I am a joint postdoctoral scholar at the Kavli Institute for Theoretical Physics and the Geometric Intelligence Lab at UC Santa Barbara. I received my PhD in Applied Physics from Stanford University in 2025.

My research lies at the intersection of neuroscience, theoretical physics, machine learning, and computer science. I study how biological and artificial neural networks implement computation, with an emphasis on learning, memory, and mechanistic interpretability.

Website:fatihdinc.github.io

At UC Santa Barbara, I work with Boris Shraiman on modeling morphogenesis using AI, focusing on how local interactions among cells give rise to biological form. With Nina Miolane, I study how large, high-dimensional populations of neurons implement smaller, behaviorally meaningful computations. We develop methods to identify the low-dimensional variables represented by neural populations, characterize the dynamics that update them, and determine when different neural networks implement the same underlying computation.

During my PhD, I worked with Mark Schnitzer and experimental collaborators to study memory formation and retrieval using large-scale brain-imaging data from mice. I developed tools for extracting neural activity and fitting data-constrained recurrent neural networks, including EXTRACT and CORNN. EXTRACT is now used by hundreds of laboratories.

I also study how artificial neural networks learn and represent algorithms. My work uses dynamical systems, optimization, and interpretable models to investigate abrupt learning, memory mechanisms, stability, and trainability.