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Ji, Shuiwang Professor
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Shuiwang Ji is currently an Associate Professor in the Department of Computer Science & Engineering, Texas A&M University, leading the Data Integration, Visualization, and Exploration (DIVE) Laboratory. Ji received the Ph.D. degree in Computer Science from Arizona State University in 2010, advised by Prof. Jieping Ye. His research interests include machine learning, data mining, and computational neuroscience. Ji received the National Science Foundation CAREER Award in 2014. He has authored over 80 research articles and has coauthored a book. Currently, Ji serves as an Action Editor for Data Mining and Knowledge Discovery, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Neural Networks and Learning Systems, and BMC Bioinformatics. Ji is a Program Chair for the 2017 Bioimage Informatics Conference and a senior member of IEEE.
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Overview
Publications
selected publications
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academic article
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Manubens-Gil, L., Zhou, Z., Chen, H., Ramanathan, A., Liu, X., Liu, Y., ... Peng, H.
(2024).
Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. Nature Methods.
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Patel, K., Xie, Z., Yuan, H., Islam, S., Xie, Y., He, W., ... Zhi, D.
(2024).
Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Communications Biology.
7(1), 414.
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Gui, S., Yuan, H., Wang, J., Lao, Q., Li, K., & Ji, S.
(2023).
FlowX: Towards Explainable Graph Neural Networks via Message Flows. IEEE Transactions on Pattern Analysis and Machine Intelligence.
PP(99), 1-12.
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Manubens-Gil, L., Zhou, Z., Chen, H., Ramanathan, A., Liu, X., Liu, Y., ... Peng, H.
(2023).
BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. Nature Methods.
20(6), 824-835.
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Wang, Z., & Ji, S.
(2023).
Second-Order Pooling for Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(6), 6870-6880.
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Cai, L., Wang, Z., Kulathinal, R., Kumar, S., & Ji, S.
(2023).
Deep Low-Shot Learning for Biological Image Classification and Visualization From Limited Training Samples. IEEE Transactions on Neural Networks and Learning Systems.
34(5), 2528-2538.
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Yuan, H., Yu, H., Gui, S., & Ji, S.
(2023).
Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(5), 5782-5799.
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Xu, X., Deng, C., Xie, Y., & Ji, S.
(2023).
Group Contrastive Self-Supervised Learning on Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(3), 3169-3180.
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Wang, J., Zhang, Z., Shi, Z., Cai, J., Ji, S., & Wu, F.
(2023).
Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(2), 1652-1667.
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Xie, Y., Xu, Z., Zhang, J., Wang, Z., & Ji, S.
(2023).
Self-Supervised Learning of Graph Neural Networks: A Unified Review. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(2), 2412-2429.
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Xu, X., Wang, Z., Deng, C., Yuan, H., & Ji, S.
(2023).
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping. IEEE Transactions on Pattern Analysis and Machine Intelligence.
45(1), 1189-1200.
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Liu, M., Wang, Z., & Ji, S.
(2022).
Non-Local Graph Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
44(12), 10270-10276.
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Xie, Y., Ding, Y. u., & Ji, S.
(2022).
Augmented Equivariant Attention Networks for Microscopy Image Transformation. IEEE Transactions on Medical Imaging.
41(11), 3194-3206.
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Gao, H., & Ji, S.
(2022).
Graph U-Nets. IEEE Transactions on Pattern Analysis and Machine Intelligence.
44(9), 4948-4960.
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Cai, L., Li, J., Wang, J., & Ji, S.
(2022).
Line Graph Neural Networks for Link Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence.
44(9), 5103-5113.
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Wang, Z., Liu, M., Luo, Y., Xu, Z., Xie, Y., Wang, L., ... Ji, S.
(2022).
Advanced graph and sequence neural networks for molecular property prediction and drug discovery. Bioinformatics.
38(9), 2579-2586.
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Yuan, H., Cai, L., Hu, X., Wang, J., & Ji, S.
(2022).
Interpreting Image Classifiers by Generating Discrete Masks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
44(4), 2019-2030.
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Liu, Y. i., & Ji, S.
(2021).
CleftNet: Augmented Deep Learning for Synaptic Cleft Detection From Brain Electron Microscopy. IEEE Transactions on Medical Imaging.
40(12), 3507-3518.
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Gao, H., Liu, Y. i., & Ji, S.
(2021).
Topology-Aware Graph Pooling Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
43(12), 4512-4518.
