Berker Banar

Berker Banar is a Researcher and PhD student at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Digital Music (C4DM) under the Music Informatics Research theme. His research focuses on developing generative deep learning systems for composing contemporary classical music, emphasizing interpretable control over musical attributes and expanding aesthetic boundaries through novel compositional methods. He explores intersections of AI, music generation, and artistic creativity, with applications in co-creative tools and potential robotic musicianship.

His work spans technical innovations such as variational autoencoders, latent space regularization, and neuro-symbolic approaches, alongside philosophical inquiries into creativity and machine learning ethics. He has presented tutorials on transformer-based music generation and published extensively on generative AI in music, sound event detection, and adaptive acoustic systems.

Berker's research portfolio demonstrates a focus on both technical advancements in machine learning architectures and their artistic implications, aiming to bridge computational methods with creative expression. His publications reflect a commitment to interdisciplinary exploration at the nexus of computer science, musicology, and AI ethics.