I'm Dilvan Sabir, an engineer and researcher interested in understanding complex systems through data.

My work sits at the intersection of machine learning, data engineering, software development, and applied research. Over the years I've built systems for digital pathology, multimodal learning, EEG-based emotion recognition, large-scale knowledge graphs, and data-intensive applications. I enjoy working across the entire stack—from designing data pipelines and infrastructure to developing machine learning models and interactive visualizations.

I recently completed a Master's in Data Engineering and Analytics at the Technical University of Munich (TUM). My thesis explored how audio features, lyrics, metadata, and brain signals can be combined to better understand emotional responses to music and guide generative visual systems.

More broadly, I'm fascinated by how information emerges from complex signals—whether those signals come from brains, music, biological systems, or human knowledge. That curiosity has led me to explore topics ranging from neuroscience and psychology to philosophy, physics, and linguistics.

Outside of engineering, you'll usually find me reading Southern Gothic literature, wandering around with a camera looking for interesting light, or disappearing down a rabbit hole about something I knew nothing about the day before.