黄晓霖

职称:教授

邮箱:xiaolinhuang@sjtu.edu.cn

地址:电信群楼 2-429

个人主页:http://www.pami.sjtu.edu.cn/xiaolin

实验室主页:http://www.pami.sjtu.edu.cn

教育经历

- 2006-2012 清华大学 工学博士
- 2002-2006 西安交通大学 工学学士/理学学士

工作经历

上海交通大学 博士生导师 国家“青年千人项目”入选者
  • 2024-         上海交通大学 教授
  • 2016-2023 上海交通大学 副教授(2023年获长聘教职)
  • 2015-2017 埃尔兰根-纽伦堡大学(Friedrich-Alexander Universität Erlangen-Nürnberg) 洪堡学者
  • 2015-2016 鲁汶大学(KU Leuven) 自由研究员(兼)
  • 2012-2015 鲁汶大学(KU Leuven) 博士后研究员

科研方向

  • 分片线性系统的建模、辨识及优化
  • 机器学习方法及其优化
  •    -  核学习方法
  •    -  深度学习动态超低维空间
  •    -  对抗训练与攻击
  • 机器学习方法的应用

学术服务

  • Senior Member, IEEE
  • Action Editor, Machine Learning
  • Area Chair, ICCV 2023
  • Senior PC member, AAAI 2024

代表作

  • [著] S. Boyd, L. Vandenberghe, [译] 王书宁, 许鋆, 黄晓霖: 《凸优化》, 清华大学出版社, 2013. 
  • M. He, F. He, L. Shi, X. Huang*, J.A.K. Suykens: Learning with Asymmetric Kernels: Least Squares and Feature Interpretation,
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8): 10044-10054, 2023
  • T. Li, L. Tan, Z. Huang, Q. Tao, Y. Liu, X. Huang*: Low Dimensional Trajectory Hypothesis is True: DNNs can be Trained in Tiny Subspaces,
  • IEEE Transactions on Pattern Analysis and Machine Intelligence45(3): 3411-3412, 2023
  • Q. Tao*, L. Li*, X. Huang*, X. Xi, S. Wang, J.A.K. Suykens: Piecewise Linear Neural Networks and Deep Learning,
    Nature Reviews Methods Primers, 2:42, 2022
  • F. Liu*, X. Huang*, Y. Chen, J.A.K. Suykens: Towards a Unified Quadrature Framework for Large-Scale Kernel Machines,
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11): 7975-7988, 2022
  • F. Liu*, X. Huang*, Y. Chen, J.A.K. Suykens: Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond,
    IEEE Transactions on Pattern Analysis and Machine Intelligence44(10): 7128-7148, 2022
  • W. Liu, P. Zhang, Y. Lei, X. Huang*, J. Yang*, M. Ng: A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing,
    IEEE Transactions on Pattern Analysis and Machine Intelligence44(10): 6631-6648, 2022
  • S. Chen, Z. He, C. Sun, J. Yang, X. Huang*: Universal Adversarial Attack on Attention and the Resulting Dataset DamageNet,
    IEEE Transactions on Pattern Analysis and Machine Intellige
    nce, 44(4): 2188-2197, 2022
  • S. Tang#, X. Huang#*, M. Chen, C. Sun, J. Yang*: Adversarial Attack Type I: Cheat Classifiers by Significant Changes, 
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3): 1100-1109, 2021
  • F. Liu*, L. Shi, X. Huang, J. Yang*, J.A.K. Suykens: Generalization Properties of Hyper-RKHS and its Applications,
    Journal of Machine Learning Research, 22(1): 1-38, 2021
  • F. Liu, X. Huang*, C. Gong, J. Yang*, L. Li: Learning Data-adaptive Non-parametric Kernels,
    Journal of Machine Learning Research, 208: 1-39, 2020
  • W. Liu, P. Zhang, X. Huang*, J. Yang*, C. Shen, I. Reid: Real-time Image Smoothing via Iterative Least Squares,
    ACM Transactions on Graphics, 39(3): 1-10, 2020
  • L. Shi, X. Huang*, Y. Feng, J.A.K. Suykens: Sparse Kernel Regression with Coefficient-based lq regularization,
    Journal of Machine Learning Research, 161:1-64, 2019
  • X. Huang*, A. Maier, J. Hornegger, J.A.K. Suykens: Indefinite Kernels in Least Squares Support Vector Machine and Principal Component Analysis, 
    Applied and Computational Harmonic Analysis, 43(1): 162-172, 2017.
  • Y. Feng*, X. Huang, L. Shi, Y. Yang, J.A.K. Suykens: Learning with the Maximum Correntropy Criterion Induced Losses for Regression, 
    Journal of Machine Learning Research, 16: 993-1034, 2015.
  • X. Huang*, L. Shi, J.A.K. Suykens: Ramp Loss Linear Programming Support Vector Machine,
    Journal of Machine Learning Research, 15: 2185-2211, 2014.
  • X. Huang*, L. Shi, J.A.K. Suykens: Support Vector Machine Classifier with Pinball Loss, 
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5): 984-997, 2014.

