Hanchuan Peng, Ph.D.
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, Peng was the head of a computational bioimage analysis lab at Howard Hughes Medical Institute, Janelia Farm Research Campus. His recent work includes developing novel algorithms for 3-D+ image analysis and data mining, building single-neuron whole-brain level 3-D digital atlases for model animals, and Vaa3D (http://vaa3d.org), which is a high-performance visualization-assisted analysis system for large 3-D+ biological and biomedical-image datasets. He is also the inventor of the widely cited minimum-redundant maximum-relevance (mRMR) feature selection algorithm in machine learning. Peng received his Ph.D. in biomedical engineering from Southeast University, China. He held postdoctoral positions at the Lawrence Berkeley National Laboratory at the University of California, Berkeley (computational biology, bioinformatics, and high-performance data mining with a particular focus on gene expression analysis) and at Johns Hopkins University Medical School (human brain imaging and analysis). He won several awards, including a Cozzarelli Prize (2013) for his collaborative research on dragonfly neurons, which "recognizes outstanding contributions to the scientific disciplines represented by the National Academy of Sciences (USA)".
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.
- Microscopic image analysis
- Computational neuroscience
- Machine learning & pattern recognition
- Computational biology
- Cell types
- Neural coding
Selected Publications View on PUBMED
July 23, 2018
Yang J, Hao M, Liu X, Wan Z, Zhong N, Peng H
July 17, 2018
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
June 6, 2018
Zhou Z, Kuo HC, Peng H, Long F
June 29, 2017
Li Y, Chen H, Jiang X, Li X, Lv J, Peng H, Tsien JZ, Liu T
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
Peng H, Bria A, Zhou Z, Iannello G, Long F
Gonzalez-Bellido P, Peng H, Yang J, Georgopoulos AP, Olberg RM
Peng H, Long F, & Myers G
Peng H, Chung P, Long F, Qu L, Jenett A, Seeds AM, Myers EW, Simpson JH
Qu L, Long F, Liu X, Myers E, Kim S, Peng H
Peng, H., Ruan, Z., Long, F., Simpson, J.H., and Myers, E.W.
Long, F.*, Peng, H.*, Liu, X., Kim, S. and Myers, E.W.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Peng, H., Long, F., and Ding, C.