Author name cluster

Nitin Gupta

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

AAAI Conference 2026 System Paper

GAICo: Demonstrating a Unified Framework for Multi-Modal GenAI Evaluation

Pallav Koppisetti
Nitin Gupta
Kausik Lakkaraju
Biplav Srivastava

The rapid evolution of Generative AI, yielding outputs across text, structured data, images, and audio, has outpaced the development of standardized evaluation tools, leading to fragmented and non-reproducible practices. GAICo (Generative AI Comparator) offers a solution: a deployed, open-source Python library that provides a unified, extensible, and reproducible framework for multi-modal GenAI evaluation. Our demonstration highlights GAICo’s utility through a practical case study: evaluating and debugging composite AI Travel Assistant pipelines. We show how GAICo facilitates isolating performance issues, for instance, distinguishing orchestrator LLM planning deficiencies from specialist image model generation flaws, by consistently comparing diverse outputs against tailored references. This framework streamlines development, improves system reliability, and promotes reproducible evaluation, making it a critical tool for building safer and more effective AI. Its rapid adoption, evidenced by over 16,000 downloads in the first 6 months, underscores its relevance and impact within the AI community.

PDF Details DOI

AAAI Conference 2025 Short Paper

Towards Enhancing Road Safety in South Carolina Using Insights from Traffic and Driver-Education Data (Student Abstract)

Nitin Gupta
Bharath Muppasani
Saina Srivastava
Aarohi Goel
Ross Hartfield
Todd Buehrig
Melissa Reck
Emma Kennedy

In this student paper, we report on our project to enhance road safety in South Carolina (SC) by analyzing traffic data provided by the Department of Transportation and evaluating the impact of a school-level student driver education program called Alive@25. We improve the understanding of road safety using these traffic and training data to understand collision patterns and areas for improvement and assess training coverage gaps. Our approach combines geospatial analysis, economic impact assessment, temporal trend analysis, and interactive visualizations while leveraging AI techniques to clean and analyze extensive datasets. Key findings revealed higher collision rates in urban counties and rising collision rates in mostly rural areas, where Alive@25 participation is declining. These insights led to recommendations for improving road infrastructure and expanding safety training programs. This research demonstrates the potential of AI-driven insights to inform timely, cost-effective interventions and promote multi-stakeholder engagement in addressing public safety challenges while teaching students data science and AI skills and civic engagement.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

LLM-powered GraphQL Generator for Data Retrieval

Balaji Ganesan
Sambit Ghosh
Nitin Gupta
Manish Kesarwani
Sameep Mehta
Renuka Sindhgatta

GraphQL offers an efficient, powerful, and flexible alternative to REST APIs. However, application developers writing GraphQL clients need both technical and domain-specific expertise to reap its benefits, and avoid over-fetching or under-fetching data. Automated GraphQL generation has so far proven to be a hard problem because of complex GraphQL schema and lack of benchmark datasets. To address these issues, our work focuses on building an LLM-powered pipeline that can accept user requirements in natural language along with the complex GraphQL schema and automatically produce the GraphQL query needed to retrieve the necessary data. Automated GraphQL generation helps reduce entry barriers to application developers, broadening GraphQL adoption.

PDF Details DOI

AAAI Conference 2018 Short Paper

Semantic Understanding for Contextual In-Video Advertising

Rishi Madhok
Shashank Mujumdar
Nitin Gupta
Sameep Mehta

With the increasing consumer base of online video content, it is important for advertisers to understand the video context when targeting video ads to consumers. To improve the consumer experience and quality of ads, key factors need to be considered such as (i) ad relevance to video content (ii) where and how video ads are placed, and (iii) non-intrusive user experience. We propose a framework to semantically understand the video content for better ad recommendation that ensure these criteria.

PDF Details