Papers by Abhijit Gupta
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis (2024.emnlp-industry)
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| Challenge: | Exploratory Data Analysis (EDA) is a complex task that requires thorough exploration and analysis of the data. |
| Approach: | They propose a fully automated EDA system that generates questions in iterations and analyzes data to produce multiple relevant insights. |
| Outcome: | QUIS is a fully automated EDA system that generates questions in iterations without human intervention or manually curated examples. |
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020.acl-main)
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Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Neural networks lack the ability to reason about qualitative physics and cannot generalize to scenarios and tasks unseen during training. |
| Approach: | They propose a framework for reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. |
| Outcome: | The proposed framework generates explanations of how the physical simulation will causally evolve so that an agent or a human can reason about a solution using interpretable descriptions. |
Federated Retrieval-Augmented Generation: A Systematic Mapping Study (2025.findings-emnlp)
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| Challenge: | Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL) with Retrieleval-augment Generation (RAG) |
| Approach: | They propose to map literature on Federated Retrieval-Augmented Generation (Federated RAG) this mapping study examines architectural patterns, temporal trends, and key challenges . |
| Outcome: | The proposed framework improves the factual accuracy of language models by grounding outputs in external knowledge. |
JobMatchAI - An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI (2026.acl-demo)
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| Challenge: | Recruiters and job seekers rely on search systems to navigate labor markets . many systems fail to handle skill synonyms and nonlinear careers . |
| Approach: | They propose a production-ready system that integrates Transformer embeddings, skill knowledge graphs, and interpretable reranking. |
| Outcome: | The proposed system optimizes utility across skill fit, experience, location, salary, and company preferences. |