found on the journal web site: http://www.springer.com/journal/10619

(4/2011) The fifth international workshop on ranking in databases (DBRank) 2011 will be held in conjunction with VLDB 2011 in Seattle, WA, USA. Davide Martinenghi and I are the program co-chairs. If you are working on ranking, please consider submitting a paper to DBRank 2011. The deadline is June 7, 2011.

Research Interests

Kaushik's interests spans many aspects of data management, information retrieval and data mining. He is specifically interested in the following topics:

  • Bridging structured and unstructured data: how can structured and unstructured information (text) be used in conjunction (rather than separately) for better search and analysis?
  • Information Extraction and Text Analytics: how can unstructured data inside the enterprise as well as that on the web be used for business intelligence?
  • Web Mining: how can we mine the web to extract knowledge about structured entities like products and people?
  • Context-aware Search: how can a user's context be used to improve search?
  • Analysis over big data: how can computational paradigms like MapReduce be used to analyse massive amounts of data (like query logs, web documents)?
  • Information Retrieval: how can IR engines efficiently support new kinds of queries like top-k and proximity queries? 
  • Machine learning: how can machine learning techniques be used to solve real-world problems in search, information extraction and data analysis?

Education

  • Ph.D., Computer Science, University of Illinois at Urbana Champaign, 2001.
  • M.S., Computer Science, University of Illinois at Urbana Champaign, 1999.
  • B. Tech. , Computer Science and Engineering, Indian Institute of Technology, 1996.

Recent Professional Activities

Best Paper Awards

Selected Publications

    2013

    2012

    2011

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    1997

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