Developed a tool for document processing as part of the NLP core group using an
OCR engine, image processing, and NLP techniques to process unstructured data and
convert them to a structured format.
Built Computer Vision models to serve educational recommendations.
Manager: Mr. Anand Chandrasekaran, Founder and CTO
Part-Time Undergraduate Research \& Teaching Assistant
Organization: Bright Academy (Previously Solarillion Foundation)
Location: Chennai, India
Duration: February 2020 - June 2022 (2 Yrs 5 Mos)
Lead the NLP group that aims to translate the video input of a German sign language translator depicting the weather,
into cohesive and accurate German sentences.
We achieved 98.19% score retention in the ROUGE-L score and 86.65% in the BLEU-4 score,
while simultaneously achieving a 30.88% reduction in model parameters when compared to the state-of-the-art model.
Collaborated on Terms of Service Classification, an NLP problem statement that uses a two-stage
knowledge distillation DL approach on low-resource devices with cutting-edge architectures like BERT
to find unfair Terms of Service terms.
Guided 5+ students in research, evaluated assignments and projects in Python and Machine Learning.
Oversaw the website development and managed the server for our research group.
Wrote bots for posting office hours and creating polls.
Advisor: Mr. Vineeth Vijayaraghavan, Director - Research and Outreach
Student Researcher
Organization: Sri Sivasubramaniya Nadar College Of Engineering
Location: Chennai, India
Duration: December 2020 - April 2022 (1 Yr 5 Mos)
Posited a novel architecture for Fake News Detection based on Transformer architecture, which considers the title and
content of a news article to determine its integrity.
Our work performed with an accuracy of 74.0% on a subset of the NELA-GT 2020 dataset. To our knowledge, FakeNews Transformer
is the first published work considering both title and content for evaluating a news article.
Proposed a robust and cost-effective automatic speech recognition model for the Tamil language leveraging Baidu's Deep Speech
architecture. Our work was compared against Google's speech-to-text API, outperforming it by 20%.