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Yuan, H., Yang, F., Du, M., Ji, S., & Hu, X.
(2021).
Towards structured NLP interpretation via graph explainers. Applied AI Letters.
2(4),
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Gao, H., Wang, Z., Cai, L., & Ji, S.
(2021).
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions. IEEE Transactions on Pattern Analysis and Machine Intelligence.
43(8), 2570-2581.
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Wang, Y., Cai, L., Chen, W., Wang, D., Xu, S., Wang, L., ... Xian, M.
(2021).
Development of XantheneBased Fluorescent Dyes: Machine LearningAssisted Prediction vs. TDDFT Prediction and Experimental Validation. Chemistry - Methods.
1(8), 389-396.
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Xu, X., Deng, C., Xie, Y., & Ji, S.
(2021).
Group Contrastive Self-Supervised Learning on Graphs
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Wang, Z., & Ji, S.
(2021).
Smoothed dilated convolutions for improved dense prediction. Data Mining and Knowledge Discovery.
35(4), 1470-1496.
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Liu, M., Fu, C., Zhang, X., Wang, L., Xie, Y., Yuan, H., ... Ji, S.
(2021).
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
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Liu, M., Luo, Y., Wang, L., Xie, Y., Yuan, H., Gui, S., ... Ji, S.
(2021).
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
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Wang, Z., Xie, Y., & Ji, S.
(2021).
Global voxel transformer networks for augmented microscopy. Nature Machine Intelligence.
3(2), 161-171.
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Yuan, H., Yu, H., Wang, J., Li, K., & Ji, S.
(2021).
On Explainability of Graph Neural Networks via Subgraph Explorations
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Xie, Y., Xu, Z., Zhang, J., Wang, Z., & Ji, S.
(2021).
Self-Supervised Learning of Graph Neural Networks: A Unified Review
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Liu, Y. i., Wang, L., Liu, M., Zhang, X., Oztekin, B., & Ji, S.
(2021).
Spherical Message Passing for 3D Graph Networks
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Jin, W., Li, Y., Xu, H., Wang, Y., Ji, S., Aggarwal, C., & Tang, J.
(2021).
Adversarial Attacks and Defenses on Graphs. ACM SIGKDD Explorations Newsletter.
22(2), 19-34.
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Liu, M., Yan, K., Oztekin, B., & Ji, S.
(2021).
GraphEBM: Molecular Graph Generation with Energy-Based Models
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Yuan, H., Yu, H., Gui, S., & Ji, S.
(2020).
Explainability in Graph Neural Networks: A Taxonomic Survey
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Zhang, W., Li, R., Zeng, T., Sun, Q., Kumar, S., Ye, J., & Ji, S.
(2020).
Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis. 6(2), 322-333.
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Liu, Y. i., Yuan, H., Wang, Z., & Ji, S.
(2020).
Global Pixel Transformers for Virtual Staining of Microscopy Images. IEEE Transactions on Medical Imaging.
39(6), 2256-2266.
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Gao, H., Yuan, H., Wang, Z., & Ji, S.
(2020).
Pixel Transposed Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.
42(5), 1218-1227.
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Feng, Y., Yang, F., Zhou, X., Guo, Y., Tang, F., Ren, F., Guo, J., & Ji, S.
(2019).
A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection. 16(6), 1794-1801.
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Banerjee, D., Islam, K., Xue, K., Mei, G., Xiao, L., Zhang, G., ... Li, J.
(2019).
A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. Knowledge and Information Systems.
60(3), 1693-1724.
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Yuan, H., Cai, L., Wang, Z., Hu, X., Zhang, S., & Ji, S.
(2019).
Computational modeling of cellular structures using conditional deep generative networks. Bioinformatics.
35(12), 2141-2149.
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Cai, L., Gao, H., & Ji, S.
(2019).
Multi-stage variational auto-encoders for coarse-to-fine image generation. 630-638.
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Wang, Z., Yuan, H., & Ji, S.
(2019).
Spatial variational auto-encoding via matrix-variate normal distributions. 648-656.
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Li, Z., Butler, E., Li, K., Lu, A., Ji, S., & Zhang, S.
(2018).
Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality. NeuroInformatics.
16(3-4), 339-349.
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Cai, L., Wu, B., & Ji, S.
(2018).
Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli. NeuroInformatics.
16(3-4), 473-488.
