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.