[Lecture] Tony Cai : When Statistics Meets Computing: A Few Interesting Problems and Challenges
Time: 9:00-10:30am, Apr. 20th, 2022, Wednesday
Venue: Zoom ID：83788204005, Password:416895
Spreker: Tony Cai , Professor, The Wharton School of the University of Pennsylvania
Abstract:
In the conventional statistical framework, the goal is to develop optimality theory and optimal statistical procedures, where the optimality is understood with respect to the sample size and parameter space. However, in many contemporary applications, nonstatistical concerns such as computational, communication, and privacy constraints associated with the statistical procedures come to the forefront. A fundamental question is: How to make optimal statistical inference under these nonstatistical constraints?
In this talk, we discuss some recent advances on distributed learning under communication constraints, differentially private learning, and interplay between statistical accuracy and computational efficiency in a few specific settings. The results show some interesting and novel phenomena and point to directions that are worthy further investigation.
Speaker’s Bio:
蔡天文 (Tony Cai) 现任美国宾夕法尼亚大学沃顿商学院Daniel H. Silberberg讲席教授及统计与数据科学教授；宾夕法尼亚大学应用数学及计算科学教授；宾夕法尼亚大学医学院生物统计, 流行病学, 及信息学系资深学者。2017-2020年任沃顿商学院副院长. 2006年当选国际数理统计学会会士。2008年获得世界统计学考普斯奖（COPSS Presidents' Award), 2017年当选泛华统计学会(ICSA)主席, 2019年获泛华统计学会杰出成就奖。曾任国际统计学顶尖刊物统计年刊 (Annals of Statistics) 主编，及多个权威学术期刊的编委会成员。
蔡教授的主要研究方向是大数据分析, 包括高维统计、机器学习、大规模统计推断、函数数据分析、统计决策论、非参数函数估计、以及在基因组与金融工程的应用。他自2000年以来，连续8次获得美国国家科学基金会基金，2012年及2017年获得美国国家卫生研究院基金，2016年获得沃顿商学院全球倡议基金。
蔡教授于1996年毕业于康奈尔大学，获得博士学位，师从美国科学院院士劳伦斯.布朗。
