Muktha Ananda

                     the scientist formerly known as "AC Surendran" :)

 Principal Applied Research Manager @OneDrive & Sharepoint

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

email: my first name

About Me + My Interests

Currently I manage an applied research team in OneDrive. Using the power of machine learning, my team aims to enable intelligent experiences on top of photos & documents, and to prescribe business decisions that directly impact customers & revenue.

We are HIRING! If you are an exceptional applied researcher and are looking for emerging challenging problems in cloud services this job is for you!

Previously I worked on Big Data problems in cloud services in Windows, 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:


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: May 23rd 2014.


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