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Zhang, L., Zhao, Y., Zhu, Z., Shen, D., & Ji, S.
(2018).
Multi-View Missing Data Completion. IEEE Transactions on Knowledge and Data Engineering.
30(7), 1296-1309.
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Li, Z., Yu, J., Hu, H., & Ji, S.
(2018).
Three-dimensional protein shape similarity analysis based on hybrid features. Gene.
663, 138-147.
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Zeng, T., Wu, B., & Ji, S.
(2017).
DeepEM3D: approaching human-level performance on 3D anisotropic EM image segmentation. Bioinformatics.
33(16), 2555-2562.
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Jie Gui, .., Zhenan Sun, .., Shuiwang Ji, .., Dacheng Tao, .., & Tieniu Tan.
(2017).
Feature Selection Based on Structured Sparsity: A Comprehensive Study. IEEE Transactions on Neural Networks and Learning Systems.
28(7), 1490-1507.
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Li, R., Zeng, T., Peng, H., & Ji, S.
(2017).
Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction. IEEE Transactions on Medical Imaging.
36(7), 1533-1541.
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Li, J., Chen, Y., Cai, L., Davidson, I., & Ji, S.
(2017).
Dense Transformer Networks
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Gao, H., Yuan, H., Wang, Z., & Ji, S.
(2017).
Pixel Deconvolutional Networks
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Fakhry, A., Zeng, T., & Ji, S.
(2017).
Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation. IEEE Transactions on Medical Imaging.
36(2), 447-456.
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Fakhry, A., Peng, H., & Ji, S.
(2016).
Deep models for brain EM image segmentation: novel insights and improved performance. Bioinformatics.
32(15), 2352-2358.
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Li, F., Tran, L., Thung, K., Ji, S., Shen, D., & Li, J.
(2015).
A Robust Deep Model for Improved Classification of AD/MCI Patients. IEEE Journal of Biomedical and Health Informatics.
19(5), 1610-1616.
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Zeng, T., Chen, H., Fakhry, A., Hu, X., Liu, T., & Ji, S.
(2015).
Allen mouse brain atlases reveal different neural connection and gene expression patterns in cerebellum gyri and sulci. 220(5), 2691-2703.
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Fakhry, A., Zeng, T., Peng, H., & Ji, S.
(2015).
Global analysis of gene expression and projection target correlations in the mouse brain. Brain Informatics.
2(2), 107-117.
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Zhang, W., Li, R., Feng, D., Chernikov, A., Chrisochoides, N., Osgood, C., & Ji, S.
(2015).
Evolutionary soft co-clustering: formulations, algorithms, and applications. Data Mining and Knowledge Discovery.
29(3), 765-791.
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Zeng, T., Li, R., Mukkamala, R., Ye, J., & Ji, S.
(2015).
Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. BMC Bioinformatics.
16(1), 147.
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Li, R., Zhang, W., Zhao, Y., Zhu, Z., & Ji, S.
(2015).
Sparsity Learning Formulations for Mining Time-Varying Data. IEEE Transactions on Knowledge and Data Engineering.
27(5), 1411-1423.
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Zhang, W., Li, R., Deng, H., Wang, L. i., Lin, W., Ji, S., & Shen, D.
(2015).
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NEUROIMAGE.
108, 214-224.
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Fakhry, A., & Ji, S.
(2015).
High-resolution prediction of mouse brain connectivity using gene expression patterns. Methods.
73, 71-78.
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Li, R., Zhang, W., & Ji, S.
(2014).
Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns. BMC Bioinformatics.
15(1), 209.
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Gui, J., Sun, Z., Cheng, J., Ji, S., & Wu, X.
(2014).
How to Estimate the Regularization Parameter for Spectral Regression Discriminant Analysis and its Kernel Version?. IEEE transactions on circuits and systems for video technology (Print).
24(2), 211-223.
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Yuan, L., Pan, C., Ji, S., McCutchan, M., Zhou, Z., Newfeld, S. J., Kumar, S., & Ye, J.
(2014).
Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression. Bioinformatics.
30(2), 266-273.
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Li, R., Zhang, W., Suk, H., Wang, L. i., Li, J., Shen, D., & Ji, S.
(2014).
Deep learning based imaging data completion for improved brain disease diagnosis. Lecture Notes in Computer Science.
17(Pt 3), 305-312.
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Ji, S., Fakhry, A., & Deng, H.
(2014).
