I am a Master’s in Computer Science student at the University of Texas at Austin. I am currently advised by Dr. Jessy Li (co-advisor Dr. Greg Durrett). My research is focused on understanding discourse relationships using the linguistic framework of QuD (Question under Discussion). I am working on developing an evaluation framework for QuD parsers and exploring QuD ranking.
In addition to this, I have a keen interest in music. I am a music aficionado at heart and the days I seem to hit the wrong note, my guitar helps me harmonize. My days aren’t complete without an element of music.
You can find my Résumé here.
Master's in Computer Science, May 2024 (expected)
The University of Texas at Austin
B.Tech in Information Technology, 2022
Veermata Jijabai Technological Institute
This paper focuses on plotting a network of Twitter users, based on a particular hashtag, and detecting the communities in it, followed by detecting and locating the bots in these communities. In addition to this, sentiment analysis is conducted on normal users’ tweets as well as the bots in these communities. This paper also aims to identify the overall sentiment of the communities, and thus provide promising conclusions relating to bot behavior in the overall network.
In this research paper, we present a comparative study of the most popular machine learning classifiers used to solve the problem of churning customers in the telecommunications sector. In the first phase of our test, all models were implemented and tested using statistical evaluative measures on the popular telecom database. In the second phase, the performance improved by boosting was evaluated.
This research has used varied approaches for the detection of bots; a supervised machine learning approach which makes use of a unique feature called the bot score to determine the bot probability of a user. Secondly, using an unsupervised machine learning approach, users were divided into various clusters based on their activity. Furthermore, to assess the influence of users in hashtag manipulation, this research analyzed various categories of users in a hashtag and made promising conclusions.
Estimated the remaining useful life of a bearing using Machine Learning. Achieved better prognostic performance with 98.2% accuracy as compared to SOTA techniques such as Kalman filter.
Devised a solution to improve city sanitation under Solid Waste Management Department using Neo4J and PostGreSQL. Developed an algorithm to identify trash from geotagged images using a Mask R-CNN model.
Created a social media bot which indexes 2000+ potential startup ideas in a database and a CRM dashboard to display it. Developed a news feed scraper to trace activities of the projects by web scraping data using Beautiful Soup.
Performed text summarization by clustering sentence embeddings trained to embed paraphrases together using K-Means. Improved the accuracy by using a Skip-Thoughts (GRU-RNN) model to preserve sequence of words in a sentence.
Devised a platform for BMC, the governing civic body, to view the real time location of garbage trucks and the amount of waste collected.Created a dashboard to view daily statistics and reports in order to formulate measures to optimize waste management