John R. Talburt Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Biography and Research Information
OverviewAI-generated summary
John R. Talburt's research focuses on the application of artificial intelligence and machine learning techniques to address challenges in data quality, bias, and accuracy, particularly within healthcare contexts. He has investigated transfer learning models for food detection using ResNet-50 and developed methods for preprocessing healthcare data to mitigate bias. His work also includes the development of algorithms for recovering missing values in single-cell RNA sequencing data, such as cnnImpute. Talburt has explored frameworks for data quality assessment using zero-trust models and examined the information quality of large language models. His research extends to unsupervised data clustering and cleaning, and he has utilized generative AI for entity resolution and household movement discovery. Talburt has published over 200 works, with an h-index of 15 and more than 800 citations.
Metrics
- h-index: 15
- Publications: 211
- Citations: 821
Selected Publications
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AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform (2026)
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Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction (2025)
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Policy-Aware Generative AI for Safe, Auditable Data Access Governance (2025)
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A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease (2025)
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Single-Cell Transcriptomic Analysis Unveils Key Regulators and Signaling Pathways in Lung Adenocarcinoma Progression (2025)
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Entity Resolution Using Transformers for Synthetic Datasets (2025)
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Semantic Entity Resolution on Synthetic Datasets: A Transformer-Centric Approach (2025)
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Entity Resolution with Household Movement Discovery Using Google Generative AI (2025)
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Using Linkage Context for Automated Correction in Unsupervised Entity Resolution (2025)
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Improving Quality of Entity Resolution Using a Cascade Approach (2025)
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A Pattern-Based Approach to Name and Address Parsing with Active Learning (2025)
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SparkDWM: a scalable design of a Data Washing Machine using Apache Spark (2024)
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Household Discovery with Group Membership Graphs (2024)
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A scalable MapReduce-based design of an unsupervised entity resolution system (2024)
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cnnImpute: missing value recovery for single cell RNA sequencing data (2024)
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