Integrative analysis of the connectivity and gene expression atlases in the mouse brain. NEUROIMAGE.
84, 245-253.
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Li, F., Tran, L., Thung, K., Ji, S., Shen, D., & Li, J.
(2014).
Robust Deep Learning for Improved Classification of AD/MCI Patients. Lecture Notes in Computer Science.
8679, 240-247.
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Zhang, W., Feng, D., Li, R., Chernikov, A., Chrisochoides, N., Osgood, C., ... Ji, S.
(2013).
A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. BMC Bioinformatics.
14(1), 372.
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Sun, Q., Muckatira, S., Yuan, L., Ji, S., Newfeld, S., Kumar, S., & Ye, J.
(2013).
Image-level and group-level models for Drosophila gene expression pattern annotation. BMC Bioinformatics.
14(1), 350.
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Ji, S., Zhang, W., & Li, R.
(2013).
A probabilistic latent semantic analysis model for coclustering the mouse brain atlas. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
10(6), 1460-1468.
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Ji, S.
(2013).
Computational genetic neuroanatomy of the developing mouse brain: dimensionality reduction, visualization, and clustering. BMC Bioinformatics.
14(1), 222.
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Liu, X., Ji, S., Glanzel, W., & De Moor, B.
(2013).
Multiview Partitioning via Tensor Methods. IEEE Transactions on Knowledge and Data Engineering.
25(5), 1056-1069.
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Ji, S., Yang, M., & Yu, K.
(2013).
3D convolutional neural networks for human action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence.
35(1), 221-231.
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Gui, J., Sun, Z., Jia, W., Hu, R., Lei, Y., & Ji, S.
(2012).
Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recognition.
45(8), 2884-2893.
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Yuan, L., Woodard, A., Ji, S., Jiang, Y., Zhou, Z., Kumar, S., & Ye, J.
(2012).
Learning sparse representations for fruit-fly gene expression pattern image annotation and retrieval. BMC Bioinformatics.
13(1), 107.
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Li, Y., Ji, S., Kumar, S., Ye, J., & Zhou, Z.
(2012).
Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
9(1), 98-112.
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Ji, S.
(2011).
Computational network analysis of the anatomical and genetic organizations in the mouse brain. Bioinformatics.
27(23), 3293-3299.
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Kumar, S., Konikoff, C., Van Emden, B., Busick, C., Davis, K. T., Ji, S., ... Newfeld, S. J.
(2011).
FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis. Bioinformatics.
27(23), 3319-3320.
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Sun, L., Ji, S., & Ye, J.
(2011).
Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
33(1), 194-200.
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Ji, S., Tang, L., Yu, S., & Ye, J.
(2010).
A Shared-Subspace Learning Framework for Multi-Label Classification. ACM Transactions on Knowledge Discovery from Data.
4(2), 8-29.
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Pong, T. K., Tseng, P., Ji, S., & Ye, J.
(2010).
TRACE NORM REGULARIZATION: REFORMULATIONS, ALGORITHMS, AND MULTI-TASK LEARNING. SIAM Journal on Optimization.
20(6), 3465-3489.
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Ji, S., Li, Y., Zhou, Z., Kumar, S., & Ye, J.
(2009).
A bag-of-words approach for Drosophila gene expression pattern annotation. BMC Bioinformatics.
10(1), 119.
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Ji, S., & Ye, J.
(2008).
Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Transactions on Neural Networks and Learning Systems.
19(10), 1768-1782.
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Ji, S., & Ye, J.
(2008).
Kernel uncorrelated and regularized discriminant analysis: A theoretical and computational study. IEEE Transactions on Knowledge and Data Engineering.
20(10), 1311-1321.
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Ji, S., Sun, L., Jin, R., Kumar, S., & Ye, J.
(2008).
Automated annotation of Drosophila gene expression patterns using a controlled vocabulary. Bioinformatics.
24(17), 1881-1888.
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Ye, J., Ji, S., & Chen, J.
(2008).
Multi-class discriminant kernel learning via convex programming. JOURNAL OF MACHINE LEARNING RESEARCH.
9, 719-758.
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Sun, L., Ji, S., & Ye, J.
(2008).
Adaptive diffusion kernel learning from biological networks for protein function prediction. BMC Bioinformatics.
9(1), 162.
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Choudhary, K., Wines, D., Li, K., Garrity, K. F., Gupta, V., Romero, A. H., ... Tavazza, F.
