• 2016 至今  上海交通大学 副教授，国家特聘专家（青年项目），博士生导师
• 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

• 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

• [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, accepted
• 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 Intelligence, 45(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 Intelligence, 44(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 Intelligence, 44(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 Intelligence, 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.

• M. He, F. He, R. Kai, 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 Representations, 2023.
• T. Li, Z. Huang, Q. Tao, Y. Wu, X. Huang*, Trainable weight averaging: Efficient training by optimizing historical solutions,  in International Conference on Learning Representations, 2023.
• 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. Zhao, 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 Recognition, 2022.
• 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 Imaging, 2017.
• 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.

• 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》

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，入职微软） 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）

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。