معرفی
Mehmet Dikmen serves as an Assistant Professor in the Computer Engineering Department at Başkent University, Ankara, where he teaches courses including Artificial Intelligence, Operating Systems, and Remote Sensing Systems. His academic foundation includes a PhD in Geodesy and Geographic Information Technologies from Middle East Technical University (2014), an MSc in Electrical and Electronics Engineering (2006), and a BSc in Computer Engineering from Başkent University (2003).
Research Expertise: Dr. Dikmen specializes in applying artificial intelligence to solve complex problems across multiple domains. His primary research thrusts include medical imaging analysis (developing zero-shot learning for thorax disease detection), geospatial intelligence (using deep learning for landmine detection and deforestation monitoring), and industrial automation (optimizing manufacturing processes through predictive modeling). His interdisciplinary approach bridges computer vision, remote sensing, and machine learning to address real-world challenges in healthcare, defense, and environmental sustainability.
Analysis of his 22 publications (2012-2025) reveals an evolving trajectory from foundational geospatial work toward cutting-edge AI applications. Early research focused on landslide susceptibility mapping using ANN, while recent efforts emphasize deep learning architectures for 3D manufacturing prediction, multi-agent reinforcement learning in military scenarios, and Parkinson's disease identification. His work consistently demonstrates methodological innovation in data augmentation, transfer learning, and saliency-based analysis.
Academic Contributions: Dr. Dikmen has supervised 14 Master's theses, guiding students through research in air combat simulation, tweet popularity prediction, and vehicle price forecasting. His teaching portfolio spans core computer engineering subjects including Programming, Operating Systems, and specialized AI/remote sensing courses. While no scientific awards are documented, his active publication record and thesis supervision reflect significant academic engagement.
