个人简介

Recruitments: o Recruiting Ph.D students and postdoctoral fellows in biomedical imaging and image analysis - This is a joint recruitment with Prof. Quanzheng Li’s group at Harvard Medical organization and Massachusetts General Hospital (MGH). - Requirements: - A degree in mathematics or computer science. - Knowledgeable on optimization and machine learning. - Strong programming skills. - Experience in deep learning is strongly preferred. - Exceptional candidates will have a chance to visit MGH at Harvard University for 1-2 years during your study. Email me for further details. Recent events o Mathematical and Scientific Machine Learning (MSML 2020), July 15-17, 2020, Princeton, USA. Past events o 1st International Workshop on Multiscale Multimodal Medical Imaging (MMMI2019), In conjunction with MICCAI 2019, October 13, 2019, Shenzhen, China. o International Workshop on Recent Advances on Mathematical Imaging and Data Science, July 2-6, 2019, Shanghai Jiaotong University, Shanghai, China. o 第二届全国大数据与人工智能科学大会（CSIAM-BDAI 2019），深度学习在 PDEs 中的应用主题研讨会，2019年7月5-7日，云南昆明。 o 中国工业与应用数学第十六届年会（CSIAM2018），"图像科学中的数学理论、模型与算法：医疗影像分析和重建"专题研讨会，2018年9月13-16日，四川成都。 o 第十届反问题年会，图像科学中的反问题，国家天元数学东北中心，长春，2018年5月28-31日。 o 中国工业与应用数学第十五届年会（CSIAM2017）, 图像科学中的数学理论、模型与算法专题研讨会, 2017年10月12-15日, 山东青岛. o PKU Summer organization on Data Science, July 10-23, 2017 (schedule is now available).Course abstracts. o PKU Workshop on Computation and Big Data Analysis, June 20-21, 2017. o PKU Short Course (unofficial): Deep Learning, Sep.-Dec. 2016. o PKU Workshop On Mathematics in Imaging Science and Data Analysis (MISDA), August 4-5, 2016.(Group photo is now available.) o PKU Mini-Course: Information Geometry, June 27-30, 2016. o PKU Workshop on Data Sciences and Applications (@BICMR), June 10, 2016. o Computational Biomedical Imaging Workshop, Shanghai Jiaotong university, October 17-18, 2015. I received my B.S. from Peking University in 2003, M.Sc from National University of Singapore in 2005 and Ph.D from University of California Los Angeles (UCLA) in 2009. Then I spent 2 years in University of California San Diego (UCSD) as a visiting assistant professor. I was a tenure-track assistant professor at University of Arizona since 2011 and joined Peking University as an associate professor in 2014. My research interest is in mathematical modeling and computations in imaging and data science. A special feature of my research is blending different branches in mathematics which includes: bridging wavelet frame theory, variational techniques and nonlinear PDEs; bridging differential equations and optimal control with deep learning. We are working on projects aiming at establishing connections among these subjects to not only acquire new understandings of the subjects themselves, but also to develop new and effective tools for imaging/data science. Education Experience: o Ph.D. Department of Mathematics, University of California, Los Angeles (2009). o M.S. Department of mathematics, National University of Singapore (2005). o B.S. organization of Mathematical Science, Peking University (2003). Working Experience: o Associate Professor, Beijing International Center for Mathematical Research, Peking University, 12/2014 - present. o Assistant Professor, Department of Mathematics, University of Arizona, 08/2011 - 12/2014. o Faculty Member, Program of Applied Mathematics, University of Arizona, 08/2011 - 12/2014. o Visiting Senior Research Fellow, Department of Mathematics, National University of Singapore, Singapore, 05/2013 - 06/2013. o SEW Assistant Professor, Department of Mathematics, University of