谭老师研究组目前尚有学博指标2个（申请考核制，已毕业硕士也可以），研究方向包括大模型、具身指能（智能机器人）等。谭老师研究方向可以参考他本人主页或者google scholar，请有意向同学与谭老师联系，邮箱mingkuitan@scut.edu.cn
2002.09-2006.06 湖南大学 环境科学与工程学院 环境工程（学士）
2006.09-2009.06 湖南大学 电气与信息工程学院 控制科学与工程（硕士）
2010.01-2014.10 新加坡南洋理工大学 计算机学院 计算机科学（博士）
(1)机器学习（全英课程）
使用教材： Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David学时：48课时（32 教学 + 16 实验）
(2)深度学习（全英课程）
使用教材：Deep Learning Tutorial, LISA LAB, University of Montreal 学时：32课时（24教学 + 4实验 + 4专题报告）
(3)人工智能前沿与软件工程
学时：16课时（16教学）
2009.07-2009.12 新加坡南洋理工大学 研究助理
2013.09-2014.05 新加坡南洋理工大学 副研究员
2014.06-2016.06 澳大利亚阿德莱德大学 高级副研究员
2016.09-至今 华南理工大学软件学院 教授
担任国际会议审稿人：
CVPR, NeurIPS, ICML, ICLR, ICCV, ECCV, AAAI, IJCAI, ICLR, ACMMM, MICCAI, ACML, AISTATS
1、超高维数据分析：特征选择、大规模矩阵恢复、大规模优化
2、深度学习及应用：网络模型压缩、网络结构自动优化、可解释性和泛化性能分析
3、复杂结构数据分析：Low-level图像处理、医疗图像分析、视频内容理解、3D数据分析
(1)论文“Towards Ultrahigh Dimensional Feature Selection for Big Data” 荣获ICCM (世界华人数学家联盟) 2019最佳论文奖
(2)论文“Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries”荣获2019年MICCAI Workshop on Ophthalmic Medical Image Analysis最佳论文奖
(3)华南理工大学建校65周年校长基金“最具科研潜质”奖
(4)2019年“TVP腾讯云最具价值专家”奖
近年来主要论文如下：
期刊论文
[1] Runhao Zeng, Chuang Gan, Peihao Chen, Wenbing Huang, Qingyao Wu, and Mingkui Tan*. Breaking Winner-takes-all: Iterative-winners-out Networks for Weakly Supervised Temporal Action Localization. TIP, 2019.
[2] Yong Guo, Qi Chen, Jian Chen, Qingyao Wu, Qinfeng Shi, and Mingkui Tan*. Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. TMM, 2019.
[3] Fan Lyu, Qi Wu, Fuyuan Hu, Qingyao Wu, and Mingkui Tan*. Attend and Imagine: Multi-label Image Classification with Visual Attention and Recurrent Neural Networks. TMM, 2019.
[4] Mingkui Tan, Zhibin Hu, Yuguang Yan, Jiezhang Cao, Dong Gong, and Qingyao Wu. Learning Sparse PCA with Stabilized ADMM Method on Stiefel Manifold. TKDE, 2019.
[5] Peilin Zhao, Yifan Zhang, Min Wu, Steven CH Hoi, Mingkui Tan*, and Junzhou Huang. Adaptive Cost-sensitive online Classification. TKDE, 2018.
[6] Dong Gong, Mingkui Tan†, Qinfeng Shi, Anton van den Hengel, and Yanning Zhang. Mptv: Matching Pursuit-based Total Variation Minimization for Image Deconvolution. TIP, 2018.
[7] Qingyao Wu, Mingkui Tan†, Xutao Li, Huaqing Min, and Ning Sun. Nmfe-sscc: Non-negative Matrix Factorization Ensemble for Semi-supervised Collective Classification. KBS, 2015.
[8] Mingkui Tan, Ivor W Tsang, and Li Wang. Towards Ultrahigh Dimensional Feature Selection for Big Data. JMLR, 2014.
[9] Mingkui Tan, Ivor W Tsang, and Li Wang. Matching Pursuit LASSO Part I: Sparse Recovery over Big Dictionary. TSP, 2014.
[10] Mingkui Tan, Ivor W Tsang, and Li Wang. Matching Pursuit Lasso Part ii: Applications and Sparse Recovery over Batch Signals. TSP, 2014.
[11] Mingkui Tan, Ivor W Tsang, and Li Wang. Minimax Sparse Logistic Regression for Very High-dimensional Feature Selection. TNNLS, 2013.
会议论文
[1] Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan*, and Chuang Gan. Location-aware Graph Convolutional Networks for Video Question Answering. In AAAI, 2020.
[2] Jiezhang Cao, Langyuan Mo, Yifan Zhang, Kui Jia, Chunhua Shen, and Mingkui Tan*. Multi-marginal wasserstein gan. In NeurIPS, 2019.
[3] Yong Guo, Yin Zheng, Mingkui Tan*, Qi Chen, Jian Chen, Peilin Zhao, and Junzhou Huang. NAT: Neural Architecture Transformer for Accurate and Compact Architectures. In NeurIPS, 2019.
[4] Runhao Zeng, Wenbing Huang, Mingkui Tan*, Yu Rong, Peilin Zhao, Junzhou Huang, and Chuang Gan. Graph Convolutional Networks for Temporal Action Localization. In ICCV, 2019.
