学术活动发布与订制系统

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level---in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as "runtime" in core statistical theory and the lack of a role for statistical concepts such as "risk" in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and methods for trading off the speed and accuracy of inference.

His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.

Michael I. Jordan教授是美国加州大学伯克利分校Pehong Chen特聘教授，担任大数据实验室（AMPLab）共同主任、统计人工智能实验室（SAIL）主任、统计系系主任。Jordan教授长期引领着机器学习、统计学的理论、方法与系统研究，是贝叶斯网络、概率图模型、层次随机过程等多个重要方向的主要奠基者之一，也是统计学与机器学习交叉融合的主要推动者之一。Jordan教授是美国国家科学院院士、美国国家工程院院士以及美国艺术与科学院院士，是美国科学促进协会（AAAS）、美国工业与应用数学学会（SIAM）、美国人工智能学会（AAAI）、国际计算机学会（ACM）、国际电气与电子工程师协会（IEEE）等主要国际学术组织的会士。同时，他还担任美国国家科学研究委员会大数据分析分委会主席、美国计算机学会图灵奖委员会委员等学术职务。Jordan教授因卓越的贡献获得人工智能学界重要奖IJCAI Research Excellence Award（2016）以及认知科学界最高奖David E. Rumelhart Prize（2015），他的学术论文多次获得国际顶级会议的最佳论文奖，其著作在谷歌学术中被引用十万余次。Jordan教授是一位享誉全球的卓越教育家，他培养学生多数已在斯坦福、麻省理工、伯克利、普林斯顿、剑桥等全球顶级大学任终身教职，影响着机器学习和统计学的发展。