جستجوی استاد راهنمای دانشگاهی

Cevdet Aykanatمشاهده پروفایل

استاد

• Parallel Computing
• High-Performance Computing
• Graph and Hypergraph Partitioning
• +۴ مورد دیگر

Prof. Cevdet Aykanat is a Professor of Computer Engineering at Bilkent University, Ankara, Turkey. He earned his BS/MS in Electrical Engineering from METU and PhD from Ohio State University as a Fulbright scholar. His research focuses on parallel computing, sparse matrix algorithms, graph partitioning, and high-performance computing for big data. He has been affiliated with Bilkent since 1989 and has held roles like Associate Provost. Education: BSc/MSc (METU Electrical Engineering), PhD (Ohio State University Electrical & Computer Engineering). Research interests include parallel scientific computing, combinatorial optimization, distributed systems, and large-scale data analysis. His work spans over 100 publications in top journals like IEEE Transactions and SIAM, with 5,500 citations and an H-index of 40. Awards: 1996 TUBITAK Investigator Award, 2007 METU Parlar Science Award. He led 6 TUBITAK projects and participated in EU-funded projects like PRACE-1IP to PRACE-6IP. Publications emphasize efficient parallel algorithms for sparse computations, graph partitioning, and distributed systems. His work addresses latency reduction, load balancing, and scalable data processing in HPC environments. Grants: Funded by TUBITAK, Intel SSD, and EU programs. Academic service includes editorial roles at IEEE Transactions on Parallel and Distributed Systems.

Milad Elyasiمشاهده پروفایل

استادیار

• Transportation and Logistics System Planning
• Manufacturing Systems
• Decomposition Algorithms for Large-Scale Optimization
• +۳ مورد دیگر

Dr. Milad Elyasi is an Assistant Professor in the Department of Industrial Engineering at Özyeğin University. He completed his Ph.D. in Industrial Engineering at Özyeğin University in 2022, following a Master’s degree from K.N. Toosi University of Technology (2012) and a Bachelor’s from University of Tabriz (2010). Education PhD in Industrial Engineering, Özyeğin University (2022) Master’s in Industrial Engineering, K.N. Toosi University of Technology (2012) Bachelor’s in Industrial Engineering, University of Tabriz (2010) His research focuses on large-scale optimization, decomposition algorithms, and combinatorial optimization, with applications in transportation and logistics system planning, manufacturing systems, and optimization under uncertainty. A key aspect of his work involves integrating machine learning to enhance operational efficiency and solve complex industrial and service system challenges. Dr. Elyasi has held postdoctoral research positions at IMT Atlantique (France) and HEC Montreal (Canada), contributing to advancements in optimization methodologies.

• Optimization
• Multi-criteria Decision Making
• Production Planning
• +۲ مورد دیگر

Selin Özpeynirci is a Professor in the Department of Industrial Engineering at the Faculty of Engineering, İzmir University of Economics, where she has been employed since February 2008. Her academic career began as a research assistant at Middle East Technical University's Department of Industrial Engineering from 2002 to 2007, where she also completed all her degrees. She has held visiting researcher positions at prestigious French institutions including Ecole Centrale Paris (2014-2015) and Centrale Supelec (2019-2020), supported by TÜBİTAK 2219 grants. Her educational background includes: B.S. in Industrial Engineering, Middle East Technical University (2002) M.S. in Industrial Engineering, Middle East Technical University (2004) Ph.D. in Industrial Engineering, Middle East Technical University (2007) Özpeynirci's research focuses on optimization methodologies applied to complex industrial systems. She specializes in multi-criteria decision making frameworks that address real-world constraints in production environments. Her work bridges theoretical advances in mathematical programming with practical applications in manufacturing scheduling and resource allocation. Current investigations emphasize interactive algorithms that incorporate human decision-maker preferences into computational models, particularly for sorting and selection problems with equity considerations. Analysis of her 15 most recent publications reveals a strong trajectory in inverse multiple criteria problems and constrained resource allocation. Her work consistently integrates flexible manufacturing systems concepts with advanced decision theory, showing increasing sophistication in handling preference uncertainty and balance requirements. The publications demonstrate methodological evolution from classical scheduling approaches toward hybrid interactive-optimization frameworks that accommodate human judgment in complex decision environments. Her scientific recognition includes: TÜBİTAK 2219 Research Grant for Ecole Centrale Paris (2014-2015) TÜBİTAK 2219 Research Grant for Centrale Supelec (2019-2020) Özpeynirci actively collaborates with international researchers including Vincent Mousseau (France), Özgür Özpeynirci (Turkey), and Burak Gokgur (Turkey) on projects spanning multiple criteria decision analysis and production systems optimization. Her current research agenda explores drone-integrated logistics and advanced decomposition methods for large-scale inverse sorting problems, supported by ongoing institutional affiliations and research grants. While specific lab information isn't provided in the source material, her work clearly operates within the industrial engineering research ecosystem focused on computational decision support systems.

Fahrettin Horasanمشاهده پروفایل

دانشیار

• Machine Learning
• Data Security
• Recommender Systems
• +۳ مورد دیگر

Dr. Fahrettin Horasan is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering and Natural Sciences . His research focuses on applied computational methods in data security, machine learning, and information retrieval systems. Specialized in medical image watermarking and encryption using matrix decomposition techniques (SVD, ULV) Developed novel collaborative filtering recommender systems with hybrid approaches and matrix approximation Contributions in sentiment analysis for healthcare and e-commerce domains His recent publications highlight interdisciplinary work combining cryptology, biomedical imaging, and large-scale data processing. Notable methods include chaotic system-based encryption, latent semantic indexing, and gradient boosting for darknet traffic analysis. While specific awards, educational background, and student advisement details are not publicly detailed in the provided texts, his technical output reflects active engagement in computational science advancements.

Güvenç Şahinمشاهده پروفایل

استاد

• Operations Research
• Transportation Planning
• Electricity Markets
• +۲ مورد دیگر

Güvenç Şahin is a Professor at Sabanci University specializing in Operations Research with applications in transportation systems and energy markets. His research employs advanced optimization techniques to solve complex real-world problems including public transit planning, electricity market design, and supply chain logistics. Research interests span: Transportation optimization (vehicle routing, crew scheduling) Electricity market competition analysis Multi-period resource allocation Agent-based simulation of market behavior Combinatorial algorithm design Publication analysis reveals consistent focus on mathematical optimization in energy and transportation domains. Recent work (2024-2025) emphasizes algorithmic solutions for market collusion detection and vehicle routing problems, while earlier contributions established foundational work in railway optimization and electricity market simulation. No scientific awards are documented in available sources. His collaborative projects demonstrate active engagement with European research institutions on transportation and energy systems. While no dedicated lab is specified, his research involves optimization modeling teams addressing transportation networks and energy market dynamics, frequently utilizing high-performance computing resources for large-scale simulations.