Research Overview

Research Statement

• Deep Learning (DL) has attracted increasing attentions, showing promising performance in many AI tasks being advanced rapidly. We, the researchers in Machine Learning and Visual Computing (MLVC) Lab, aim to pave the way of creating new DL architectures, algorithms and applications which are much more powerful, efficient and widely applicable. Our efforts will push the boundaries of human knowledge and improve the quality and safety of life through AI technology. The specific research topics in MLVC Lab are as follows:

1. Visual Computing

• 3D reconstriction

• Image restoration(e.g., super-resolution and denoising)

• Image/Video generation

• Image/Video compression

2. Efficient Machine Learning

• Distillation

• Pruning

• Quantization

• Filter decomposition

• Neural architecture search

• Neuromorphic system

• Data augmentation

• Dataset distillation

3. Robustness and explainable AI  

• Adversarial robustness

• Explainable AI

4. AI+X

• AI+Material science

• AI+Space mobility

• AI+Marine mobility

• AI+Astronomy 

• AI+Medical science

Collaborators

(in alphabetical order)

• Prof. Tae-Choong Chung (Kyung Hee University, South Korea)

• Dr. Soo Ye Kim (Adobe Research, USA)

• Prof. Phillip Kim (Harvard University, USA)

• Prof. Jong Hwan Ko (Sungkyunkwan University, South Korea)

• Prof. Sungyong Lee (Kyung Hee University, South Korea)

• Prof. Yong-Jae Moon (Kyung Hee University, South Korea)

• Prof. Tae-Hyun Oh (POSTECH, South Korea)

• Dr. Young Jae Shin (Brookhaven National Lab, USA)

• Prof. Simon Woo (Sungkyunkwan University, South Korea)