其它论文

  • M. He, F. He, R. Yang, X. Huang*, Diffusion representation for asymmetric kernels via magnetic transform, Neural Information Processing Systems, 2023
  • J. Zhong, X. Huang, X. Yu*, Multi-frame self-supervised depth estimation with multi-scale feature fusion in dynamic senses, ACM International Conference on Multimedia, 2023 
  • Y. Chen, C. Shang, X. Huang, X. Yin*, Data-driven safe controller synthesis for deterministic systems: A posteriori method with validation tests, IEEE Conference on Decision and Control, 2023
  • T. Chu, Z. Yang,  X. Huang*, Improving the post-training neural networks quantization by prepositive feature quantization, IEEE Transactions on Circuits and Systems for Video Technology, 2023
  • Y. Wu, S. Chen, K. Fang, X. Huang*, Unifying gradients to improve real-world robustness for deep networks, ACM Transactions on Intelligent Systems and Technology, 2023
  • L. Tan, S. Wu, W. Zhou, X. Huang*, Weighted neural tangent kernel: A generalized and improved network-induced kernel, Machine Learning, 2023. 
  • S. Wu, S. Chen, C. Xie, X. Huang*, One-pixel shortcut: On the learning preference of deep neural networks, in International Conference on Learning Representations2023.
  • T. Li, Z. Huang, Q. Tao, Y. Wu, X. Huang*, Trainable weight averaging: Efficient training by optimizing historical solutions,  in International Conference on Learning Representations2023.
  • S. Chen, G. Yuan, X. Cheng, Y. Gong, M. Qin, Y. Wang, X. Huang*, Self-ensemble protection: Training checkpoints are good data protectors, in International Conference on Learning Representations, 2023.
  • S. Chen, Z. Huang, Q. Tao,  X. Huang*, Query attack by multi-identity surrogates,  IEEE Transactions on Artificial Intelligence, 2023.
  • F. He, M. He, L. Shi*, X. Huang*, Global search and analysis for the non-convex two-level l1 penalty, IEEE Transactions on Neural Networks and Learning Systems, 2022.
  • T. Yan, X. Huang, Q. Zhao*, Hierarchical superpixel segmentation by parallel CRTrees labeling, IEEE Transactions on Image Processing, 2022
  • Y. Gu*, Y. Xu, X. Huang, J. Yang, W. Xue, G.-Z. Yang, Towards robust histology-prior embedding for endomicroscopy image classification, IEEE Transactions on Medical Imaging,  2022
  • S. Chen, Z. Huang, Q. Tao, Y. Wu, C. Xie, X. Huang*: Adversarial attack on attackers: Post-process to mitigate black-box score-based query attacks, in Neural Information Processing Systems, 2022
  • T. Li, Y. Wu, S. Chen, K. Fang, X. Huang*: Subspace Adversarial Training, in Computer Vision and Pattern Recognition2022.
  • F. Liu#, L. Shi#, X. Huang, J. Yang, J.A.K. Suykens: Analysis of regularized least-squares in reproducing kernel Kreĭn spaces, Machine Learning, 2021
  • Y. Qin, H. Zheng, Y. Gu, X. Huang, J. Yang*, L. Wang, F. Yao, Y.-M. Zhu, G.-Z. Yang: Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT, IEEE Transactions on Medical Imaging, 2021
  • S. Chen*, X. Zhong, S. Dorn, N. Ravikumar, Q. Tao, X. Huang, M. Lell, M. Kachelriess, A. Maier: Improving generalization capability of multi-organ segmentation models using dual-energy CT, IEEE Transactions on Radiation and Plasma Medical Sciences, 2021
  • F.  Liu, X. Huang*, L. Shi, J. Yang*, J.A.K. Suykens: A double-variational Bayesian framework in random Fourier features for indefinite kernels, IEEE Transactions on Neural Networks and Learning Systems,  2020
  • W. Xiao*, X. Huang*, F. He, J. Silva, S. Emrani, A.Chaudhuri: Online robust principal component analysis with change point detection, IEEE Transactions on Multimedia, 2020
  • C. Ma, C. Gong, X. Li, X. Huang, W. Liu, J. Yang*: Toward making unsupervised graph Hashing discriminative, IEEE Transactions on Multimedia, 22(3): 760-774, 2020.
  • Y. Qin, J. Wan, H. Zheng, X. Huang, J. Yang*, L. Wu, N. Song, Y. Zhu, G.-Z. Yang: Varifocal-Net: A chromosome classification approach using deep convolutional networks, IEEE Transactions on Medical Imaging, 2019.
