基本信息
浏览量:523
职业迁徙
个人简介
Junghoo Cho is a professor in the Department of Computer Science at University of California, Los Angeles. He received a Ph.D. degree in Computer Science from Stanford University and a B.S. degree in physics from Seoul National University. His research interest is in the theory and practice of learning, particularly in the area of language acquisition and understanding. More specifically, he wants to (1) understand how humans are able to learn a language simply by being immersed into it and (2) develop algorithms that are able to replicate this ability on a machine. He is a recipient of the 10-Year Best-Paper Award at VLDB 2010, NSF CAREER Award, IBM Faculty Award, Okawa Research Award, Northrop Grunmann Excellence in Teaching Award and Dr. Stevenson Faculty-in-Residence Award.
My research is mainly driven by my conviction that a human brain is nothing more than a computational machine made out of neurons. Fundamentally, the role of our brain is to process our sensory input signals and create a succinct and high-level representation of the input, so that our core "self" can use this high-level representation to make the best decision for our survival and other important causes.
Given this conviction, I am seeking to answer the following questions: If our brain is a computer, what algorithms are running inside it? How is our brain able to "learn" and "understand" the world through mere observations without much explicit "teaching"? What are the mathematical principles behind this magical learning capability of our brain? Is it possible to replicate this capability on a computer as concrete algrotithms?
I am trying to answer above questions by formulating "learning" as a mathematical optimization problem. This exploration is guided by investigating and analyzing human language data. Our language is the ultimate "data" produced by our brain, so any theory on our brain's information processing algorithms should be consistent with what we observe in human language.
My research is mainly driven by my conviction that a human brain is nothing more than a computational machine made out of neurons. Fundamentally, the role of our brain is to process our sensory input signals and create a succinct and high-level representation of the input, so that our core "self" can use this high-level representation to make the best decision for our survival and other important causes.
Given this conviction, I am seeking to answer the following questions: If our brain is a computer, what algorithms are running inside it? How is our brain able to "learn" and "understand" the world through mere observations without much explicit "teaching"? What are the mathematical principles behind this magical learning capability of our brain? Is it possible to replicate this capability on a computer as concrete algrotithms?
I am trying to answer above questions by formulating "learning" as a mathematical optimization problem. This exploration is guided by investigating and analyzing human language data. Our language is the ultimate "data" produced by our brain, so any theory on our brain's information processing algorithms should be consistent with what we observe in human language.
研究兴趣
论文共 223 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
arxiv(2022)
引用0浏览0EI引用
0
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn