师资队伍
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黄晓霖
职       称: 副教授
研究方向: 机器学习、优化算法、及其应用
办公地址: 电信群楼 2-517
电子邮箱: xiaolinhuang@sjtu.edu.cn
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
  • 2016 至今  上海交通大学 副教授 
  • 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
  • 机器学习方法及其优化 
  • 分片线性系统的建模、辨识及优化
  • 机器学习方法的医学应用(CT 图像重建等)
科研项目
主持项目
  • 2017 第十三批“千人计划”青年项目 
  • 2017-2019  自然科学基金(青年项目):稳健一比特压缩感知及其应用
  • 2015-2017  Alexander von Humboldt Foundation: Piecewise linear technology in machine learning and applications on medical engineering

 

参与项目

  • 2017-2019 自然科学基金(青年项目):基于计算机图像模式识别技术的中重度非增殖性糖尿病视网膜病变定量分析研究
  • 2012-2016 ERC Advanced Grant: Advanced data-driven black-box modeling
  • 2010-2012 自然科学基金:若干连续分片线性问题的深入研究
  • 2006-2010 自然科学基金:连续分片线性系统的逼近方法研究        
科研成果

部分论文

 

  • [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.
     
  • 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, doi:10.1109/TNNLS.2017.2668610.
  • J. Cai, X. HuangModified Sparse Linear-Discriminant Analysis via Nonconvex Penalties,  IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2017.2785324.
  • 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,doi:10.1109/TRPMS.2018.2805328.
 
  • 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 Analysis43(1): 162-172, 2017.
  • 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,  28(7):1584-1593, 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 Imaging36 (10): 2148-2159, 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, 36(9): 1939-1954, 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.
  • 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.
  • 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: 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.
  • 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.

教授课程

 AU311 本科生课程《模式识别导论》(英文授课)

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