Research & Publications
Work at the intersection of peer-review transparency, argumentation theory, and applied GenAI systems.
Dynamic Optimization of Peer Review Length Using Information Density Analysis
Introduces an information-density framework that balances relevance, argumentative strength, and cognitive load to optimise peer-review length.
MetaSearch: Search-Augmented LLM with Reasoning for Consensus Resolution in Peer Review
Combines disagreement detection, fact-grounded synthesis, and argument evaluation to automate meta-review decisions.
From Black Box to Glass House: Argumentation Theory as a Bridge Between LLM Opacity and Peer Review Transparency
Aligns argumentation theory with LLM signals to improve review interpretability, achieving strong agreement with human annotations.
Unmasking Bias: LLM-Driven AI Agents in Academic Peer Review
Automates bias detection in peer reviews by analysing sentiment, coherence, and consistency, supporting organisers with actionable insights.
LLM-Based Requirement Generation: Evaluation Pipeline and Benchmarking
A 70-page enterprise benchmark comparing DeepSeek R1, GPT-4o, Gemini 2.0, and LLaMA 3.2 on BLEU, Levenshtein, and Jaccard metrics for requirement engineering.
DigniFy: Multimodal Hate Speech Detection in Digital Platforms
Explores multilingual, multimodal safety systems with real-world platform evaluations and policy-aligned mitigation insights.
Achievements & Recognition
GATE DS&AI Rank 499
Ranked 499 nationwide among 60,000+ candidates in GATE Data Science & AI, demonstrating strong theoretical foundations.
Amazon ML Challenge Rank 292
Placed 292 out of 70,000+ participants in Amazon ML Challenge 2024, showcasing applied machine-learning expertise.
CodeBounty 2024 Winner
Won the flagship competitive programming contest at DJSCE, highlighting algorithmic design strengths.
Rajasthan Police Hackathon Finalist
Led ML for the finalist solution tackling law-enforcement challenges with data-driven citizen feedback analysis.
sktime Mentoring Program
Selected as a mentee under Dr. Franz Kiraly for the sktime mentoring program focused on time-series ML research.