JARVIS-Leaderboard: a large scale benchmark of materials design methods. npj Computational Materials.
10(1), 93.
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Mohseni, S., Yang, F., Pentyala, S., Du, M., Liu, Y. i., Lupfer, N., ... Ragan, E.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection. 15, 421-431.
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Manubens-Gil, L., Zhou, Z., Chen, H., Ramanathan, A., Liu, X., Liu, Y., ... Peng, H.
(2024).
Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets. Nature Methods.
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book
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Sun, L., Ji, S., & Ye, J.
(2016).
Multi-Label Dimensionality Reduction
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Sun, L., Ji, S., & Ye, J.
(2016).
Multi-Label Dimensionality Reduction
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chapter
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Ye, J., & Ji, S.
(2009).
Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments. Biometrics.
(pp. 1-19).
Wiley.
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Ye, J., & Ji, S.
(2009).
Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments. Biometrics.
(pp. 1-19).
Wiley.
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conference paper
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Xu, M., Liu, M., Jin, W., Ji, S., Leskovec, J., & Ermon, S.
(2023).
Graph and Geometry Generative Modeling for Drug Discovery. 5833-5834.
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Ji, S., Liu, M., Liu, Y. i., Luo, Y., Wang, L., Xie, Y., Xu, Z., & Yu, H.
(2022).
Frontiers of Graph Neural Networks with DIG. 4796-4797.
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Yang, R., Wang, J., Geng, Z., Ye, M., Ji, S., Li, B., & Wu, F.
(2022).
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions. 2242-2252.
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Dong, Y., Ding, K., Jalaian, B., Ji, S., & Li, J.
(2021).
AdaGNN. 392-401.
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Mao, H., Liu, X. i., Duffleld, N., Yuan, H., Ji, S., & Mohanty, B.
(2020).
Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
392-401.
-
Wang, Z., Sisman, B., Wei, H., Dong, X. L., & Ji, S.
(2020).
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage. 1322-1327.
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Liu, Y. i., Yuan, H., Cai, L., & Ji, S.
(2020).
Deep Learning of High-Order Interactions for Protein Interface Prediction. 679-687.
-
Gao, H., Wang, Z., & Ji, S.
(2020).
Kronecker Attention Networks. 229-237.
-
Liu, M., Gao, H., & Ji, S.
(2020).
Towards Deeper Graph Neural Networks. 338-348.
-
Yuan, H., Tang, J., Hu, X., & Ji, S.
(2020).
XGNN: Towards Model-Level Explanations of Graph Neural Networks. 430-438.
-
Wang, Z., Zou, N. a., Shen, D., & Ji, S.
(2020).
Non-Local U-Nets for Biomedical Image Segmentation. Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
6315-6322.
-
Cai, L., & Ji, S.
(2019).
An Efficient Policy Gradient Method for Conditional Dialogue Generation. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
31-40.
-
Yuan, H., Zou, N. a., Zhang, S., Peng, H., & Ji, S.
(2019).
Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
806-815.
-
Liu, Y. i., Yuan, H., & Ji, S.
(2019).
Learning Local and Global Multi-Context Representations for Document Classification. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
1234-1239.
-
Li, J., Chen, Y., Cai, L., Davidson, I., & Ji, S.
(2019).
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation. 2894-2900.
-
Gao, H., & Ji, S.
(2019).
Graph Representation Learning via Hard and Channel-Wise Attention Networks. 741-749.
-
Du, M., Liu, N., Yang, F., Ji, S., & Hu, X.
(2019).
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition. 383-393.
-
Yang, F., Pentyala, S. K., Mohseni, S., Du, M., Yuan, H., Linder, R., ... Hu, X. B.
(2019).
XFake: Explainable Fake News Detector with Visualizations. 3600-3604.
-
Gao, H., Chen, Y., & Ji, S.
(2019).
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. 2743-2749.
-
Zhang, Y., Liu, N., Ji, S., Caverlee, J., & Hu, X.
(2019).
An Interpretable Neural Model with Interactive Stepwise Influence. Lecture Notes in Computer Science.
528-540.
-
Chen, Y., Shi, M., Gao, H., Shen, D., Cai, L., & Ji, S.
(2018).
Voxel Deconvolutional Networks for 3D Brain Image Labeling. KDD : proceedings / International Conference on Knowledge Discovery & Data Mining. International Conference on Knowledge Discovery & Data Mining.
1226-1234.
