"The future of computing is the computer that talks, listens, sees and learns." -- Anonymous
"Wake up everyday with a feeling of passion for the difference technology will make in people's lives." -- Anonymous
"Publish, or perish." -- Anonymous
"Few, but ripe." -- Gauss
Tong Lin, Ph.D. 林 通
Key Laboratory of Machine Perception (Ministry of Education),
School of Artificial Intelligence,Peking University, Beijing 100871, China
Email: lintong [at] pku [dot] edu [dot] cn
Tong Lin received the PhD degree in Applied Mathematics from Peking University in 2001. In 1999 and 2000, he was a visiting student at the Media Computing Group, Microsoft Research Asia. In 2002, he joined the Key Laboratory of Machine Perception at Peking University, China, where he is currently an associate professor. From 2004 to 2005, he was an exchange scholar at Seoul National University, Korea. From 2007 to 2008, he was an exchange scholar at UCSD Moores Cancer Center, CA, USA. His recent research interests are machine learning algorithms, with applications in medical data analysis.
To Prospective Students:
(Only Accepting Master and Undergraduate Students, Can NOT supervising PhD. Candidates!)
Students with good mathematics training (especially of Mathematics majors) and strong programming skills are welcome.
Previous Projects in Medical Applications:
Welcome any student interested in this subfield.
 Tong Lin & Hongbin Zha, "Riemannian Manifold", book chapter in “Computer Vision: A Reference Guide”, Katsushi Ikeuchi (ed.), Springer, Jan. 2020.
 Tong Lin & Yucheng Lin, “Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques”, book chapter of “Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging”, edited by K. Suzuki and Y. Chen, Springer, pp. 337-359, 2018.
 Zhiyi Chen, Tong Lin*, “Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning”, 30th International Conference on Artificial Neural Networks (ICANN), Sept. 14-17, 2021.
 Xuan Tang, Tong Lin*, “Adversarial Variational Knowledge Distillation”, 30th International Conference on Artificial Neural Networks (ICANN), Sept. 14-17, 2021.
 Gen Ye, Tong Lin*, “Channel Capacity of Neural Networks”, 30th International Conference on Artificial Neural Networks (ICANN), Sept. 14-17, 2021.
 Shijie Fang, Tong Lin*, “Intra-Model Collaborative Learning of Neural Networks”, International Joint Conference on Neural Network (IJCNN), July 18-21, 2021.
 Gen Ye, Chen Du, Tong Lin, Yan Yan*, Jack Jiang, “Deep Learning for Laryngopharyngeal Reflux Diagnosis”, Applied Sciences. 2021; 11(11):4753.
 Zhongyi Ji, Xiao Han, Tong Lin* and Wenmin Wang, "A Dense-Gated U-Net for Brain Lesion Segmentation", IEEE Int. Conf. Visual Communications and Image Processing (VCIP), Dec.1-4, 2020.
 Jinhao Dong, Tong Lin*, “MarginGAN: Adversarial Training in Semi-Supervised Learning”, Proc. NeurIPS, Vancouver, Canada, Dec. 8-14, 2019.
 Hua-Mao Gu, Tong Lin, Xun Wang*, Shichao Zhang, “A Preliminary Geometric Structure Simplification for Principal Component Analysis,” Neurocomputing, Volume 336, Pages 46-55, 7 April 2019.
 Zhongxue Chen*, Yan Lu, Tong Lin, Qingzhong Liu,and Kai Wang, “Gene-based genetic association test with adaptive optimal weights,” Genetic Epidemiology, Volume 42, Issue 1, pages 95-103, February 2018.
 Zhongxue Chen*, Tong Lin, and Kai Wang, “A powerful variant-set association test based on chi-square distribution,” Genetics, Nov. 1, 2017; 207(3):903-910
 Tong Lin, Tiebing Liu, Yucheng Lin, Chaoting Zhang, Lailai Yan, Zhongxue Chen, Zhonghu He*, Jingyu Wang*, “Serum Levels of Chemical Elements in Esophageal Squamous Cell Carcinoma in Anyang, China — A Case-control Study Based on Machine Learning Methods,” BMJ Open 2017; 7: e015443. doi: 10.1136/bmjopen-2016-015443
 Tong Lin, Tiebing Liu, Yucheng Lin, Lailai Yan, Zhongxue Chen, Jingyu Wang*, “Comparative Study on Serum Levels of Macro and Trace Elements in Schizophrenia based on Supervised Learning Methods,” Journal of Trace Elements in Medicine and Biology (JTEMB), 43:202-208, 2017.
