Farid Hashemian
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Farid Hashemian's research focuses on the application of machine learning and simulation techniques to enhance the reliability and maintenance of complex systems. His work includes developing machine learning frameworks for joint reliability improvement and maintenance optimization, particularly for all-terminal networks. Hashemian has also investigated methods for system simulation and machine learning-based maintenance optimization in contexts such as inland waterway transportation. His publications explore techniques for improving network reliability through machine learning and metaheuristics, as well as the application of machine learning to address missing data in tabular prediction tasks.
He has collaborated with researchers including Haitao Liao, Maryam Aghamohammadghasem, Edward Pohl, and Manuel D. Rossetti at the University of Arkansas at Fayetteville. Hashemian's work also touches upon user behavior analysis for e-book platforms, involving customer segmentation and A/B testing to understand subscription sales drivers. He has authored eight publications with an h-index of 2 and 29 citations.
Metrics
- h-index: 3
- Publications: 8
- Citations: 32
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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