Dr. Jing Liu | Connectomics | Best Researcher Award 

Dr. Jing Liu completed his academic journey with a Ph.D. in Pattern Recognition and Intelligence Systems from the University of Chinese Academy of Sciences, where he studied from September 2016 to January 2022. Prior to his doctoral studies, he earned his Bachelor’s degree in Computer Science and Technology from Northwestern Polytechnical University, graduating in July 2016. Dr. Liu’s educational background reflects a strong foundation in both computer science and neuroscience, positioning him as an expert in applying artificial intelligence to brain research and connectomics.

Work Experience 💼:

Dr. Jing Liu currently serves as an Assistant Professor at the Institute of Automation, Chinese Academy of Sciences, a position he has held since February 2022. In this role, he works at the Laboratory of Brain Atlas and Brain-inspired Intelligence, focusing on advanced research in connectomics and volume electron microscopy. Prior to this, he completed his doctoral studies at the University of Chinese Academy of Sciences, where he developed deep learning-based algorithms for synapse reconstruction and analysis at the nanoscale. Dr. Liu has collaborated with renowned researchers, including Professor Peace Cheng at Peking University, on major connectomic projects. His expertise lies at the intersection of artificial intelligence and neuroscience, making significant contributions to understanding brain connectivity.

Research Interests:

Dr. Jing Liu’s research interests lie at the intersection of artificial intelligence and neuroscience, with a focus on connectomics and volume electron microscopy. He is particularly interested in developing deep learning-based algorithms for the automatic reconstruction of synapses at the nanoscale, aiming to enhance the understanding of brain connectivity. His work includes synapse organization analysis in the mouse auditory cortex and cochlea, as well as exploring the computational techniques for analyzing large-scale neural networks. Dr. Liu is dedicated to advancing the field of brain-inspired intelligence through innovative methods in image segmentation, 3D reconstruction, and neural network modeling.

📚 Publications 

1. A novel 3D instance segmentation network for synapse reconstruction from serial electron microscopy images
   • Authors: Liu, J., Hong, B., Xiao, C., … Xie, Q., Han, H.
   • Journal: Expert Systems with Applications, 2024, 255, 124562
2. Spatial patterns of noise-induced inner hair cell ribbon loss in the mouse mid-cochlea
   • Authors: Lu, Y., Liu, J., Li, B., … Hua, Y.
   • Journal: iScience, 2024, 27(2), 108825
   • Citations: 3
3. SegNeuron: 3D Neuron Instance Segmentation in Any EM Volume with a Generalist Model
   • Authors: Zhang, Y., Guo, J., Zhai, H., Liu, J., Han, H.
   • Conference: Lecture Notes in Computer Science, 2024, 15008 LNCS, pp. 589–600
4. An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography
   • Authors: Chang, S., Li, L., Hong, B., … Chen, X.
   • Journal: BMC Biology, 2023, 21(1), 198
   • Citations: 2
5. Graph partitioning algorithms with biological connectivity decisions for neuron reconstruction in electron microscope volumes
   • Authors: Hong, B., Liu, J., Shen, L., … Emrouznejad, A.
   • Journal: Expert Systems with Applications, 2023, 222, 119776
   • Citations: 5
6. Intra-and Inter-Cellular Awareness for 3D Neuron Tracking and Segmentation in Large-Scale Connectomics
   • Authors: Zhai, H., Liu, J., Hong, B., … Han, H.
   • Conference: Proceedings of Machine Learning Research, 2023, 227, pp. 1691–1712
7. Planar to Spatial: a Synapse Reconstruction Method to Rebuild Voxel Connections for Anisotropic Serial EM Images
   • Authors: Guo, J., Liu, J., Hong, B., … Xu, Y., Han, H.
   • Conference: Proceedings – International Symposium on Biomedical Imaging, 2023, 2023-April
8. Joint reconstruction of neuron and ultrastructure via connectivity consensus in electron microscope volumes
   • Authors: Hong, B., Liu, J., Zhai, H., … Xie, Q., Han, H.
   • Journal: BMC Bioinformatics, 2022, 23(1), 453
   • Citations: 2
9. Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data
   • Authors: Liu, J., Qi, J., Chen, X., … Yang, Y.
   • Journal: Cell Reports, 2022, 40(5), 111151
   • Citations: 8
10. Deep residual contextual and subpixel convolution network for automated neuronal structure segmentation in micro-connectomics

• Authors: Xiao, C., Hong, B., Liu, J., … Xie, Q., Han, H.
• Journal: Computer Methods and Programs in Biomedicine, 2022, 219, 106759
• Citations: 5

Conclusion