California, San Diego, 06/2009 - 07/2011. o Visiting Senior Research Fellow, Department of Mathematics, National University of Singapore, Singapore, 07/2010 - 08/2010. Honors and Awards: o Qiu Shi Outstanding Young Scholar Award, Qiu Shi Foundation, Hongkong, China, 2014. o Silver Medal, New World Mathematics Award for Master Thesis, 4th International Congress of Chinese Mathematicians, 12/2007. Research Projects: o Machine learning methods in medical and biological imaging and image analysis o Deep learning from applied mathematics perspective o Sparse representation on general domains (surfaces, manifolds, graphs, etc) o Approximations in image restoration Book (Chapters) /Review Papers 1. Chenyang Shen, Dan Nguyen, Zhiguo Zhou, Steve B. Jiang, Bin Dong, and Xun Jia, An introduction to deep learning in medical physics: advantages, potential, and challenges, Physics in Medicine and Biology, DOI: 10.1088/1361-6560/ab6f51, 2020. 2. 董彬，图像反问题中的数学与深度学习方法，计算数学，第41卷、第4期，2019年11月 3. Haimiao Zhang and Bin Dong, A Review on Deep Learning in Medical Image Reconstruction,accepted by Journal of the Operations Research Society of China, 2019. (arXiv: 1906.10643) 4. 欧高炎、朱占星、董彬、鄂维南，数据科学导引，高等教育出版社，2017年12月 5. 董彬、沈佐伟、张小群，图象恢复问题中的数学方法，中科院数学所讲座系列（席南华 主编），科学出版社，2017. 6. Bin Dong and Zuowei Shen, Image restoration: a data-driven perspective, Proceedings of the International Congress of Industrial and Applied Mathematics (ICIAM), Beijing, China, High Education Press (Lei Guo and Zhi-Ming Ma eds), 65-108, 2015. 7. Bin Dong and Zuowei Shen, MRA-based wavelet frames and applications, IAS Lecture Notes Series, Hong-Kai, ed. "Mathematics in Image Processing". Vol. 19. American Mathematical Society, 2013. Others 1. “用深度神经网络学习偏微分方程及其数值求解的离散格式”，北京智源人工智能研究院，2020年1月19日。 2. “天生一对，硬核微分方程与深度学习的「联姻」之路”，机器之心，2019年5月17日。 3. Robert Crandall, Bin Dong and Ali Bilgin, Randomized Iterative Hard Thresholding: A Fast Approximate MMSE Estimator for Sparse Approximations, Technical Report, June 2013 (revised, April 2014). 4. Bin Dong, The implicit representation of biological shapes and forms, Biomedical Computation Review (issue: Spring 2009), Published by Simbios, the NIH National Center for Physics-Based Simulation of Biological Structures, 2009. 5. Bin Dong, Applications of Variational Models and Partial Differential Equations in Medical Image and Surface Processing, PhD Thesis, UCLA, 2009. 6. Bin Dong, Pseudo-splines, Wavelets and Framelets, M.S. Thesis, National University of Singapore, 2005. Courses: o 2019-2020年春季：研究型学习（Learning by Research） o 2019-2020年秋季：图像处理中的数学方法（Mathematical Image Processing） o 2018-2019年春季：研究型学习（Learning by Research） o 2018-2019年秋季：图像处理中的数学方法（Mathematical Image Processing） o 2017-2018年春季：研究型学习（Learning by Research） o 2017-2018年秋季：线性代数B（Linear Algebra），生科 o 2016-2017年春季：图像处理中的数学方法（Mathematical Image Processing） o 2016-2017年秋季：线性代数B（Linear Algebra），信科 o 2015-2016年春季：图像处理中的数学方法（Mathematical Image Processing） o 2015-2016年秋季：线性代数B（Linear Algebra），经济学院 - Short Lectures and Tutorials： o Dynamic System and Optimal Control Perspective of Deep Learning, presented at the Workshop on Deep learning and partial differential equations, organized by the American Institute of Mathematics, San Jose, California, October 14-18, 2019. o 浅谈人工智能与数学，北大数学中学生夏令营，2019年8月5-7日。 o Guest Lectures on Dynamics, Control and Deep Learning, PSU-PKU joint course (497): An Introduction to Deep Learning, July 23-24, 2019. o 人工智能4Kids，2019年4月17日，北京大学燕园幼儿园。 o Tutorial ACML 2018, Dynamic System and Optimal Control Perspective of Deep Learning and Beyond, Nov. 14-16, 2018. o 天元数学东北中心短课程《图像处理中的数学方法》，2018年7月19日-7月21日.