譚明奎博士，現擔任華南理工大學軟體學院教授、博士生導師，以及廣州市機器人軟體及複雜信息處理重點實驗室主任、華南理工大學計算中心主任。
基本介紹
- 中文名：譚明奎
- 國籍：中國
- 畢業院校：湖南大學
- 職業：教師
人物經歷,學歷,教學經歷,工作經歷,社會兼職,研究方向,獲獎情況,科研項目,發表文章,
人物經歷
譚明奎目前為華南理工大學軟體學院“人工智慧與機器學習”團隊負責人，是廣東省“珠江人才團隊”的核心成員；現主持國家自然科學基金項目、廣東省新一代人工智慧重點研發項目等多個項目；主要從事機器學習和深度學習理論和算法研究；以第一作者或者通訊作者完成的相關成果發表於人工智慧頂級國際會議如 NeurIPS、ICML、CVPR、KDD、ICCV、AAAI和人工智慧權威期刊如JMLR、TNNLS、TIP等。
曾獲得世界華人數學家聯盟最佳論文獎（ICCM Best Paper Award）、第六屆MICCAI workshop最佳論文獎、華南理工大學建校65周年校長基金“最具科研潛質”獎、2019年“TVP騰訊雲最具價值專家”獎等。
學歷
2002.09-2006.06 湖南大學 環境科學與工程學院 環境工程（學士）
2006.09-2009.06 湖南大學 電氣與信息工程學院 控制科學與工程（碩士）
2010.01-2014.10 新加坡南洋理工大學 計算機學院 計算機科學（博士）
教學經歷
主講軟體學院三門專業課程：1、本科生《機器學習（全英）》課程；2、本科生《人工智慧前沿與軟體工程》課程；3、研究生《深度學習（全英）》課程。具體課程信息如下：
(1)機器學習（全英課程）
使用教材： Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David學時：48課時（32 教學 + 16 實驗）
(2)深度學習（全英課程）
使用教材：Deep Learning Tutorial, LISA LAB, University of Montreal 學時：32課時（24教學 + 4實驗 + 4專題報告）
(3)人工智慧前沿與軟體工程
學時：16課時（16教學）
工作經歷
2009.07-2009.12 新加坡南洋理工大學 研究助理
2013.09-2014.05 新加坡南洋理工大學 副研究員
2014.06-2016.06 澳大利亞阿德萊德大學高級副研究員
2016.09-至今 華南理工大學軟體學院 教授
社會兼職
擔任國際期刊審稿人：
Journal of Machine Learning Research (JMLR), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Networks and Learning Systems (TNNLS)，IEEE Transactions on Signal Processing (TSP), IEEE Transactions on Image Processing (TIP), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Vehicular Technology, IEEE Transactions on Big Data, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Signal Processing Letters, IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, Elsevier Int. J. of Electronics and Communications, Multimedia Tools and Applications, Future Generation Computer Systems, Tsinghua Science and Technology, Imaging Science Journal, Information Sciences, Sensing and Imaging.
擔任國際會議審稿人：
CVPR, NeurIPS, ICML, ICLR, ICCV, ECCV, AAAI, IJCAI, ICLR, ACMMM, MICCAI, ACML, AISTATS
研究方向
主要從事機器學習、機器視覺和深度學習的基礎理論和套用研究，具體包括：
1、超高維數據分析：特徵選擇、大規模矩陣恢復、大規模最佳化
2、深度學習及套用：網路模型壓縮、網路結構自動最佳化、可解釋性和泛化性能分析
3、複雜結構數據分析：Low-level圖像處理、醫療圖像分析、視頻內容理解、3D數據分析
獲獎情況
(1)論文“Towards Ultrahigh Dimensional Feature Selection for Big Data” 榮獲ICCM (世界華人數學家聯盟) 2019最佳論文獎
(2)論文“Guided M-Net for High-resolution Biomedical Image Segmentation with Weak Boundaries”榮獲2019年MICCAI Workshop on Ophthalmic Medical Image Analysis最佳論文獎
(3)華南理工大學建校65周年校長基金“最具科研潛質”獎
(4)2019年“TVP騰訊雲最具價值專家”獎
科研項目
(1)基於大規模張量解的超高維數據結構化表示與分析方法研究，國家自然科學基金項目，2017-2019，在研，主持
(2)高效可解釋性神經網路模型及理論研究，廣東省重點研發計畫項目，2019-2022，在研，主持
發表文章
近年來主要論文如下：
* 表示通訊作者
† 表示共同一作
期刊論文
[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.
會議論文
[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.
