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  4. Learning for High-dimensional Tensor Data
Learning for High-dimensional Tensor Data
作者：
时间：2022-01-11
阅读量：481次
  • 演讲人： Dr. Anru, Zhang（Duke University）
  • 时间：2022年01月20日 周四上午10:00
  • 地点：腾讯会议 ID：654-681-040
  • 主办单位：浙江大学数据科学研究中心，浙江大学数学科学学院

摘要: The analysis of tensor data has become an active research topic in this era of big data. Datasets in the form of tensors, or high-order matrices, arise from a wide range of applications, such as financial econometrics, genomics, and material science. In addition, tensor methods provide unique perspectives and solutions to many high-dimensional problems, such as topic modeling and high-order interaction pursuit, where the observations are not necessarily tensors. High-dimensional tensor problems generally possess distinct characteristics that pose unprecedented challenges to the data science community. There is a clear need to develop new methods, efficient algorithms, and fundamental theory to analyze the high-dimensional tensor data.

 

In this talk, we discuss some recent advances in high-dimensional tensor data analysis through the consideration of several fundamental and interrelated problems, including tensor SVD and tensor regression. We illustrate how we develop new statistically optimal methods and computationally efficient algorithms that exploit useful information from high-dimensional tensor data based on the modern theories of computation, high-dimensional statistics, and non-convex optimization.

 

 

个人简历：张安如，杜克大学生物统计与生物信息系Eugene Anson Stead, Jr. M.D. 长聘冠名副教授，杜克大学计算机系、数学系、统计系副教授。他于2015-2021担任威斯康星大学统计系助理教授，2015年获得宾夕法尼亚大学博士学位（师承蔡天文教授），2010年获得北京大学学士学位。他目前的研究方向主要包括：高维数据分析、非凸优化、统计学习理论、以及在基因组、微生物、计算成像学的应用。他获得了一系列奖项，包括2021美国统计学会诺特奖(ASA Gottfried E. Noether Junior Award)、2021伯努利学会年轻学者奖(Bernoulli Society New Researcher Award)、2021泛华统计协会杰出年轻学者奖(ICSA Outstanding Outstanding Young Researcher Award)、2020年美国自然科学基金委员会职业发展奖(NSF Career Award)。