-
Cai, L., Wang, Z., Gao, H., Shen, D., & Ji, S.
(2018).
Deep Adversarial Learning for Multi-Modality Missing Data Completion. 1158-1166.
-
Gao, H., Wang, Z., & Ji, S.
(2018).
Large-Scale Learnable Graph Convolutional Networks. 1416-1424.
-
Wang, Z., & Ji, S.
(2018).
Smoothed Dilated Convolutions for Improved Dense Prediction. 2486-2495.
-
Wang, Z., & Ji, S.
(2018).
Learning Convolutional Text Representations for Visual Question Answering. 594-602.
-
Banerjee, D., Islam, K., Mei, G., Xiao, L., Zhang, G., Xu, R., Ji, S., & Li, J.
(2017).
A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
11-20.
-
Gao, H., & Ji, S.
(2017).
Efficient and Invariant Convolutional Neural Networks for Dense Prediction. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
871-876.
-
Hu, X., & Ji, S.
(2017).
IDM 2017: Workshop on Interpretable Data Mining - Bridging the Gap between Shallow and Deep Models. 2565-2566.
-
Zeng, T., Wu, B., Zhou, J., Davidson, I., & Ji, S.
(2017).
Recurrent Encoder-Decoder Networks for Time-Varying Dense Prediction. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
1165-1170.
-
Wang, Q. i., Sun, M., Zhan, L., Thompsen, P., Ji, S., & Zhou, J.
(2017).
Multi-Modality Disease Modeling via Collective Deep Matrix Factorization. 1155-1164.
-
Li, R., Si, D., Zeng, T., Ji, S., & He, J.
(2016).
Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine.
41-46.
-
Zhang, L., Wang, S., Zhang, X., Wang, Y., Li, B., Shen, D., & Ji, S.
(2016).
Collaborative Multi-View Denoising. 2045-2054.
-
Lin, K., Xu, J., Baytas, I. M., Ji, S., & Zhou, J.
(2016).
Multi-Task Feature Interaction Learning. 1735-1744.
-
Li, Q., Qiu, S., Ji, S., Thompson, P. M., Ye, J., & Wang, J.
(2016).
Parallel Lasso Screening for Big Data Optimization. 1705-1714.
-
Sammons, D., Winfree, W. P., Burke, E., & Ji, S.
(2016).
Segmenting Delaminations in Carbon Fiber Reinforced Polymer Composite CT using Convolutional Neural Networks. AIP Conference Proceedings.
110014.
-
Yang, T., Zhao, X., Lin, B., Zeng, T., Ji, S., & Ye, J.
(2015).
Automated gene expression pattern annotation in the mouse brain. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
144-155.
-
Zeng, T., & Ji, S.
(2015).
Deep Convolutional Neural Networks for Multi-Instance Multi-Task Learning. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
579-588.
-
Zhang, W., Li, R., Zeng, T., Sun, Q., Kumar, S., Ye, J., & Ji, S.
(2015).
Deep Model Based Transfer and Multi-Task Learning for Biological Image Analysis. 1475-1484.
-
Yang, S., Sun, Q., Ji, S., Wonka, P., Davidson, I., & Ye, J.
(2015).
Structural Graphical Lasso for Learning Mouse Brain Connectivity. 1385-1394.
-
Li, R., Zhang, W., Suk, H., Wang, L. i., Li, J., Shen, D., & Ji, S.
(2014).
Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis. Lecture Notes in Computer Science.
305-312.
-
Zhang, W., Ji, S., & Zhang, R.
(2013).
Evolutionary Soft Co-Clustering. 121-129.
-
Zhang, W., Ji, S., & Zhang, R.
(2013).
Evolutionary soft co-clustering. 121-129.
-
Si, D., Ji, S., Nasr, K. A., & He, J.
(2012).
A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers.
698-708.
-
Ji, S., Zhang, W., & Liu, J.
(2012).
A sparsity-inducing formulation for evolutionary co-clustering. 334-342.
-
Bian, W.
(2011).
Preface to the Large Scale Visual Analytics Workshop. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
xliii-xliv.
-
Wang, F., He, J., Li, T., Ding, C., Yong, C., He, J., ... Zhu, J.
(2011).
Workshop on Optimization Based Methods for Emerging Data Mining Problems. Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining.
xxvii-xxviii.
-
Ji, S., Xu, W., Yang, M., & Yu, K.
(2010).
