基本信息
views: 523
![](https://originalfileserver.aminer.cn/sys/aminer/icon/show-trajectory.png)
Bio
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.
Research Interests
Papers共 237 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation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
CoRR (2022)
semanticscholar(2020)
Cited0Views0Bibtex
0
0
Load More
Author Statistics
#Papers: 227
#Citation: 12952
H-Index: 43
G-Index: 112
Sociability: 6
Diversity: 1
Activity: 0
Co-Author
Co-Institution
D-Core
- 合作者
- 学生
- 导师
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn