经济学院学术报告之陈焱来

报告时间：2019年6月25日（周二）下午16:15 – 17:00

报告地点：翡翠湖校区科教B楼1008会议室

报 告 人：陈焱来教授

工作单位：University of Massachusetts Dartmouth

主办单位：经济学院

摘要：

Models of reduced computational complexity and guaranteed accuracy is indispensable in scenarios where a large number of numerical solutions to a sequence of problems are desired in a fast/real-time fashion. Reduced basis method (RBM) is such a paradigm in computational mathematics that can improve efficiency by several orders of magnitudes leveraging a machine learning philosophy, an offline-online procedure, and the recognition that the solution space of the concerned sequence of problems can be well approximated by a smaller space in a tailored fashion. A critical ingredient to guarantee the accuracy of the surrogate solution and guide the construction of the surrogate space is a mathematically rigorous theory.

After a brief introduction of RBM, this talk will present some of our recent applications including to fast face recognition, and a new fast iterative linear solver. Applications in economics and finance will be discussed as well.

报告人简介:陈焱来，麻省大学达特茅斯分校（University of Massachusetts Dartmouth)教授，2002年本科毕业于中国科学技术大学获学士学位，2007年获得美国明尼苏达大学应用数学博士学位。陈焱来教授的研究领域包括大规模高性能科学计算的算法设计和分析，已在美国及欧洲著名期刊发表论文近30余篇；先后主持两项美国国家科学基金会关于快速算法的专项基金，并在此方法上做出的世界独创性的研究正在被用于数据压缩、机器学习、深度学习、随机方程等多个新兴领域。陈教授曾担任Journal of Scientific Computing特任编辑。