Farid Hashemian
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Graduate Research Assistatnt
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Farid Hashemian's research focuses on the application of machine learning techniques to enhance the reliability and maintenance strategies of complex systems. His work includes developing machine learning frameworks for joint reliability improvement and maintenance of all-terminal networks, as well as optimizing maintenance for inland waterway transportation systems. Hashemian has also investigated the use of machine learning in improving all-terminal network reliability and exploring self-supervised distillation for missing data in tabular prediction tasks.
His publications also touch upon leveraging customer segmentation and A/B testing for e-book platforms to understand user behavior and drive subscription sales. Hashemian's academic collaborations include work with Haitao Liao, Maryam Aghamohammadghasem, Edward Pohl, and Manuel D. Rossetti at the University of Arkansas at Fayetteville. He has a publication record of 8 papers with 33 citations and an h-index of 3.
Metrics
- h-index: 3
- Publications: 8
- Citations: 34
Positions
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Graduate Research Assistatnt 2022–presentUniversity of Arkansas at Fayetteville Industrial Engineering ORCID
Selected Publications
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SSL-MTab: Self-Supervised Distillation for Missing Data in Tabular Prediction Tasks (2026)
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A Machine Learning Framework for Joint Reliability Improvement and Maintenance of All-Terminal Networks (2026)
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Network Reliability Enhancement Using Machine Learning and Metaheuristics (2026)
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Automated input distribution fitting based on multiple criteria for the Kotlin Simulation Library (2025)
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From User Behavior to Subscription Sales: An Insight Into E-Book Platform Leveraging Customer Segmentation and A/B Testing (2024)
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System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System (2023)
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Applying Machine Learning Methods to Improve All-Terminal Network Reliability (2023)
Collaboration Network
Top Collaborators
- System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System
- Applying Machine Learning Methods to Improve All-Terminal Network Reliability
- A Machine Learning Framework for Joint Reliability Improvement and Maintenance of All-Terminal Networks
- Applying Machine Learning Methods to Improve All-Terminal Network Reliability
- Network Reliability Enhancement Using Machine Learning and Metaheuristics
- A Machine Learning Framework for Joint Reliability Improvement and Maintenance of All-Terminal Networks
- System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System
- Applying Machine Learning Methods to Improve All-Terminal Network Reliability
- System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System
- Automated input distribution fitting based on multiple criteria for the Kotlin Simulation Library
- Network Reliability Enhancement Using Machine Learning and Metaheuristics
- A Machine Learning Framework for Joint Reliability Improvement and Maintenance of All-Terminal Networks
- System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System
- System Simulation And Machine Learning-Based Maintenance Optimization For An Inland Waterway Transportation System
- From User Behavior to Subscription Sales: An Insight Into E-Book Platform Leveraging Customer Segmentation and A/B Testing
- From User Behavior to Subscription Sales: An Insight Into E-Book Platform Leveraging Customer Segmentation and A/B Testing
- Automated input distribution fitting based on multiple criteria for the Kotlin Simulation Library
- SSL-MTab: Self-Supervised Distillation for Missing Data in Tabular Prediction Tasks
- SSL-MTab: Self-Supervised Distillation for Missing Data in Tabular Prediction Tasks
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