3D convolutional neural networks for human action recognition. 495-502.
-
Sun, L., Ji, S., & Ye, J.
(2009).
A least squares formulation for a class of generalized eigenvalue problems in machine learning. 977-984.
-
Ji, S., & Ye, J.
(2009).
An accelerated gradient method for trace norm minimization. 457-464.
-
Ji, S., & Ye, J.
(2009).
Linear Dimensionality Reduction for Multi-label Classification. IJCAI International Joint Conference on Artificial Intelligence.
1077-1082.
-
Ji, S., Sun, L., Jin, R., & Ye, J.
(2009).
Multi-label multiple kernel learning. 777-784.
-
Liu, J., Ji, S., & Ye, J.
(2009).
Multi-task feature learning via efficient
2, 1 -norm minimization. 339-348.
-
Sun, L., Ji, S., Yu, S., & Ye, J.
(2009).
On the Equivalence Between Canonical Correlation Analysis and Orthonormalized Partial Least Squares. IJCAI International Joint Conference on Artificial Intelligence.
1230-1235.
-
Ji, S., Yuan, L., Li, Y., Zhou, Z., Kumar, S., & Ye, J.
(2009).
Drosophila Gene Expression Pattern Annotation Using Sparse Features and Term-Term Interactions. KDD : proceedings / International Conference on Knowledge Discovery & Data Mining. International Conference on Knowledge Discovery & Data Mining.
407-415.
-
Shen, B., Ji, S., & Ye, J.
(2009).
Mining Discrete Patterns via Binary Matrix Factorization. 757-765.
-
Sun, L., Ji, S., & Ye, J.
(2009).
A least squares formulation for a class of generalized eigenvalue problems in machine learning. 977-984.
-
Ji, S., & Ye, J.
(2009).
An accelerated gradient method for trace norm minimization. 457-464.
-
Li, Y., Ji, S., Kumar, S., Ye, J., & Zhou, Z.
(2009).
Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label Learning. IJCAI International Joint Conference on Artificial Intelligence.
1445-1450.
-
Ji, S., Tang, L., Yu, S., & Ye, J.
(2008).
Extracting shared subspace for multi-label classification. 381-389.
-
Sun, L., Ji, S., & Ye, J.
(2008).
Hypergraph spectral learning for multi-label classification. 668-676.
-
Chen, J., Ji, S., Ceran, B., Li, Q. i., Wu, M., & Ye, J.
(2008).
Learning subspace kernels for classification. 106-114.
-
Ji, S., & Ye, J.
(2008).
A Unified Framework for Generalized Linear Discriminant Analysis. Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
1-7.
-
Sun, L., Ji, S., & Ye, J.
(2008).
A least squares formulation for canonical correlation analysis. 1024-1031.
-
Ye, J., Ji, S., & Chen, J.
(2007).
Learning the Kernel Matrix in Discriminant Analysis via Quadratically Constrained Quadratic Programming. 854-863.
-
Ye, J., Chen, J., & Ji, S.
(2007).
Discriminant kernel and regularization parameter learning via semidefinite programming. 1095-1102.
-
Xu, M., Liu, M., Jin, W., Ji, S., Leskovec, J., & Ermon, S.
(2023).
Graph and Geometry Generative Modeling for Drug Discovery. 5833-5834.
-
institutional repository document
-
Patel, K., Xie, Z., Yuan, H., Islam, M. S., Zhang, W., Gottlieb, A., ... Zhi, D.
(2022).
New phenotype discovery method by unsupervised deep representation learning empowers genetic association studies of brain imaging
-
Manubens-Gil, L., Zhou, Z., Chen, H., Ramanathan, A., Liu, X., Liu, Y., ... Peng, H.
(2022).
BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology
-
Liu, M., Luo, Y., Uchino, K., Maruhashi, K., & Ji, S.
(2022).
Generating 3D Molecules for Target Protein Binding
-
Luo, Y., McThrow, M., Au, W. Y., Komikado, T., Uchino, K., Maruhashi, K., & Ji, S.
(2022).
Automated Data Augmentations for Graph Classification
-
Qi, Q. i., Luo, Y., Xu, Z., Ji, S., & Yang, T.
(2021).
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
-
Dong, Y., Ding, K., Jalaian, B., Ji, S., & Li, J.
(2021).
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
-
Luo, Y., Yan, K., & Ji, S.
(2021).
