People

We lead the development of key and
fundamental technologies, driving the nation's future
in the era of infinite technological competition.
We lead the development of key and fundamental technologies, driving the nation's future in the era of infinite technological competition.

Professor

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Moongu Jeon

Machine Learning and Vision Lab

062-715-2406
  • 컴퓨터 응용을 위한 알고리즘에 관한 연구
  • 기계학습, 디지털 영상처리, Bio-Informatics 연구

Introduction

  • Machine Learning & Vision Lab.(MLV)The Machine Learning and Vision Lab comprises a highly innovative research program focused on inventing a better future through creative applications of innovative digital technologies.
  • -Machine Learning / Pattern Recognition: Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.
  • The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.
  • -Computer Vision: Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image.[4] This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

Research Field

  • • Machine learning, Data mining, Biomedical Informatics
  • • Biologically inspired computing : GA, PSO, ANN, etc
  • • Optimization : linear and nonlinear programming, derivative free optimization
  • • Image processing : level set methods, wavelets
  • • Scientific computing, High performance and parallel computing

Education

  • 2001 University of Minnesota (Ph.D.- Scientific Computation)
  • 1999 University of Minnesota (M.S.- Computer Science)
  • 1988 Korea University (B.S.-Architectural Engineering)

Professional Career

  • 2013-Present GIST, Professor
  • 2008-2013 GIST, Associate Professor
  • 2005-2008 GIST, Assistant Professor
  • 2003-2005 IBD NRC Canada, Researcher
  • 2001-2003 UCSB, Postgraduate Researcher