Education
- B.S. in Science, Shandong University, 1997-2001
- M.S. in Engineering, Tsinghua University, 2001-2004
- Ph.D. in Engineering, Virginia Tech, 2006-2011
Q Lab
QUIC Lab
Quantification Intelligence and Computation Lab
QUIC Lab develops quantitative, intelligent, and computational methods for understanding multiscale imaging, spatiotemporal signals, cellular dynamics, and neural information in living systems.
Principal Investigator
Prof. Guoqiang Yu
Prof. Guoqiang Yu is a Professor in the Department of Automation at Tsinghua University. He has long been dedicated to developing computational and quantitative methods for biological imaging, cellular dynamics, and neural information analysis, with the goal of making complex living systems measurable and interpretable.
Research Interests
- Machine learning
- Statistical models and optimization techniques
- Large-scale 4D image analysis
- Computational brain science
- Bioinformatics
Work Experience
- Researcher, Nuctech, Tsinghua Tongfang, 2004-2006
- Postdoctoral scholar, Stanford University, 2011-2012
- Assistant Professor, Department of Electrical and Computer Engineering, Virginia Tech, 2012-2018
- Associate Professor, Department of Electrical and Computer Engineering, Virginia Tech, 2018-2022
- Professor, Department of Electrical and Computer Engineering, Virginia Tech, 2022-2023
- Professor, Department of Automation, Tsinghua University, 2023-present
- Researcher, Tsinghua University McGovern Institute for Brain Research, 2024-present
- Researcher, Beijing National Research Center for Information Science and Technology, 2024-present
Research Highlights
Quantitative intelligence for biological data
Our lab builds computational methods that connect imaging, signals, cellular motion, and neural information with scalable quantitative models.
Spatiotemporal Molecular Signal Quantification (AQuA2)
AQuA2 (Activity Quantification and Analysis) is a tool for quantifying spatiotemporal signals across biosensors, cell types, organs, animal models and imaging modalities of biological fluorescent imaging data.
Open AQuA2Image TODO
Electron Microscopy Connectomics
Building methods for large-scale electron microscopy data analysis, including neural structure reconstruction, organelle and synapse identification, connectome mapping, and scalable data processing. The goal is to turn dense ultrastructural data into reliable biological measurements.
Read more
3D Embryo Cell Tracking (ITEC)
Developing methods for accurate cell tracking and lineage reconstruction in 3D+t imaging data.
Open ITEC
3D Cell Segmentation (FOCUS-3D)
Developing foundation models for generalizable 3D cell and nuclei instance segmentation across organisms, tissues, and imaging modalities.
Open FOCUS-3DSelected Publications
Representative work
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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals
Xuelong Mi, Alex Bo-Yuan Chen, Daniela Duarte, Erin Carey, Charlotte R. Taylor, Philipp N. Braaker, Mark Bright, Rafael G. Almeida, Jing-Xuan Lim, Virginia M. S. Ruetten, Yizhi Wang, Mengfan Wang, Weizhan Zhang, Wei Zheng, Michael E. Reitman, Yongkang Huang, Xiaoyu Wang, Lei Li, HanFei Deng, Song-Hai Shi, Kira E. Poskanzer, David A. Lyons, Axel Nimmerjahn, Misha B. Ahrens, and Guoqiang Yu
Cell
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Data science and its future in large neuroscience collaborations
Manuel Schottdorf, Guoqiang Yu, and Edgar Y. Walker
Neuron
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Efficient Global Multi-object Tracking Under Minimum-cost Circulation Framework
Congchao Wang, Yizhi Wang, and Guoqiang Yu
IEEE Transactions on Pattern Analysis and Machine Intelligence
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SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images
Yizhi Wang, Congchao Wang, Petter Ranefall, Gerard Joey Broussard, Yinxue Wang, Guilai Shi, Boyu Lyu, Yue Wang, Lin Tian, and Guoqiang Yu
Bioinformatics
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Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology
Yizhi Wang, Nicole V. DelRosso, Trisha V. Vaidyanathan, Michelle K. Cahill, Michael E. Reitman, Silvia Pittolo, Xuelong Mi, Guoqiang Yu, and Kira E. Poskanzer
Nature Neuroscience