Krishnaram Kenthapadi

***Update: Please use my personal webpage for my contact details and updated information: ***

Brief bio:

Krishnaram Kenthapadi is a Researcher at Microsoft Research Silicon Valley in Mountain View, California. He graduated with a Ph.D. degree in Computer Science from Stanford University in 2006, under the supervision of Professor Rajeev Motwani. His Ph.D. thesis is titled "Models and Algorithms for Data Privacy". Before joining Stanford, he received his Bachelors degree in Computer Science and Engineering from Indian Institute of Technology-Madras. His recent research interest is in applying algorithmic and data mining techniques for augmenting documents with multimedia content, with applications for enriching e-reader experience and improving education. His other interests include algorithms for the web and social networks, privacy preserving data mining, hashing and load balancing. He received the Best Student Paper award in the SODA 2006 conference. His WWW 2009 paper was nominated for the Best Paper Award.

Recent research themes:

  • Algorithmic Augmentation of Documents with Multimedia Content:
    • Enriching e-reader experience: Over the last few years, the prevalence of tablet devices has increased dramatically, and this trend is expected to continue as commoditization makes such devices accessible to a larger and larger audience. The proliferation of low-cost tablet devices that are readily connected to the cloud opens up several new opportunities to transform reading experience. With printed books being digitized, as is, we address the following question: How do we enrich the experience of reading from electronic versions of printed books, on cloud-connected tablets? Towards this goal, we have developed approaches for enriching books by first understanding the semantics of the content, and using that representation to algorithmically augment a non-fiction book with links to selective web content such as text, images and videos, at any desired granularity. We have incorporated these technologies into a Windows 8 app as well as a web app. These apps are serviced off a Microsoft Azure cloud service that automatically identifies relevant content to augment the book.

         Video demonstration of the app 

    • Enriching e-textbooks: Taking into account the vast amount of existing textbooks designed for traditional printed medium and the potential for enabling new kinds of functionalities through the medium of electronic textbooks, our research focuses on algorithmically diagnosing and enhancing the quality of textbooks. Specifically, we have developed a diagnostic tool for authors and educators to identify weaknesses in a textbook. We have also developed algorithmic and data mining techniques for augmenting different sections of a textbook with links to selective web content (web articles, images & videos) and providing easy access to concepts explained elsewhere in the book that are necessary for understanding the present section. We have also built a demonstration of the ideas running on Windows 8 devices as well as on Aakash, the low cost tablet being developed by the Indian government for distribution to millions of students.

         Video demonstration of the app

  • Algorithmic identification and ranking of Twitter groups: Our research reports on a new kind of group conversation on Twitter that we call a group chat. These chats are periodic, synchronized group conversations focused on specific topics and they exist at a massive scale. We have developed techniques for algorithmically identifying and ranking these groups, and performed large scale evaluation over one year of tweets.
  • Lincoln: Next Generation Product Search Engine: As part of the Lincoln project, we built a working end-to-end experimental commerce search engine showcasing state-of-the-art breakthrough technologies in structured search query processing, hierarchical query classification, indexing strategies, catalog creation and decision aids for users to make informed decisions.
  • Privacy in Search Logs: Our research demonstrates how web search queries, clicks and their associated perturbed counts can be published in a manner that rigorously preserves privacy, while providing utility for applications such as keyword generation [Nominated for WWW 2009 Best Paper Award]. Our later work on privacy preserving distance estimation involves projecting each user's representation into a random, lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then adding Gaussian noise to each entry of the lower-dimensional representation.

Krishnaram Kenthapadi

Contact Information:

Krishnaram Kenthapadi
Microsoft Research
1065 La Avenida
Mountain View, CA 94043, USA
Cell: +1-650-804-2502


kngk [at] (primary)

kngk [at] (secondary)


Personal Website:


Amplifying Learning through Electronic Textbooks

Lincoln: Next Generation Product Search Engine


Search Labs