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Neela Sawant on Muck Rack

Neela Sawant

As seen in: Amazon Science
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Articles

Code compliance assessment as a learning problem

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By Neela Sawant, Srinivasan H. Sengamedu, Aditya Anantharaman, Zhuang Wang
| Amazon Science
Manual code reviews and static code analyzers are the traditional mechanisms to verify if source code complies with coding policies. However, they are hard to scale. We formulate code compliance assessment as a machine learning (ML) problem, to take as input a natural language policy and code, and generate a prediction on the code’s compliance, non-compliance, or irrelevance. Our intention for ML-based automation is to scale the development of Amazon CodeGuru, a commercial code analyzer.
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Contextual multi-armed bandits for causal marketing

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By Neela Sawant, Chitti Babu Namballa, Narayanan Sadagopan
| Amazon Science
Amazon’s Global Accounts Receivable Data Analytics’s (GARDA) science team seeks an outstanding Data Scientist with the technical expertise and business intuition to invent the future of Accounts Receivable at Amazon. As a key member of the science team, the Data Scientist will own high-visibility analyses, methodology, and algorithms in the Order-to-Cash (O2C) lifecycle to drive free cash flow improvements for Amazon Finance Operations.
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Learning-based identification of coding best practices from software documentation

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By Gokhan Tur, Neela Sawant, Srinivasan H. Sengamedu
| Amazon Science
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous.
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