Research Assistant, Ankara University. PhD Candidate in Computer Engineering.
Ankara University, Graduate School of Natural and Applied Sciences
Department of Computer Engineering. Supervisor: Prof. Dr. Mehmet Serdar Güzel. Status: Ongoing. Thesis title: Deep Reinforcement Learning in Contact-Rich Robotic Manipulation Tasks.
Erciyes University, Department of Computer Engineering
Advisor: Asst. Prof. Dr. Fehim Köylü. Graduated with a GPA of 3.88 / 4.00. Thesis on autonomous system design by modeling vehicle overtaking with deep reinforcement learning.
Süleyman Demirel University, Department of Computer Engineering
Graduated with a GPA of 3.17 / 4.00. Completed English Preparatory Program. Senior project on mobile game development with augmented reality.
Anadolu University, Open Education Faculty
Graduated with a GPA of 2.69 / 4.00.
Ankara University, Department of Artificial Intelligence and Data Engineering
Research and teaching support in artificial intelligence, data engineering, programming, machine learning, and deep learning courses.
Turkish Armed Forces
Completed military service as a reserve officer.
Vega Yazılım
Software development internship.
Electro Mechanic Devices Factory
Hardware-focused internship experience.
Scientific Reports, 16, Article 3971
DOI: 10.1038/s41598-025-34095-z
This study investigates crayfish sex identification using traditional machine learning, deep learning, Kolmogorov-Arnold networks, and autoencoder-based feature extraction. It highlights the potential of hybrid artificial intelligence models for biological classification problems.
Applied Sciences, 14(14), 6211
DOI: 10.3390/app14146211
This study compares deep learning and canonical machine learning techniques for crayfish disease detection using an imbalanced dataset. It explores hybrid approaches that combine deep feature extraction with traditional classifiers.
Overtaking with deep reinforcement learning methods in autonomous vehicles
Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 13(2), 429-439
DOI: 10.28948/ngumuh.1331354
This article studies the autonomous overtaking problem using deep reinforcement learning methods, including curriculum learning-based training strategies for autonomous vehicle decision-making.
Erciyes University, Graduate School of Natural and Applied Sciences. Department of Computer Engineering
Supervisor: Asst. Prof. Dr. Fehim Köylü. Status: Completed.
My master's thesis focused on modeling the autonomous overtaking problem using deep reinforcement learning and designing an autonomous decision-making system for autonomous vehicles.
Ongoing PhD research titled Deep Reinforcement Learning in Contact-Rich Robotic Manipulation Tasks, with an emphasis on insertion and assembly-style robot learning problems.
