Find Academic Supervisors

• Computer Software
• Programming Languages
• Green Computing
• +3 more

Dr. Lecturer İbrahim ŞANLIALP is a faculty member at the Faculty of Engineering and Architecture, Ahi Evran University, specializing in Computer Hardware and Software Engineering. He holds a Ph.D. (2022) and M.Sc. (2015) in Computer Engineering from Süleyman Demirel University, alongside a B.Sc. in Electronics and Computer Education (2010). Education: Ph.D. in Computer Engineering (Süleyman Demirel University, 2015-2022) M.Sc. in Computer Engineering (Süleyman Demirel University, 2012-2015) B.Sc. in Computer Engineering (Süleyman Demirel University, 2005-2010) Diploma in Electronics and Computer Education (Süleyman Demirel University, 2005-2010) His research focuses on Computer Software and Programming Languages , with recent studies analyzing energy efficiency in code refactoring, landslide prediction using statistical methods, and machine learning applications in public health. His work spans green computing , geospatial analysis , and AI-driven classification systems. Dr. Şanlıalp has contributed to 9 publications, including 2 journal articles (SCI-Exp, TR Dizin) and 7 conference papers. His projects include "3D Environment Path-Finding Algorithms for UAVs" (2025-2026) and "Grid-Based Path Planning Simulation" (2024-2025) as principal investigator. Metrics highlight 26 total citations (Google Scholar), h-index of 1, and active collaborations with researchers from Süleyman Demirel University, Ankara Yıldırım Beyazıt University, and Niğde Ömer Halisdemir University. His teaching portfolio includes courses like "Game Programming", "Computer Concepts", and "Cybercrime" since 2022.

MUZAFFER ASLANView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Associate Professor Muzaffer Aslan is a faculty member at Bingöl University's Faculty of Engineering and Architecture, specializing in applied artificial intelligence research. His work bridges computer science, electrical engineering, and biomedical domains with practical implementations in industrial, medical, and energy systems. His academic journey includes a BSc in Electronic-Computer Education from Gazi University (1993), MSc from Fırat University (2004), and PhD in Electrical-Electronics Engineering from Fırat University (2016). This multidisciplinary foundation enables his cross-domain research approach. Professor Aslan's research centers on developing efficient deep learning solutions for real-world problems. His primary focus areas include medical imaging analysis (brain tumor and COVID-19 detection from X-rays), fall detection systems using depth sensors, emotion recognition from EEG signals, and appliance classification for smart grids. He innovates through hybrid architectures that combine CNNs with signal processing techniques like wavelet transforms and dispersion entropy, achieving high accuracy while maintaining computational efficiency. His publication record shows accelerating output since 2020, with 11 journal articles in 2021-2022 alone spanning medical diagnostics, agricultural technology, and industrial quality control. Recent work demonstrates increasing sophistication in model design, particularly in efficient architectures for resource-constrained environments as seen in his 2023 surface defect detection paper. As Principal Investigator for a TÜBİTAK 1002 project on appliance classification, he secures active research funding while mentoring graduate students. His supervision style emphasizes practical implementation, with students frequently co-authoring publications and contributing to textbook development. The collaborative nature of his work is evident in multi-institutional authorship patterns across his publications.

SADIK ALASHANView profile

Associate Professor

• Hydraulic Engineering
• Hydrology
• Water Resources
• +4 more

Dr. Sadik Alashan serves as Associate Professor in the Faculty of Engineering and Architecture at Bingol University, Turkey, specializing in Civil Engineering disciplines through teaching courses including Hydraulics, Hydrology, Fluid Mechanics, and Water Resources Structures. His academic journey spans over a decade at Bingöl University with progressive appointments from Research Assistant to current Associate Professor status. Education: Ph.D. in Hydraulic and Water Resources Engineering, Istanbul Technical University (2011-2016) M.S. in Hydraulic Engineering, Firat University (2008-2011) B.S. in Civil Engineering, Dicle University (1999-2003) Research Focus: Dr. Alashan pioneers statistical methodologies for hydrological trend analysis, particularly advancing Sen's Innovative Trend Analysis (ITA) into frameworks like S-IPTA and ITA-NF. His work bridges climate science and water engineering through drought assessment in Mediterranean regions, earthquake recurrence modeling in Eastern Turkey, and machine learning applications for hydraulic structures. Recent publications demonstrate evolution from foundational ITA refinements (2016-2020) toward integrated climate-hydrology frameworks (2023-2025), with increasing international collaboration. Scientific Awards: No major scientific awards listed in the provided information Academic Contributions: Dr. Alashan has co-supervised Master's research on climate impacts to river systems and serves as reviewer for leading journals including Water Resources Management and Natural Hazards. His publication trajectory shows consistent output with 15+ articles in the last five years, primarily in Q1 hydrology/climate journals. While no formal lab is specified, his computational research group focuses on methodological innovation for water resource challenges in semi-arid regions. Research Environment: Based in Bingöl (Eastern Turkey), his work addresses regional water security through earthquake-prone basin studies and Mediterranean drought analysis. His recent shift toward machine learning integration (2025 publications) indicates evolving technical capacity within his research group, leveraging computational approaches to solve practical hydraulic engineering problems.