 Y. Lin, L. Yang, Z Lin*, T. Lin, H. Zha, “Factorization for Projective and Metric Reconstruction via Truncated Nuclear Norm,” 2017 International Joint Conference on Neural Networks (IJCNN 2017), May 14-19, 2017, Anchorage, Alaska, USA.
 T. Lin, Y. Liu, B. Wang, L. Wang, H. Zha, “Nonlinear Dimensionality Reduction by Local Orthogonality Preserving Alignment,” Journal of Computer Science and Technology (JCST), 31(3): 512-524, 2016.
 T. Lin, Y. Liu, B. Wang, L. Wang, H. Zha, “Local Orthogonality Preserving Alignment for Nonlinear Dimensionality Reduction,” The Fourth International Conference on Computational Visual Media (CVM 2016), Cardiff, UK, April 6-8, 2016.
 T. Lin, H. Xue, L. Wang, B. Huang, H. Zha, “Supervised Learning via Euler's Elastica Models,” Journal of Machine Learning Research (JMLR), vol. 16 (Dec): 3637-3686, 2015.
 T. Lin, S. Liu, and H. Zha, "Incoherent Dictionary Learning for Sparse Representation," 21st Int. Conf. Pattern Recognition (ICPR), Tsukuba, Japan, Nov. 11-15, 2012.
 T. Lin, H. Xue, L. Wang, and H. Zha, "Total Variation and Euler's Elastica for Supervised Learning," 29th Int. Conf. Machine Learning (ICML), Edinburgh, Scotland, UK, June 26-July 1, 2012.
 Y. Ji, T. Lin*, and H. Zha, "CDP Mixture Models for Data Clustering," 20th Int. Conf. Pattern Recognition (ICPR), 23-26 August, 2010, Istanbul Turkey.
 X. Tang, T. Lin, and S.B. Jiang, "A Feasibility Study of Treatment Verification Using EPID Cine Images for Hypofractionated Lung Radiotherapy," Physics in Medicine and Biology (PMB), vol. 54, no. 18,pp. S1-S8, 2009.
 T. Lin, R. Li, X. Tang, J.G. Dy, and S.B. Jiang, "Markerless Gating for Lung Cancer Radiotherapy based on Machine Learning Techniques," Physics in Medicine and Biology (PMB), Institute of Physics (IOP), vol. 54, no. 6,pp. 1555-1563, 21 March 2009.
 T. Lin, L.I. Cervino, X. Tang, N. Vasconcelos, and S.B. Jiang, "Fluoroscopic Tumor Tracking for Image-Guided Lung Cancer Radiotherapy," Physics in Medicine and Biology (PMB), Institute of Physics (IOP), vol. 54, no. 4, pp. 981-992, 21 Feb. 2009.
 Y. Ji, T. Lin*, and H. Zha, "Mahalanobis Distance Based Non-negative Sparse Representation for Face Recognition," The Eighth International Conference on Machine Learning and Applications (ICMLA), Oral Presentation, Miami, Florida, USA, Dec. 13-15, 2009.
 Y. Geng, T. Lin, Z. Lin, and P. Hao, "Refined Exponential Filter with Applications to Image Restoration and Interpolation," The Ninth Asian Conference on Computer Vision (ACCV), Xi'an, China, Sept. 23-27, 2009.
 T. Lin and H. Zha, "Riemannian Manifold Learning," IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), vol. 30, no. 5, pp.796-809, May 2008.
 T. Lin, L. Cervino, X. Tang, N. Vasconcelos, and S.B. Jiang, "Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques," Seventh International Conference on Machine Learning and Applications (ICMLA), pp. 533 - 538, San Diego, USA, 11-13 Dec. 2008.
