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陈万师博士 Dr. Antoni Bert CHAN

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陈万师: 男,助理教授,现任香港城市大学多媒体软件工程研究中心研究员。2000年及2001年,于美国康奈尔大学 分别获电子工程本科及硕士学位。2008年,于美国加州大学圣地亚哥分校获电子与计算机工程博士学位。2001年至 2003年,为美国康奈尔大学视觉与图像分析实验室访问学者。2009年,为美国加州大学圣地亚哥分校统计视觉计算 实验室博士后研究员,后加入香港城市大学电脑科学系,担任助理教授。2006年至2008年,获NSF IGERT奖学金。 2012年,获香港研究资助局(RGC)Early Career Award。


• 在相关领域国际顶级期刊及会议上发表论文40余篇。
• 担任多个计算机视觉与机器学习顶级会议及期刊的审稿人
• 获得Google研究奖

陈万师博士的研究兴趣包括计算机视觉、机器学习、模式 识别及音乐分析,已在Journal of Machine Learning Research (JMLR)、IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI)等顶级期刊,以及ACM SIGGRAPH、ACM SIGGRAPH Asia、IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)、Neural Information Processing Systems (NIPS)等顶级会议上发表了 40多篇论文。2011年,与美国加州大学圣地亚哥分校Gert Lanckriet教授(Co-PI)一起获得Google研究奖(52.5万港币) 。近四年来,陈万师博士获香港研究资助局GRF项目3项(共220万港币)。

陈万师博士担任多个计算机视觉与机器学习 顶级会议(如CVPR、ECCV、ICCV、ICML)的Program Committee member,以及IEEE Trans. Pattern Analysis and Machine Intelligence、IEEE Trans. on Image Processing、IEEE Trans. Circuits and Systems for Video Technology、IEEE Trans. Multimedia、 IEEE Trans. Neural Networks等期刊的审稿人。

成就及荣誉(Achievements & Professional Services)


• System, method and apparatus for small pulmonary nodule computer aided diagnosis from computed tomography scans
A.P. Reeves, D.F. Yankelevitz, C.I. Henshke, and A.B. Chan, US Patents 7,499,578 B2 (2009) and 7,751,607 B2 (2010), Jan 2003.

所获奖项(Awards and Honours)

• NSF IGERT Fellowship: Vision and Learning in Humans and Machines, UCSD, 2006-07.
• Outstanding Teaching Assistant Award, ECE Department, UCSD, 2005-06.
• Office of the President Award, UCSD, 2003.
• Henry G. White Scholorship, Cornell University, 2001.
• Knauss M. Engineering Scholorship, Cornell University, 2001.
• GTE Fellowship, Cornell University, 2001.

代表性论著(Representative Publications)

  • ·W. Liu, A. B. Chan, Rynson W. H. Lau, D. Manocha, “Leveraging long-term predictions and online-learning in agent-based multiple person tracking”, CoRR abs/1402.2016, 2014.
  • ·A. Mumtaz, E. Coviello, Gert R. G. Lanckriet, A. B. Chan, “Clustering dynamic textures with the hierarchical EM algorithm for modeling video”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(7): 1606-1621.
  • ·Z. Ma, A. B. Chan, “Crossing the line: Crowd counting by integer programming with local features”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013: 2539-2546.
  • ·E. Coviello, A. Mumtaz, A. B. Chan, Gert R. G. Lanckriet, “That was fast! Speeding up NN search of high dimensional distributions”, International Conference on Machine Learning (ICML), 2013: 468-476.
  • ·L. Shang, A. B. Chan, “On approximate inference for generalized Gaussian process models”, CoRR abs/1311.6371, 2013.
  • ·A. B. Chan, N. Vasconcelos, “Counting people with low-level features and Bayesian regression”, IEEE Transactions on Image Processing, 2012, 21(4): 2160-2177.
  • ·Y. Cao, A. B. Chan, Rynson W. H. Lau, “Automatic stylistic manga layout”, ACM Trans. Graph, 2012, 31(6): 141.
  • ·Y. Chen, A. B. Chan, G. Wang, “Adaptive figure-ground classification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012: 654-661.
  • ·E. Coviello, A. Mumtaz, A. B. Chan, Gert R. G. Lanckriet, “Growing a bag of systems tree for fast and accurate classification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012: 1979-1986.
  • ·E. Coviello, A. B. Chan, Gert R. G. Lanckriet, “The variational hierarchical EM algorithm for clustering hidden Markov models”, Advances in Neural Information Processing Systems (NIPS), 2012: 413-421.
  • ·A. B. Chan, V. Mahadevan, N. Vasconcelos, “Generalized stauffer-grimson background subtraction for dynamic scenes”, Machine Vision and Applications, 2011, 22(5): 751-766.
  • ·E. Coviello, A. B. Chan, G. R. G. Lanckriet, “Time series models for semantic music annotation”, IEEE Trans. on Audio, Speech and Language Processing, 2011, 19(5): 1343-1359.
  • ·A. B. Chan, D. Dong, “Generalized Gaussian process models”, IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011.
  • ·Tom L. H. Li, A. B. Chan, “Genre classification and the invariance of MFCC features to key and tempo”, International Conference on MultiMedia Modeling (MMM), 2011.