Joel Zylberberg Laboratory

Saad Idrees

Postdoctoral Scholar

Saad works at the intersection of neuroscience and machine learning, developing interpretable, accurate, and generalizable computational models of retinal circuits under natural viewing conditions. He uses these models to investigate how the retina encodes information, particularly at scales that are difficult to access experimentally. His broader interests include using biologically grounded models to probe mechanisms underlying retinal diseases, and developing bio-inspired AI architectures that enable generalization to unfamiliar conditions.

Fazil Onuralp Ardic(he/him)

PhD Student

Fazil Onuralp develops machine learning models of retinal activity to better understand neural computations underlying vision. His research focuses on modeling retinal ganglion cell responses to dynamic visual stimuli, with an emphasis on temporal processing, neural dynamics, and context-dependent visual encoding.

Sakin Kirti(he/him)

PhD Student, Bioinformatics IDP

With a background in machine learning, Sakin studies how the retina encodes visual information, using information theory and machine learning to ask why the retina has so many distinct ganglion cell types. He is also interested in comparing computer vision models to the retina, with the goal of using insights from biological vision to build better computer vision systems.

Katheryn Zhou(she/her)

PhD Student

Katheryn studies learning rules within biological and artificial neural networks. She is interested in the intersection between theoretical neuroscience and experimental neuroscience.