GraphDF: A Discrete Flow Model for Molecular Graph Generation
-
Wang, Z., Liu, M., Luo, Y., Xu, Z., Xie, Y., Wang, L., ... Ji, S.
(2020).
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
-
Cai, L., Wang, Z., Kulathinal, R., Kumar, S., & Ji, S.
(2020).
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
-
Wang, Z., Sisman, B., Wei, H., Dong, X. L., & Ji, S.
(2020).
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
-
Wang, Z., Xie, Y., & Ji, S.
(2020).
Global Voxel Transformer Networks for Augmented Microscopy
-
Mohseni, S., Yang, F., Pentyala, S., Du, M., Liu, Y. i., Lupfer, N., ... Ragan, E.
(2020).
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
-
Yang, F., Pentyala, S. K., Mohseni, S., Du, M., Yuan, H., Linder, R., ... Hu, X.
(2019).
XFake: Explainable Fake News Detector with Visualizations
-
Gao, H., & Ji, S.
(2019).
Graph U-Nets
-
Wang, Z., Zou, N. a., Shen, D., & Ji, S.
(2018).
Non-local U-Net for Biomedical Image Segmentation
-
Patel, K., Xie, Z., Yuan, H., Islam, M. S., Zhang, W., Gottlieb, A., ... Zhi, D.
(2022).
New phenotype discovery method by unsupervised deep representation learning empowers genetic association studies of brain imaging
Research
principal investigator on
- III: Small: Collaborative Research: Demystifying Deep Learning on Graphs: From Basic Operations to Applications awarded by Directorate for Computer & Information Science & Engineering - (Arlington, Virginia, United States) 2020 - 2023
- Collaborative Research: ABI Innovation: Towards Computational Exploration of Large-Scale Neuro-Morphological Datasets awarded by Directorate for Biological Sciences - (Arlington, Virginia, United States) 2020 - 2021
- III: Medium: Collaborative Research: Towards Scalable and Interpretable Graph Neural Networks awarded by Directorate for Computer & Information Science & Engineering - (Arlington, Virginia, United States) 2020 - 2021
- BIGDATA: Collaborative Research: F: Efficient and Exact Methods for Big Data Reduction awarded by Directorate for Computer & Information Science & Engineering - (Arlington, Virginia, United States) 2018 - 2021
- CAREER: Towards the Next Generation of Data-Driven awarded by Directorate for Biological Sciences - (Arlington, Virginia, United States) 2018 - 2021
- III: Small: Deep Learning for Gene Expression Pattern Image Analysis awarded by Directorate for Computer & Information Science & Engineering - (Arlington, Virginia, United States) 2018 - 2021
- Deep Learning for Connectomics awarded by National Institute of Neurological Disorders and Stroke - (Bethesda, Maryland, United States) 2017 - 2021
- III: Small: Collaborative Research: Structured Methods for Multi-Task Learning awarded by Directorate for Computer & Information Science & Engineering - (Arlington, Virginia, United States) 2018 - 2020
Teaching
teaching activities
- CSCE291 Hnr - Research Instructor
- CSCE291 Hnr-research Instructor
- CSCE421 Machine Learning Instructor
- CSCE489 Sptp: Machine Learning Instructor
- CSCE491 Hnr-research Instructor
- CSCE636 Deep Learning Instructor
- CSCE636 Deep Learning Instructor
- CSCE684 Professional Internship Instructor
- CSCE685 Directed Studies Instructor
- CSCE691 Research Instructor
Works By Students
chaired theses and dissertations
-
Gao, Hongyang (2020-06). Graph Neural Networks: A Feature and Structure Learning Approach.
-
Wang, Zhengyang (2020-10). Deep Attention Networks for Images and Graphs.
-
Yuan, Hao (2021-06). Towards Explainable Deep Models for Images, Texts, and Graphs.
Background
education and training
- Ph.D. in Computer Science, Arizona State University - (Tempe, Arizona, United States) 2010
awards and honors
- Enhancing Development and Generating Excellence in Scholarship (EDGES) Fellow, conferred by Texas A&M University - (College Station, Texas, United States), 2024
- Fellow, conferred by Institute of Electrical and Electronics Engineers - (Piscataway, New Jersey, United States), 2023
- Presidential Impact Fellows, conferred by Texas A&M University - (College Station, Texas, United States), 2021
Contact
first name
- Shuiwang
has last name
- Ji
Other
- sji@tamu.edu