Staff Profiles

Hanchuan Peng, Ph.D.

Director, Advanced Computing

Hanchuan Peng joined the Allen Institute in 2012 to build a computational neuroanatomy and smart imaging group for the Institute's new initiatives in neural coding and cell types. His current research focuses on bioimage analysis, large-scale informatics, machine learning, as well as computational biology. Before joining the Allen Institute, Hanchuan was the head of a computational bioimage analysis lab at Howard Hughes Medical Institute, Janelia Research Campus. His recent work includes developing novel artificial intelligence algorithms for 3-D+ image analysis and data mining, building single-neuron whole-brain level 3-D digital atlases for model animals, and Vaa3D (, which is a high-performance visualization-assisted analysis system for large 3-D+ biological and biomedical-image datasets.

Hanchuan is also the inventor of the widely cited minimum-redundant maximum-relevance (mRMR) feature selection algorithm in machine learning. He was recognized by several awards, including a Cozzarelli Prize (2013), which "recognizes outstanding contributions to the scientific disciplines represented by the National Academy of Sciences (USA)". In 2019, he was inducted as a Fellow into American Institute for Medical and Biological Engineering (AIMBE) for “outstanding contributions to massive-scale imaging informatics including visualization, management, analysis, digital-atlas modeling, and global collaboration of bioimage- and brain-informatics.


Research Interests

My research group focuses on two areas. One is to develop novel computational techniques to aid large-scale bioimage data acquisition, high-throughput data analysis/mining, and high-performance visualization. The other is to use these techniques to study significant biological problems, especially in the areas of neuroscience, systems biology, and microscopic imaging. Recently, I have been conducting the following specific studies: Developing fast, accurate and robust image analysis and machine learning methods for 3-D image registration, segmentation, and recognition in the context of large-scale cell biology, systems biology, and neuroscience applications; Building 3-D whole-brain digital atlases for model animals including Drosophila (fruit fly) and C. elegans at single cell or single neurite tract resolution. Developing techniques for mouse brain network reconstruction; Reconstructing neuronal networks and gene regulatory networks, and analyzing gene expression at single-cell resolution; Developing "smart" microscopic imaging techniques based on computer vision, machine learning, and signal processing approaches; Developing a high-performance 3D/4D/5D visualization-assisted analysis platform for large 3D+ biological and biomedical image data sets; Correlative microscopy and analysis of multiscale multimodality microscopic image data for biological discoveries. Some of these studies have been published in Nature Biotechnology, Nature Methods, Cell, etc. My algorithms and software packages have been used in many research labs worldwide.


  • Neuroanatomy
  • Microscopic image analysis
  • Computational neuroscience
  • Informatics
  • Machine learning & pattern recognition
  • Computational biology
  • Bioinformatics

Research Programs

  • Cell types
  • Neural coding
  • Atlasing

Selected Publications View on PUBMED

TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain

Nature Communications
August 2, 2019

Wang Y, Li Q, Liu L, Zhou Z, Ruan Z, Kong L, Li Y, Wang Y, Zhong N, Chai R, Luo X, Guo Y, Hawrylycz M, Luo Q, Gu Z, Xie W, Zeng H, Peng H

Classification of electrophysiological and morphological neuron types in mouse visual cortex

Nature Neuroscience
June 17, 2019

Gouwens NW, Sorensen SA, Berg J, Lee C, Jarsky T, Ting J, Sunkin S, Feng D, Anastassiou C, Barkan E, Bickley K, Blesie N, Braun T, Brouner K, Budzillo A, Caldejon S, Casper T, Castelli D, Chong P, Crichton K, Cuhaciyan C, Daigle T, Dalley R, Dee N, Desta T, Dingman S, Doperalski A, Dotson N, Egdorf T, Fisher M, de Frates RA, Garren E, Garwood M, Gary A, Gaudreault N, Godfrey K, Gorham M, Gu H, Habel C, Hadley K, Harrington J, Harris J, Henry A, Hill D, Josephsen S, Kebede S, Kim L, Kroll M, Lee B, Lemon T, Liu X, Long B, Mann R, McGraw M, Mihalas S, Mukora A, Murphy GJ, Ng L, Ngo K, Nguyen TN, Nicovich PR, Oldre A, Park D, Parry S, Perkins J, Potekhina L, Reid D, Robertson M, Sandman D, Schroedter M, Slaughterbeck C, Soler-Llavina C, Sulc J, Szafer A, Tasic B, Taskin N, Teeter C, Thatra N, Tung H, Wakeman W, Williams G, Young R, Zhou Z, Farrell C, Peng H, Hawrylycz MJ, Lein E, Ng L, Arkhipov A, Bernard A, Phillips J, Zeng H, Koch C

FMST: an Automatic Neuron Tracing Method Based on Fast Marching and Minimum Spanning Tree

July 23, 2018

Yang J, Hao M, Liu X, Wan Z, Zhong N, Peng H

DeepNeuron: an open deep learning toolbox for neuron tracing

Brain Informatics
June 6, 2018

Zhou Z, Kuo HC, Peng H, Long F

Discover mouse gene coexpression landscapes using dictionary learning and sparse coding

June 29, 2017

Li Y, Chen H, Jiang X, Li X, Lv J, Peng H, Tsien JZ, Liu T

Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis

Nature Communications
July 11, 2014

Peng H, Tang J, Xiao H, Bria A, Zhou J, Butler V, Zhou Z, Gonzalez-Bellido PT, Oh SW, Chen J, Mitra A, Tsien RW, Zeng H, Ascoli GA, Iannello G, Hawrylycz M, Myers E, Long F

Extensible visualization and analysis for multidimensional images using Vaa3D

Nature Protocols
January 2014

Peng H, Bria A, Zhou Z, Iannello G, Long F

Automatic tracing of 3D neuron structures using all-path pruning

July 2011

Peng H, Long F, & Myers G

BrainAligner: 3D registration atlases of Drosophila brains

Nature Methods
June 2011

Peng H, Chung P, Long F, Qu L, Jenett A, Seeds AM, Myers EW, Simpson JH

Simultaneous recognition and segmentation of cells: application in C. elegans

October 2011

Qu L, Long F, Liu X, Myers E, Kim S, Peng H

V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets

Nature Biotechnology

Peng, H., Ruan, Z., Long, F., Simpson, J.H., and Myers, E.W.

A 3D digital atlas of C. elegans and its application to single-cell analyses

Nature Methods

Long, F.*, Peng, H.*, Liu, X., Kim, S. and Myers, E.W.

Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
August 2005

Peng, H., Long, F., and Ding, C.