Dhaval Patel — accepted AI-conference papers

2026

AssetOpsBench-Live: Privacy-Aware Online Evaluation of Multi-Agent Performance in Industrial Operations

Dhaval Patel, Nianjun Zhou, Shuxin Lin, James Rayfield, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula

AAAI 2026technical

Industrial automation increasingly relies on multi-agent AI, yet evaluation remains difficult due to task complexity and data confidentiality. We present AssetOpsBench-Live, a demo of a competition-ready platform for real-time, privacy-preserving evaluation of multi-agent AI in industrial contexts.

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2026

Deployed AI Agents for Industrial Asset Management: CodeReAct Framework for Event Analysis and Work Order Automation

Nianjun Zhou, Dhaval Patel, Anamitra Bhattacharyya

AAAI 2026technical

Maintenance of mission-critical industrial assets is frequently hindered by fragmented data, inconsistent record-keeping, and limited access to analytical expertise, resulting in reactive rather than predictive practices. We present \textit{CodeReAct}, an AI-powered agentic framework deployed in lar

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2026

Diversity Meets Relevancy: Multi-Agent Knowledge Probing for Industry 4.0 Applications

Christodoulos Constantinides, Dhaval Patel, Scott Kimbleton, Nishu Garg, Muhammad Paracha

AAAI 2026technical

Industrial data scientists require deep domain understanding to model asset conditions effectively, yet traditional sources such as Subject Matter Experts (SMEs) and Failure Modes and Effects Analysis (FMEA) documents are often unavailable or incomplete. We present a deployed Multi-Agent System (MAS

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2026

SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search

Yifan Zhang, Giridhar Ganapavarapu, Srideepika Jayaraman, Bhavna Agrawal, Dhaval Patel, Achille Fokoue

AAAI 2026technical

Large Language Models (LLMs) often falter at complex planning tasks that require exploration and self-correction, as their linear reasoning process struggles to recover from early mistakes. While search algorithms like Monte Carlo Tree Search (MCTS) can explore alternatives, they are often ineffecti

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2024

Identifying Homogeneous and Interpretable Groups for Conformal Prediction

Natalia Martinez Gil, Dhaval Patel, Chandra Reddy, Giri Ganapavarapu, Roman Vaculin, Jayant Kalagnanam

UAI 2024poster

Conformal prediction methods are a tool for uncertainty quantification of a model’s prediction, providing a model-agnostic and distribution-free statistical wrapper that generates prediction intervals/sets for a given model with finite sample generalization guarantees. However, these guarantees hol…

Cited by 2 Source ↗PDF ↗Scholar ↗

2023

AI Model Factory: Scaling AI for Industry 4.0 Applications

Dhaval Patel, Shuxin Lin, Dhruv Shah, Srideepika Jayaraman, Joern Ploennigs, Anuradha Bhamidipati +1

AAAI 2023technical

This demo paper discusses a scalable platform for emerging Data-Driven AI Applications targeted toward predictive maintenance solutions. We propose a common AI software architecture stack for building diverse AI Applications such as Anomaly Detection, Failure Pattern Analysis, Asset Health Forecasti…

Cited by 7 Source ↗PDF ↗Scholar ↗