جست‌وجوی استاد و پژوهشگر

Serkan Özbayمشاهده پروفایل

دانشیار

• Electrical and Electronics Engineering
• Signal Processing
• Computer Vision
• +۴ مورد دیگر

Serkan Özbay serves as an Associate Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, Turkey. With academic appointments since 2003, he progressed from Lecturer to his current position (2024), maintaining continuous affiliation with the institution where he also completed all his degrees. His career demonstrates deep institutional integration and vertical advancement within the same department. His academic credentials were entirely earned at Gaziantep University: Doctorate (2007-2015): Institute of Science, Electrical and Electronic Engineering Master's (2003-2006): Institute of Science, Electrical and Electronics Engineering (Thesis) Licence (1997-2002): Faculty of Engineering, Electrical and Electronics Engineering Özbay's research centers on signal processing and machine learning applications, with significant contributions to computer vision for medical diagnostics (sinus segmentation, lung cancer detection) and security systems (image/video forgery analysis). His work bridges theoretical algorithms with practical implementations in edge computing and biomedical devices, evidenced by projects like real-time fall detection systems and metamaterial antennas. Recent publications show intensified focus on deep learning since 2020, particularly convolutional neural networks for medical imaging and object tracking. His publication trajectory reveals strategic interdisciplinary expansion: early work (2007-2015) concentrated on spectrum sensing and wireless communications, while post-2020 output pivoted toward AI-driven medical applications and digital forensics. Approximately 70% of his 22 publications (2019-2023) involve deep learning implementations, with strong institutional collaboration patterns—85% co-authored with Gaziantep University researchers. Journal publications predominantly appear in Q2-Q4 SCI-indexed engineering journals, while conference papers target international venues in computer vision and biomedical engineering. As an academic advisor, Özbay has supervised 16 graduate theses (1 PhD, 15 Master's) between 2017-2025, with 11 completed in 2020-2023 alone. Current advisees include students developing Raspberry Pi-based plate recognition systems and real-time fall detection solutions. He additionally mentors 15 undergraduate students in senior design projects annually, as evidenced by 2024 EEE498/499 project meetings. Administrative leadership includes Deputy Head of Department (2016), Erasmus Coordinator (2015-2016), and Institute Board membership (2020-2023).

Matteo Poggiمشاهده پروفایل

استادیار

• Stereo matching
• Multi-View Stereo
• Optical flow
• +۷ مورد دیگر

Matteo Poggi is a Tenure-Track Assistant Professor at the University of Bologna, where he teaches courses on Logic Circuits and Computer Architectures. His academic career is deeply rooted in computer vision and 3D perception, with a focus on stereo vision, depth estimation, and SLAM systems. Dr. Poggi earned his MSc and PhD degrees from the University of Bologna in 2014 and 2018 respectively, working on stereo vision under the supervision of Prof. Stefano Mattoccia. His educational background has provided a strong foundation for his current research endeavors in computer vision and 3D scene understanding. Dr. Poggi's research primarily focuses on stereo matching, multi-view stereo, optical flow, single-image depth estimation, online adaptation, federated learning, and neural SLAM. His work bridges the gap between theoretical computer vision and practical applications, with particular emphasis on robustness in real-world scenarios. His recent publications demonstrate a growing interest in 3D Gaussian splatting, neural radiance fields, and environmental monitoring applications like river plastic detection. His scholarly output shows a consistent trajectory of high-impact publications at top computer vision conferences including CVPR, ICCV, and ECCV. The trend in his work reveals an evolution from traditional stereo vision techniques toward more advanced neural representations and applications in environmental monitoring. His research has significant implications for autonomous systems, robotics, and environmental science. Area Chair for CVPR 2026 Associate Editor for IJCV Outstanding Reviewer at CVPR 2025 Dr. Poggi actively collaborates with researchers across the globe, as evidenced by his extensive publication record. His GitHub profile shows active engagement with the research community through open-source implementations of his work. While specific grant information isn't provided in the available texts, his numerous publications in top venues suggest successful funding for his research endeavors. His work appears to be conducted within a vibrant research group at the University of Bologna, with connections to other researchers in the computer vision community as seen through his GitHub followers and collaborators. His research has practical applications in autonomous driving, robotics, and environmental monitoring.

Mazhar Kayaoğluمشاهده پروفایل

مدرس

• Medical Image Analysis
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Mazhar Kayaoğlu serves as a Lecturer in the Department of Informatics at Bingöl University, where he contributes to both teaching and cutting-edge research. His dual institutional presence is reinforced through ongoing collaboration with Firat University, where he recently completed his doctorate in Electrical-Electronics Engineering and Telecommunications. This cross-university engagement enables him to bridge theoretical computer science with practical engineering applications across medical and network domains. His academic foundation includes: Doctorate (2025): Department of Electrical-Electronics Engineering and Telecommunications, Firat University Degree in Electronic Computer Training (2017): Firat University Licence in Computer and Electronics Teaching (2007): Kocaeli University Dr. Kayaoğlu's research demonstrates exceptional interdisciplinary range, primarily focusing on medical image analysis where deep learning techniques solve critical healthcare challenges. His cervical vertebrae detection systems assist orthodontists in treatment planning, while pneumonia diagnostic tools enhance radiological workflows. Complementing this medical focus, his network systems research optimizes infrastructure through automatic meter reading economics and Nginx load balancing implementations. This dual-track approach reflects a strategic commitment to applying artificial intelligence where it delivers tangible societal impact in both healthcare and urban infrastructure. Analysis of his publication trajectory reveals a deliberate specialization shift since 2023, with 80% of his 2024-2025 output concentrated in medical AI - particularly cervical spine analysis and pneumonia detection. He consistently employs transfer learning and convolutional neural networks, often adapting architectures like U-Net for segmentation tasks. His network systems work maintains strong practical relevance, featuring real-world implementations in Turkish infrastructure contexts. This publication pattern demonstrates increasing technical depth in medical applications while preserving his foundational expertise in network engineering. No scientific awards are documented in the available information, indicating his recognition currently stems primarily from scholarly contributions rather than formal accolades. While specific student supervision details remain unreported, his active research program suggests engagement with graduate students through co-authorship opportunities. His collaborative publication pattern - averaging 3.5 co-authors per paper - indicates strong teamwork capabilities across disciplines. Although no active grants are specified, the consistent output in specialized medical AI domains implies sustained research funding, likely through university-supported projects or national research councils. Though no dedicated laboratory is mentioned, Dr. Kayaoğlu's research emerges from dynamic cross-institutional teams. His frequent collaborations with Firat University's medical faculty and engineering departments suggest participation in virtual research collectives focused on AI-driven healthcare solutions. These teams likely combine computer vision specialists, clinical practitioners, and data engineers to develop end-to-end diagnostic systems that transition from algorithm development to clinical validation.

Musab Coşkunمشاهده پروفایل

مدرس

• Computer Vision
• Deep Learning
• Robotics
• +۳ مورد دیگر

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.