人工智能中的数学基础，类脑智能，深度学习，神经影像。

乔琛，西安交通大学数学统计学院信息科学系教授，博士生导师，陕西省工业与应用数学学会常务理事，西安交通大学苏州研究院脑科学实验室主任。本硕博均毕业于西安交通大学。先后赴美国 Tulane University 生物医学工程系进行博士后研究工作，以及到访英国 University of Liverpool、澳大利亚 The University of Queensland 和 University of Technology Sydney、新加坡 Nanyang Technological University 开展合作研究。

1. 国家自然科学基金委员会,面上项目,12271429,基于自稀疏和特征深度选择的深度学习可解释性模型、方法及应用,2023-01至2026-12,在研,主持

2. 国家自然科学基金委员会,重大项目,12090021,强不适定性数学反问题与医学成像新原理和新方法,2021-01至2025-12,在研,课题负责人

3. 科学技术部,科技创新2030-“新一代人工智能”重大项目,2020AAA0106300,认知计算基础理论与方法研究,2020-11至 2024-10,在研,骨干

4. 科学技术部,国家重点研发计划项目,2022YFA1004200,超快智能磁共振成像数学理论与关键技术,2022-12至2027-11, 在研,骨干

5. 国家自然科学基金委员会，数学天元基金-数学与医疗健康交叉重点专项，12226007，多期对比增强超低剂量CT成像方法及应用，2023-01至2024-12,在研,骨干

6. 陕西省科技厅,面上项目,2022JM-005,深层特征选择技术的深度学习架构及其在精神疾病脑异常研究中的应用,2022-01至2023-12,在研,主持

7. 西安市科技局,高校重大科技创新平台建设项目, 2019421315KYPT-004JC006,大数据深度学习算法集成、模型训练及调度平台开发,2019-07至2021-06,结题,主持

[1] Lan Yang, Chen Qiao∗, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang. Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences from Three fMRI Paradigms, IEEE Trans. Biomedical Engineering, DOI: 10.1109/TBME.2023.3244921

[2] Faming Xu, Chen Qiao∗, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang. An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development, Neural Networks, 2023, 159, 185-197

[3] Jiajia Li, Faming Xu, Na Gao, Yuanqiang Zhu, Yuewen Hao, Chen Qiao∗. Sparse non-convex regularization based explainable DBN in the analysis of brain abnormalities in schizophrenia, Computers in Biology and Medicine, 2023, 155, 106664

[4] Aiju Yu, Longyun Chen, Chen Qiao∗. Graph Convolutional Network with Attention Mechanism for Discovering the Brain's Abnormal Activity of Attention Deficit Hyperactivity Disorder, 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2022, 1-5, doi: 10.1109/CISP-BMEI56279.2022.9979902

[5] Chen Qiao, Lan Yang, Yan Shi, Hanfeng Fang and Yanmei Kang. Deep belief networks with self-adaptive sparsity. Applied Intelligence, 2022, 52, 237-253

[6] Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M. Plis, Yu-Ping Wang, Jing Sui and Vince D. Calhoun. Deep learning in neuroimaging: promises and challenges. IEEE Signal Processing Magazine, 2022, 39(2): 87-98

[7] Qi Huang, Chen Qiao*, Kaili Jing, Xu Zhu, Kai Ren. Biomarkers identification for Schizophrenia via VAE and GSDAE-based data augmentation. Computers in Biology and Medicine, 2022, 146, 105603

[8] Chen Qiao, Xin-Yu Hu, Li Xiao, Vince D. Calhoun and Yu-Ping Wang. A deep autoencoder with sparse and graph Laplacian regularization for characterizing dynamic functional connectivity during brain development, Neurocomputing, 2021, 456, 97-108

[9] Chen Qiao, Lan Yang, Vince D. Calhoun, Zong-Ben Xu and Yu-Ping Wang. Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study, Neural Networks, 2021, 135, 91-104

[10] Chen Qiao, Yan Shi, Yu-Xian Diao, Vince D. Calhoun and Yu-Ping Wang. Log-sum enhanced sparse deep neural network. Neurocomputing, 2020, 407(24), 206-220

[11] Chen Qiao, Bin Gao, Yan Shi. SRS-DNN: a deep neural network with strengthening response sparsity. Neural Computing and Applications, 2020, 32(12), 8127-8142

[12] Lan Yang, Shun Qi, Chen Qiao*, Yanmei Kang. Exploring the abnormal brain regions and abnormal functional connections in SZ by multiple hypothesis testing techniques. CMES-Computer Modeling in Engineering & Sciences, 2020, 125(1), 215-237

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