Abdulkadir Gokce

I am a first-year Ph.D. student in Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL), where I am advised by Martin Schrimpf. My research focuses on building artificial models of perception.

Previously, I earned my master's degree at EPFL and my bachelor's in Electrical & Electronics Engineering and Mathematics at Bogazici University. I was fortunate to gain research experience at ETH Zurich, MIT, and Nanyang Technological University (NTU).

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Research

My research investigates the extent to which current deep learning models can capture neural and behavioral signals, and aims to develop new modeling strategies that expand these capabilities. I am particularly focused on building scalable, multimodal models of human perception that integrate diverse neurocognitive datasets.

We systematically explored scaling laws for primate vision models and discovered that neural alignment stops improving beyond a certain scale, even though behavior keeps aligning better.

We find that contour integration, a core feature of human object recognition, emerges in models only at large scales and correlates with improved shape bias.

Inspired by human cognitive development, our BabyLLaMA model learns language and vision jointly through a self-synthesis loop, generating its own training data from unlabeled images.

EndoL2H enhances capsule endoscopy images up to 12x using a spatial attention-guided GAN, outperforming existing methods in perceptual quality and clinical relevance.