  • F. Liu, X. Huang*, J.Yang*, C. Gong, J.A.K. Suykens: Indefinite kernel logistic regression with concave-inexact-convex procedure, IEEE Transactions on Neural Networks and Learning Systems,  2019
  • J. Cai, X. Huang*: Modified sparse linear-discriminant analysis via nonconvex penalties,  IEEE Transactions on Neural Networks and Learning Systems, 2018
  • X. Huang*, J.A.K. Suykens, S. Wang, J. Hornegger, A. Maier: Classification with truncated l1 distance kernel,  IEEE Transactions on Neural Networks and Learning Systems,  2018.
  • Y. Huang*, O. Taubmann, X. Huang, V. Haase, G. Lauritsch, A. Maier, Scale-space anisotropic total variation for limited angle tomography, IEEE Transactions on Radiation and Plasma Medical Sciences, 2018.
  • Y. Lu*, M. Kowarschik, X. Huang, S. Chen, Q. Ren, R. Fahrig, J. Hornegger, A. Maier: Material decomposition using ensemble learning for spectral X-ray imaging, IEEE Transactions on Radiation and Plasma Medical Sciences,2018.
  • F. Liu, C. Gong, X. Huang, T. Zhou, J. Yang*, D. Tao, Robust visual tracking revisited: From correlation filter to template matching, IEEE Transactions on Image Processing,  2018. 
  • X. Huang*, L. Shi, J.A.K. Suykens: Solution path for pin-SVM classifiers with positive and negative tau value, IEEE Transactions on Neural Networks and Learning Systems,  2017.
  • Y. Liu*, S. Wu, X. Huang, B. Chen, C. Zhu, Hybrid CS-DMRI: Periodic time-variant subsampling and omnidirectional total variation based reconstruction, IEEE Transactions on Medical Imaging,  2017.
  • J. Wang*, R. Schaffert, A. Borsdorf, B. Heigl, X. Huang, J. Hornegger, A. Maier: Dynamic 2-D/3-D rigid registration framework using point-to-plane correspondence model, IEEE Transactions on Medical Imaging2017.
  • T. Köhler*, X. Huang, F. Schebesch, A. Aichert, A. Maier, and J. Hornegger: Robust Multi-Frame Super-Resolution Employing Iteratively Re-weighted Minimization, IEEE Transactions on Computational Imaging, 2(1): 42-58, 2016.
  • Y. Feng*, Y. Yang, X. Huang, S. Mehrkanoon, J.A.K. Suykens: Robust Support Vector Machines for Classification with Non-convex and Smooth Losses, Neural Computation, 28, 1217-1247, 2016.
  • Y. Yang*, Y. Feng, X. Huang, J.A.K. Suykens: Rank-1 Tensor Properties with Applications to a Class of Tensor Optimization Problems, SIAM Journal on Optimization, 26(1): 171-196, 2016.
  • C. Shang, F. Yang, X. Gao, X. Huang, J.A.K. Suykens, D. Huang*: Concurrent Monitoring of Operating Condition Deviations and Process Dynamics Anomalies with Slow Feature Analysis, AIChE Journal, 61(11): 3666-3682, 2015.
  • X. Huang*, L. Shi, J.A.K. Suykens: Asymmetric Least Squares Support Vector Machine, Computational Statistics and Data Analysis, 70: 395-405, 2014.
  • L. Shi*, X. Huang, J.A.K. Suykens: Quantile Regression with l1-regularization and Gaussian Kernels, Advances in Computational Mathematics, 40(2): 517-551, 2014.
  • X. Huang*, M. Matijas, J.A.K. Suykens: Hinging Hyperplanes for Time-Series Segmentation, IEEE Transactions on Neural Networks and Learning Systems, 24(8): 1279-1291, 2013.
  • F. Chen*, X. Huang, J. Zhou: Hierarchical Minutiae Matching for Fingerprint and Palmprint Identification, IEEE Transactions on Image Processing, 22(12): 4964-4971, 2013.
  • X. Huang, J. Xu, S. Wang*: Exact Penalty and Optimality Condition for Nonseparable Continuous Piecewise Linear Programming, Journal of Optimization Theory and Applications, 155: 145-164, 2012.
  • X. Huang, J. Xu, X. Mu, S. Wang*: The Hill Detouring Method for Minimizing Hinging Hyperplanes Functions, Computers and Operations Research, 39(7): 1763-1770, 2012.
  • S. Wang*, X. Huang, Y. Yeung: A Neural Network of Smooth Hinge Functions, IEEE Transactions on Neural Networks, 21(9): 1381-1395, 2010.
  • J. Xu, X. Huang, S. Wang*: Adaptive Hinging Hyperplanes and its Applications in Dynamic System Identification, Automatica, 45(10):2325-2332, 2009.
  • S. Wang*, X. Huang, K.K. Junaid: Configuration of Continuous Piecewise Linear Neural Networks, IEEE Transactions on Neural Networks, 19(8): 1431-1445, 2008.



