Projects

News Filtering System

With an effort to mitigate the bias and misinformation in news articles, we confront the discernible subjectivity in news platforms by developing a news filtering model that summarizes and maintains the valuable content of published material. Our model prompts users to input a news topic, and in return, they receive a paragraph summary of content related to the given topic. The outputted information is obtained as a result of the application of clustering and extractive summarization techniques.

Contributions:

Algorithm Lead

Co-led the implementation of algorithms for the least biased extractive summarization of news articles.
Utilization of multi-core CPUs in Google Cloud Platform for model performance efficiency in evaluation and calculations.

Gredient Mobile Application

Gredient was a mobile application developed to focus on safeguarding the health and improving shopping experiences of the 32 million American consumers with food allergies by helping them easily check the contents of ingredient labels. The Gredient iOS app is based on an optical character recognition model hosted in a serverless web architecture that can scale to allow millions of people to use our service.

Contributions:

Algorithm and Data Architecture Lead

Spearheaded the algorithm development for optical character recognition (OCR) of ingredients on the image of food product ingredient labels.
Implemented and optimized OCR model to assess customers on the ingredient safety of food products.
Co-led the back-end design of severless architecture with AWS Lambda, DynamoDB, SageMaker and Rekognition.