 X. Tang, T. Lin, and S.B. Jiang, "Towards On-line Treatment Verification Using cine EPID for Hypofractionated Lung Radiotherapy," Seventh International Conference on Machine Learning and Applications (ICMLA), pp.551 - 555, San Diego, USA, 11-13 Dec. 2008.
 T. Lin, P. Hao, and S. Xu, "Matrix Factorizations for Reversible Integer Implementation of Orthonormal M-Band Wavelet Transforms," Signal Processing (SP), Elsevier, vol. 86, no. 8, pp. 2085-2093, Aug. 2006.
 T. Lin, S. Xu, Q. Shi, and P. Hao, "An Algebraic Construction of Orthonormal M-Band Wavelets with Perfect Reconstruction", Applied Mathematics and Computation (AMC), Elsevier, vol. 172, no. 2, pp. 717-730, Jan. 2006.
 T. Lin, H. Zha, and S. Lee, “Riemannian Manifold Learning for Nonlinear Dimensionality Reduction,” 9th European Conference on Computer Vision (ECCV), Oral Presentation, LNCS 3951, vol 1, pp. 44-55, Graz, Austria, May 7-13, 2006.
 T. Lin and P. Hao, “Compound Image Compression for Real-Time Computer Screen Image Transmission”, IEEE Trans. Image Processing (TIP), vol. 14, no. 8, pp.993-1005, 13 pages, Aug. 2005.  T. Lin, P. Hao, and S. Lee, “Efficient Coding of Computer Generated Compound Images,” IEEE Int. Conf. Image Processing (ICIP), vol 1, pp. 561-564, Genoa, Italy, Sept. 11-14, 2005.
 T. Lin, P. Hao, and S. Xu, "Factoring M-Band Wavelet Transforms into Integer Mapping Steps and Lifting Steps," IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, USA, March 19-23,2005.
 T. Lin, P. Hao, C. Xu, and J. Feng, “Hybrid Image Coding for Real-TimeComputer Screen Video Transmission”, Visual Communications and ImageProcessing (VCIP), part of the IS&T/SPIE Symposium on Electronic Imaging, San Jose, CA, USA, Jan. 18-22, 2004.
 T. Lin, Q. Shi, and P. Hao, "An Algebraic Approach to M-Band WaveletsConstruction," IASTED Int. Conf. Signal and Image Processing (SIP), Hawaii, USA, Aug. 13-15, 2003.
 T. Lin, H. Zhang, J. Feng, and Q. Shi, "Shot Content Analysis for VideoRetrieval Applications," Journal of Software (in Chinese), vol. 13, no.8, pp. 1577-1585, Aug. 2002.
 T. Lin, H. Zhang, and Q. Shi, “Video Content Representation for ShotRetrieval and Scene Extraction,” Int. Journal of Image and Graphics (IJIG), vol. 1, no. 3, pp. 507-526, Aug. 2001.
 T. Lin, C. Ngo, H. Zhang, and Q. Shi, “Integrating Color and Spatial Featuresfor Content-Based Video Retrieval,” IEEE Int. Conf. Image Processing (ICIP), Invited paper, Thessaloniki, Greece, Oct. 7-10, 2001.
 T. Lin, H. Zhang, and Q. Shi, “Video Scene Extraction by Force Competition”, IEEE Int. Conf. Multimedia and Expo (ICME), Tokyo, Japan, Aug. 22-25, 2001.
 T. Lin, and Q. Shi, "Image Segmentation by an Edge Growing Method,"Journal of Image and Graphics (in Chinese), no. 11, pp. 911-915, Nov. 2000.
 T. Lin and H. Zhang, “Automatic Video Scene Extraction by Shot Grouping”, Int. Conf. Pattern Recognition (ICPR), Oral presentation, Barcelona, Spain,Sept. 3-8, 2000.
 H. Jiang, T. Lin and H. Zhang, “Video Segmentation with the Support of AudioSegmentation and Classification”, IEEE Int. Conf. Multimedia and Expo (ICME), Oral presentation, New York, USA, July 31-Aug. 2, 2000.