讲授课程

  • AU311   本科生课程《模式识别导论》(中英双文授课)
  • AU4303 本科生课程《线性规划与非线性规划》
  • VE485   本科生课程(密歇根学院)《Convex Optimization in Machine Learning》 (英文授课)
  • AU7021 研究生课程《学习与控制中的优化》


   



小组成员(包含巴黎高科学院、宁波人工智能研究院学生)

2016级:杨海岩(硕士研究生,2019 年毕业,入职百度)

2017级:楚天舒(博士研究生)   谢佳轩(硕士研究生,2020年毕业,UC Irvine 攻读博士)   何    凡(硕士研究生,2019年毕业,本校攻读博士)

2018级:王凯捷(博士研究生)   孙程锦(硕士研究生,2021年毕业,入职美团)   徐金田(硕士研究生,2021年毕业,入职拼多多) 

2019级:吴颖雯(博士研究生)   何    凡(博士研究生,2023年毕业,KU Leuven 博士后)   罗    钦(硕士研究生,2022年毕业,香港中文大学攻读博士)  姚乐宇(硕士研究生,2022年毕业,入职英特尔) 王鹏博(硕士研究生,2022年毕业,清华大学攻读博士)  谭   雷(硕士研究生,2022年毕业,入职字节跳动)

2020级:何铭震(博士研究生)   陈思哲(硕士研究生,2023年毕业,UC Berkeley 攻读博士)    李    涛(硕士研究生,本校攻读博士)  吴枢同(硕士研究生,2023年毕业,UW-Madison 攻读博士) 李明哲(硕士研究生,2023年毕业,入职字节跳动)   傅雨佳(硕士研究生,2023年毕业,入职字节跳动) 张一航(硕士研究生,2023年毕业,入职国家机关)

2021级:杨睿凯(博士研究生)丁瑞琪(博士研究生) 何正保(硕士研究生,本校攻读博士)  叶之星(硕士研究生) 雷泽浩(硕士研究生)王天瑶(硕士研究生)   

2022级:黄哲昊(博士研究生)程欣雯(博士研究生) 李  涛(博士研究生)

2023级:何正保(博士研究生)周文杏(硕士研究生)田翰凌(硕士研究生)


博士后研究员:期待有志在机器学习理论、优化算法、及其工业应用方面开展研究的优秀博士加入课题组,欢迎来信咨询。

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