Microsoft Research Lab – India
Projects
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We present a health data record management application on a low-cost digital slate prototype (UX’12) built through iterative prototyping.
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VideoKheti is a multimodal video search system for low-literate farmers that combines local language speech, graphics and touch interaction to help find and watch agriculture extension videos (of digitalGreen), in the farmer’s own language and dialect.
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Angelic verification (AV) brings the benefits of static assertion checking to production software without inundating users with false alarms and not burdening them with upfront modeling. In other words, the goal of AV is to democratize static assertion checking for…
In the Harnessing AutoMobiles for Safety (HAMS) project, we use low-cost sensing devices to construct a virtual harness for vehicles. The goal is to monitor the state of the driver and how the vehicle is being driven in the context…
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99DOTS is a technology-enabled project focusing on medication adherence for anti-Tuberculosis drugs. Treatment programs wrap each anti-TB blister pack in a custom envelope, which hides phone numbers behind the medication. Patients can only see these hidden numbers after dispensing their…
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The goal of Project Mélange is to understand the uses of and build tools around code-mixing, analyze and understand code-switching behavior, and equip speech and language processing systems with the capabilities of processing, understanding and generating code-switched language.
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Our goal is to enable data-driven farming. We believe that data, coupled with the farmer’s knowledge and intuition about his or her farm, can help increase farm productivity, and also help reduce costs. However, getting data from the farm is…
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We are designing programming languages for building safe and reliable asynchronous systems. These languages are based on the programming idiom of asynchronous communicating state machines. They offer first-class support for writing safety and liveness specifications as well as building abstract models…
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We explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc.