Research

2026

1. The Generative AI Dilemma: Enhancing Productivity while Managing Technical Debt in Open Source Development

   Shivansh Gupta, Vasundhara Sharma, and Aditya Karanam

   2026

   Work in progress

   Abs

   This project examines the trade-offs introduced by generative AI in open-source development, focusing on productivity gains and the accumulation of technical debt.

2. Effects of Social Movements on Student Bias in Faculty Evaluations

   Shivansh Gupta, Vasundhara Sharma, Deepa Mani, and Aditya Karanam

   2026

   Work in progress

   Abs

   This project studies how social movements influence bias in student evaluations of faculty, with a focus on behavioral and perceptional changes.

3. Examining Effects of AI Disclosure on Early-Stage Ventures

   Shivansh Gupta, Vasundhara Sharma, and Aketi Gayatri

   2026

   Work in progress

   Abs

   This project analyzes how AI disclosure affects user perception and outcomes in early-stage ventures.

2025

1. Platform Design to Curb Misinformation

   Vasundhara Sharma and Shivansh Gupta

   2025

   Research assistance
2. Hidden Ads in YouTube Kids Videos: Effects of Platform Policy Changes

   Vasundhara Sharma, Sumeet Kumar, and Shivansh Gupta

   2025

   Research assistance
3. Platform Me or Not: Case of Digital Goods

   Vasundhara Sharma and Shivansh Gupta

   2025

   Research assistance

2023

1. Method and System for Classifying and Detecting True Well Control Events During Drilling Operations

   Jaspreet Singh, Somnath Das, Idris Kagdi, and Shivansh Gupta

   2023

   Indian Patent, Published and under review, Patent No. 202311057956

2019

1. Automated Analysis through Natural Language Processing of DGMS Fatality Reports on Indian Non-Coal Mines

   Himanshu Shekhar, Shivansh Gupta, and Siddharth Aggarwal

   In International Conference on Information Systems and Computer Networks, 2019

   Paper presentation

   Abs

   Mining activities pose a major threat to the safety of mine workers due to the inherent risky nature of the job. To realize the objective of preventing fatal and non-fatal injuries, it is imperative for a mine management to analyze site specific mine safety data. This paper is the first attempt to automate the analysis of Directorate General Mines Safety (DGMS) fatality reports on Indian non-coal mines. It is done by text mining and natural language processing (NLP) techniques. The proposed method sidesteps multiple man hours of scrutinizing each report and technical expertise required in analyzing mining accidents. The authors have taken 6 years’ worth of data ranging from 2010–2015 published by DGMS. The attempt revealed valuable insights into the causes of injuries such as accidents occurrence is highest in states of Rajasthan followed by Andhra Pradesh. The leading indicators of fatality included falling from heights, age between 28 to 32 years, worker class of ‘mazdoors’, and shift timings between 10 AM and 2 PM. Furthermore, results show accidents happened mostly in small scale stone mines. This will be a step forward for the Indian mining industry to extract meaningful results from more such text data quickly and accurately to make better decisions. The methodology can be extended to the Indian coal mining industry easily.