简介:苏循华,中国科学技术大学学士,挪威商学院硕士,挪威经济学院金融博士,挪威科学技术大学博士后。现任挪威经济学院金融系教授,苏教授具体研究方向为fintech和公司金融,包括企业融资、创新、流动性管理、金融契约与企业竞争等。企业在当今社会扮演着极其重要的角色,针对企业竞争、融资、创新等方面的学术研究具有十分重要的理论与现实意义,其研究结果发表在顶级金融学杂志journal of financial and quantitative analysis (jfqa),journal of banking and finance, journal of corporate finance, journal of money credit and banking (jmcb),financial management等。
报告题目:block chain
教授观点: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.