Enes Algül

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.