Staff Profiles

Uygar Sümbül joined the Allen Institute as an Assistant Investigator in 2017. His current research at the Institute focuses on the modeling and analysis of neuronal cell type identity based on genetic, anatomical, and physiological data. Previously, Uygar held postdoctoral positions in Sebastian Seung’s lab at MIT, and Liam Paninski’s lab at Columbia University. During these appointments, he developed machine learning methods for multi-modal classification of cell types, and for segmenting individual neurons in multispectral images of the nervous tissue. In particular, his research demonstrated a submicron reproducibility in the anatomy of mouse retinal neurons, and the existence of a consistent mapping between anatomical and molecular definitions of cell types. Uygar received a Bachelor’s degree from Bilkent University (Ankara, Turkey). He earned his PhD in Electrical Engineering and Mathematics from Stanford University, where he studied time-series models of dynamic magnetic resonance imaging.

Research Interests

What defines a neuronal cell type is a long-standing problem in Neuroscience. It is not always clear whether consistent identities can be assigned to neuronal populations across anatomical, molecular, and physiological observations. Uygar’s research tries to address these problems using the multi-modal observations of cortical neurons obtained at the Institute. He develops machine learning methods for classification that take advantage of these large, high-quality datasets. Uygar is also broadly interested in the anatomical organization of the cortex and how this pertains to neuronal identity.

Expertise

• Computational neuroscience
• Machine learning
• Neuroanatomy

Research Programs

• MAT
• Cell type classification