Social Media Analytics
(July 2017 - Dec 2019)
Worked with the Windows Social Media Analytics team to classify User feedback data from Social Media Channels and Feedhub Hub for better bug triage and automated actionable next steps. Leveraged Machine Learning and Natural Language Processing Techniques to conduct various experimentations for feature selection, tuning parameters and model training for picking the best parameters for production. Also did experimentations around merging structured and unstructured data to create a better balanced dataset.
(November 2016 - present)
Led a team to build a Fake news detecting Chrome extension "Project Fib", Google Moonshot Prize winner at HackPrinceton 2016 Featured on Business Insider, Washington Post, The Next Web, Mashable, Huffington Post, Microsoft Blog, Wired, Hacker News, Fortune, CNN, BBC, CBS, CBC News, Bloomberg TV, Boston Globe etc -
Invited to "Forbes 30 Under 30 Conference" 2017 for the work 50K User Requests per min, 650 stars on Github and 20K Dev views on Github page on 1st week
Won Grinspoon Entrepreneurial Concept Award 2017
Research Software Engineering Intern,
(July 2014 - August 2014)
Implemented an US state level Location Inferring Classifier for Twitter Users with overall 21% accuracy boost from Baseline for Project Trupil ( http://research.microsoft.com/en-us/projects/trupil/ ), lead by the Social Analytics Team.
This SVM Classifier, built with 5.1 million user tweets, uses the text content of the User's tweets (indicator of dialect of the region), time-of-tweeting as the main features and reverse geocoded latitude-longitude info of the location tagged tweets as class label
1. Masters Thesis : Detection of Fake News from Social Media.
Built Fake news detector Chrome Extension "FiB", which was featured in Washington Post, Business Insider, Mashable, Financial Times, TheNextWeb, Hacker news, The Huffington Post, the Boston Globe, Microsoft Blog, CBC News, CNBC News, CNN News, BBC News, CNET etc. - https://projectfib.azurewebsites.net