Enes AlgülView profile

Lecturer

• Graph Machine Learning
• Geometric Deep Learning
• Bioinformatics
• +5 more

Enes Algül serves as a Lecturer in the Faculty of Engineering and Architecture at Bingol University, bringing deep expertise in Graph Machine Learning and Geometric Deep Learning developed through his PhD at the University of York and postdoctoral work at the University of Copenhagen. His research bridges theoretical computer science with practical applications in bioinformatics and computer vision. His educational foundation includes: PhD in Computer Science, University of York, UK (2017-2022) MSc in Software Engineering, University of Hertfordshire, UK (2016-2017) Bachelor's in Computer Engineering, Ankara University, Turkey (2009-2014) Research Interests: Algül pioneers methods for transforming complex data into graph and 3D point cloud representations, applying Graph Kernels and Graph Neural Networks to solve challenging problems in RNA structure classification and face recognition. His work uniquely combines graph theory, deep learning, and domain-specific knowledge in bioinformatics, with recent focus on novel graph representations for RNA molecules based on sequence free energy and 3D structural properties. This interdisciplinary approach enables breakthroughs in analyzing biological data and visual recognition systems. Scientific Recognition: Turkish Ministry of National Education Scholarship (2014) - Awarded to top 900 undergraduate students nationally Academic Contributions: Algül teaches advanced courses including Software Engineering, Natural Language Processing, and Object-Oriented Programming at Bingol University. His graduate teaching experience at York demonstrates strong pedagogical skills across Python, Java, and data science curricula. The scholarship supporting his entire graduate education reflects exceptional academic promise, and his recent publication surge in 2023 indicates active research momentum with potential for significant future contributions in geometric deep learning applications. Research Environment: While no dedicated lab is specified, Algül's work spans multiple high-impact domains including computational biology and computer vision. Students would engage with cutting-edge techniques in graph representation learning and neural network architectures applied to real-world data challenges.

Musab CoşkunView profile

Lecturer

• Computer Vision
• Deep Learning
• Robotics
• +3 more

Dr. Musab Coşkun serves as a Lecturer at Bingöl University's Continuing Education Application and Research Center. He maintains active international collaborations through past visiting researcher positions at the University of Koblenz and Landau (Germany) and the University of Malta. His academic credentials include: Bachelor of Science in Electrical and Electronics Engineering, Fırat University (2006-2010) with Erasmus exchange at Bialystok University of Technology, Poland (2008-2009) Master of Science in Electrical and Electronics Engineering, Fırat University (2012-2015) Doctor of Philosophy in Electrical and Electronics Engineering, Fırat University (2015-2022) Dr. Coşkun's research integrates Computer Vision, Deep Learning, and Robotics to solve practical problems in unmanned systems and human-machine interaction. His work spans theoretical algorithm development (e.g., efficient neural networks for sEMG classification) and hardware implementation (FPGA acceleration, UAV systems), with recent emphasis on reinforcement learning for robotic manipulation. The progression from 2016-2017 object tracking studies to 2021-2022 robotic grasping research demonstrates evolving technical sophistication while maintaining core computer vision expertise. His publication record shows consistent output in high-impact venues, with recent work focusing on deployable deep learning solutions for robotics. The 2018 TÜBİTAK-funded project on humanoid robot training algorithms provides evidence of competitive grant acquisition capability. As an early-career lecturer, Dr. Coşkun offers students hands-on experience with cutting-edge robotics and vision systems within Bingöl University's continuing education framework, emphasizing practical implementation skills alongside theoretical foundations.

ONUR POLATView profile

Lecturer

• Network Security
• Software Defined Networking
• Machine Learning
• +3 more

Dr. Onur Polat serves as a Lecturer at Bingol University's Faculty of Engineering and Architecture, specializing in cybersecurity research with focus on Distributed Denial of Service (DDoS) attack detection in Software Defined Networking (SDN) environments. His work extends to critical applications in Vehicular Ad Hoc Networks (VANET) and SCADA systems. Research interests include: Network Security and Cybersecurity Software Defined Networking (SDN) Machine Learning for Intrusion Detection Vehicular Networks Security Industrial Control System Protection AI-driven Real-time Monitoring Analysis of his 9 journal publications (2020-2024) reveals consistent innovation in AI-powered security frameworks, with recent work emphasizing hybrid models combining convolutional networks, decision trees, and ensemble methods for VANET and SCADA environments. His research demonstrates increasing sophistication in handling 'pandemic-scale' attacks and real-time traffic monitoring. Dr. Polat maintains active research collaboration with co-authors including H. Polat and M. Türkoğlu, presenting at IEEE conferences since 2016. Prospective students should note his focus on practical security solutions for emerging network infrastructures, with opportunities to contribute to impactful cybersecurity research in transportation and industrial systems.