[5] Pengshuai Yin, Qingyao Wu, Yanwu Xu, Huaqing Min, Ming Yang, Yubing Zhang, and Mingkui Tan*. PM-Net: Pyramid Multi-label Network for Joint Optic Disc and Cup Segmentation. In MICCAI, 2019.
[6] Yifan Zhang, Hanbo Chen, Ying Wei, Peilin Zhao, Jiezhang Cao, Xinjuan Fan, Xiaoying Lou, Hailing Liu, Jinlong Hou, Xiao Han, Jianhua Yao, Qingyao Wu, Mingkui Tan*, and Junzhou Huang. From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification. In MICCAI, 2019.
[7] Shihao Zhang, Huazhu Fu, Yuguang Yan, Yubing Zhang, Qingyao Wu, Ming Yang, Mingkui Tan*, and Yanwu Xu. Attention Guided Network for Retinal Image Segmentation. In MCCAI, 2019.
[8] Shihao Zhang, Yuguang Yan, Pengshuai Yin, Zhen Qiu, Wei Zhao, Guiping Cao, Wan Chen, Jin Yuan, Risa Higashita, Qingyao Wu, Mingkui Tan*, and Jiang Liu. Guided M-Net for High-Resolution Biomedical Image Segmentation with Weak Boundaries. In Workshop on OMIA, 2019.
[9] Jingwen Wang, Yuguang Yan, Yanwu Xu, Wei Zhao, Huaqing Min, Mingkui Tan*, and Jiang Liu. Conditional Adversarial Transfer for Glaucoma Diagnosis. In EMBC, 2019.
[10] Yuguang Yan, Mingkui Tan†, Yanwu Xu, Jiezhang Cao, Michael Ng, Huaqing Min, and Qingyao Wu. Oversampling for Imbalanced Data via Optimal Transport. In AAAI, 2019.
[11] Yuguang Yan, Wen Li, Hanrui Wu, Huaqing Min, Mingkui Tan*, and Qingyao Wu. Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation. In IJCAI, 2018.
[12] Jiezhang Cao, Yong Guo, Qingyao Wu, Chunhua Shen, Junzhou Huang, and Mingkui Tan*. Adversarial Learning with Local Coordinate Coding. In ICML, 2018.
[13] Chaorui Deng, Qi Wu, Qingyao Wu, Fuyuan Hu, Fan Lyu, and Mingkui Tan*. Visual Grounding via Accumulated Attention. In CVPR, 2018.
[14] Zhuangwei Zhuang, Mingkui Tan†, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu. Discrimination-aware Channel Pruning for Deep Neural Networks. In NeurIPS, 2018.
[15] Yifan Zhang, Peilin Zhao, Jiezhang Cao, Wenye Ma, Junzhou Huang, Qingyao Wu, and Mingkui Tan*. online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data. In KDD, 2018.
[16] Yong Guo, Qingyao Wu, Chaorui Deng, Jian Chen, and Mingkui Tan*. Double Forward Propagation for Memorized Batch Normalization. In AAAI, 2018.
[17] Chao Han, Qingyao Wu, Michael K Ng, Jiezhang Cao, Mingkui Tan*, and Jian Chen. Tensor based Relations Ranking for Multi-relational Collective Classification. In ICDM, 2017.
[18] Jiezhang Cao, Qingyao Wu, Yuguang Yan, Li Wang, and Mingkui Tan*. On the Flatness of Loss Surface for Twolayered ReLU Networks. In ACML, 2017.
[19] Dong Gong, Mingkui Tan†, Yanning Zhang, Anton van den Hengel, and Qinfeng Shi. Mpgl: An Efficient Matching Pursuit Method for Generalized Lasso. In AAAI, 2017.
[20] Yuguang Yan, Qingyao Wu, Mingkui Tan*, and Huaqing Min. online Heterogeneous Transfer Learning by Weighted Offline and online Classifiers. In ECCV, 2016.
[21] Mingkui Tan, Shijie Xiao, Junbin Gao, Dong Xu, Anton Van Den Hengel, and Qinfeng Shi. Proximal Riemannian Pursuit for Large-scale Trace-norm Minimization. In CVPR, 2016.
[22] Wei Emma Zhang, Mingkui Tan*, Quan Z Sheng, Lina Yao, and Qingfeng Shi. Efficient Orthogonal Non-negative Matrix Factorization over Stiefel Manifold. In CIKM, 2016.
[23] Mingkui Tan, Yan Yan, Li Wang, Anton Van Den Hengel, Ivor W Tsang, and Qinfeng Javen Shi. Learning Sparse Confidence-weighted Classifier on Very High Dimensional Data. In AAAI, 2016.
[24] Yan Yan, Mingkui Tan*, Ivor Tsang, Yi Yang, Chengqi Zhang, and Qng Shi. Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search. In IJCAI, 2015.
[25] Mingkui Tan, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Junbin Gao, Fuyuan Hu, and Zhen Zhang. Learning Graph Structure for Multi-label Image Classification via Clique Generation. In CVPR, 2015.
[26] Mingkui Tan, Ivor W Tsang, Li Wang, Bart Vandereycken, and Sinno Jialin Pan. Riemannian Pursuit for Big Matrix Recovery. In ICML, 2014.
[27] Mingkui Tan, Ivor W Tsang, Li Wang, and Xinming Zhang. Convex Matching Pursuit for Large-scale Sparse Coding and Subset Selection. In AAAI, 2012.
[28] Mingkui Tan, Li Wang, and Ivor W Tsang. Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. In ICML, 2010.