Fatih Cengil Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
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Research Areas
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Biography and Research Information
OverviewAI-generated summary
Fatih Cengil's research focuses on the intersection of machine learning and complex optimization problems, particularly within power systems and public health logistics. He has investigated methods to accelerate the attainment of globally optimal solutions for the AC Optimal Power Flow (AC-OPF) problem, a critical challenge in power grid management. His work includes developing machine learning approaches to expedite the tightening of convex relaxations for AC-OPF, aiming to improve computational efficiency.
Additionally, Cengil's research extends to the complex scheduling and procurement processes for pediatric vaccines in low- and middle-income countries. This work involves taxonomic reviews of vaccine tender scheduling and procurement strategies to identify areas for improvement and efficiency. His collaborators at the University of Arkansas at Fayetteville include Sandra D. Ekşioğlu and Burak Ekşioğlu, with whom he has co-authored multiple publications.
Metrics
- h-index: 2
- Publications: 6
- Citations: 20
Selected Publications
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Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem (2025)
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Vaccine tender scheduling and procurement: A taxonomic review (2025)
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Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem (2024)
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Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem (2022)
Collaboration Network
Top Collaborators
- Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem
- Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem
- Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Vaccine tender scheduling and procurement: A taxonomic review
Showing 5 of 6 shared publications
- Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem
- Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem
- Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Vaccine tender scheduling and procurement: A taxonomic review
Showing 5 of 6 shared publications
- Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem
- Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem
- Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Vaccine tender scheduling and procurement: A taxonomic review
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Vaccine tender scheduling and procurement: A taxonomic review
- Pediatric vaccine tender scheduling in low- and middle-income countries
- Learning to accelerate tightening of convex relaxations of the AC optimal power flow problem
- Learning to Accelerate Tightening of Convex Relaxations of the AC Optimal Power Flow Problem
- Learning to accelerate globally optimal solutions to the AC Optimal Power Flow problem
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