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香港中文大学苏文藻教授学术报告

时间:2020-01-09 21:33      来源:新皇冠体育

报告题目:Some Recent Advances in Non-Convex Optimization for Machine Learning, Signal Processing, and Statistics

人:香港中文大学 苏文藻 教授

报告时间:2020110(周五)16:00

报告地点:清水河校区主楼A1-513

人:肖义彬 教授


报告摘要:

Optimization is now widely reckoned as an indispensable tool in machine learning, signal processing, and statistics. Although convex optimization remains a powerful, and is by far the most extensively used, paradigm in these fields, we have witnessed a shift in interest to non-convex optimization techniques over the last few years. In this talk, we will highlight some recent advances in this exciting area of research and discuss open issues that are motivated by existing or emerging applications.


报告人简介:

Anthony Man-Cho So received his BSE degree in Computer Science from Princeton University with minors in Applied and Computational Mathematics, Engineering and Management Systems, and German Language and Culture. He then received his MSc degree in Computer Science and his PhD degree in Computer Science with a PhD minor in Mathematics from Stanford University. Dr. So joined The Chinese University of Hong Kong (CUHK) in 2007. He currently serves as Assistant Dean of the Faculty of Engineering and is an Associate Professor in the Department of Systems Engineering and Engineering Management. He also holds a courtesy appointment as Associate Professor in the CUHK-BGI Innovation Institute of Trans-omics. His recent research focuses on the interplay between optimization theory and various areas of algorithm design, such as computational geometry, machine learning, signal processing, bioinformatics, and algorithmic game theory.


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