AC

    Muktha Ananda

                     the scientist formerly known as "AC Surendran" :)

 Architect/Principal Applied Researcher @XBOX

 One Microsoft Way, Redmond WA 98052,
 Phone: (425) 707-5018, Fax: (425) 936-7329,

email: my first name @MICROSOFT.com

About Me + My Interests

Currently I am a data & knowledge scientist working on gaming BI for XBOX.

Previously I worked on Big Data problems in search, recommendations, social search, online advertisements, knowledge bases, text mining and speech/speaker recognition, in the following roles:

 

Some recent projects for which I managed the applied research team:

Much of this work was on "finding connections that interest you". It is about helping people discover things that interest and delight them & help them make better decisions. For this, I have used data mining, machine learning, semantics & recommendations. I have worked extensively in various aspects of this space:

 

  • Interest Graphs.  One simple example of what we have enabled is if you search on Bing for "meat dress costume" we connect you in the Sidebar to Facebook friends who like Lady Gaga.

Machine generated alternative text:
Bing for meat dress costume, find Facebook friends who like Lady Gaga

 

  • In the Knowledge Web team, we built the very first large scale knowledge graph in Microsoft, which has become the seed for Bing's own knowledge graph. On top of this, we built experiences for discovery of knowledge. The example here also shows ads that are semantic.

Machine generated alternative text: Bing for taylor swift

 

Machine generated alternative text: Find Fearless, Tim McGraw, Kanye West, MTV Awards

 

  • Our first knowledge base was in personal finance, and was the primary driver for Bing Finance. One example of what we did was to provide alternate recommendations when searching for a stock - other "higher dividend paying", "top buy rated in the same sector", "having similar PE ratio" etc. which helped people make better stock picking decisions

Machine generated alternative text: While searging for MSFT find related suggestions

 

  • We also built recommendation apps that advanced a "push" paradigm (restaurant recommendation on a mobile phone & video recommendation on tablet). They had well-integrated social experience (Facebook friends), worked on "context genomes" and "taste mash-ups"

Machine generated alternative text: Restaurant recommendation app screen shot

 

Recent research community activities:

Selected Publications:

Advertising & Web:

Semidefinite Programming:

Boosting:

Text Mining, Information Organization and Retrieval:

Acoustic Echo Suppression:

Speech Detection:

Bayesian Learning for speech recognition:

Speaker Verification:

Neural Networks for model adaptation and novel feature extraction:

Microphone arrays:

Other useless information:

 

Last updated: Feb 18th 2014.

 

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