Team

Staff Profiles

Uygar Sümbül, Ph.D.

Assistant Investigator

uygars@alleninstitute.org

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

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

Selected Publications

The Markov link method: a nonparametric approach to combine observations from multiple experiments

bioRxiv
October 30, 2018

Loper J, Bakken T, Sumbul U, Murphy G, Zeng H, Bleib D, Paninski L

Automated scalable segmentation of neurons from multispectral images

Advances in Neural Information Processing Systems
2016

Sümbül U, Roossien D, Cai D, Chen F, Barry N, Cunningham JP, Boyden E, Paninski L

Neuronal cell types and connectivity: lessons from the retina

Neuron
September 17, 2014

Seung HS, Sümbül U

A genetic and computational approach to structurally classify neuronal types

Nature Communications
March 24, 2014

Sümbül U, Song S, McCulloch K, Becker M, Lin B, Sanes JR, Masland RH, Seung HS

Automated computation of arbor densities: a step toward identifying neuronal cell types

Frontiers in Neuroanatomy
November 25, 2014

Sümbül U, Zlateski A, Vishwanathan A, Masland RH, Seung HS