Dongyoung Kim, born at 1987.05.26, received his Ph.D. degree in Biomedical Engineering at Texas A&M University in 2016 .
He worked as a researcher in Electrical Engineering and Computer Science at the University of Texas at Dallas for two years after his bachelor’s degree at the University of Texas at Dallas in 2012. He also received a Bachelor of Science degree at Kyungpook National University, Daegu, Republic of Korea.
Dongyoung Kim is currently focusing on developing new artificial intelligence (AI)
modalities for science and engineering applications.
He combined a range of AI technologies including supervised machine learning, deep neural network, generative adversarial network, and unsupervised machine learning to create AI agent performing biomedical research. He was worked on the optimization theory and the image processing in the big data applied for oncology and cell biology.
Dongyoung Kim is currently a research fellow at the Institute for Basic Science, Republic of Korea.
He worked for Texas A&M University, The University of Southwestern medical center, and the University of Texas at Dallas for oncology, antibody engineering, and biomedical optics.
He was a research assistant for the embedded software research center at Kyungpook National University during his bachelor’s degree. He had an internship program for EOS in Australia.
AI-powered transmitted light microscopy uses a range of AI technologies to make:
Generative Adversarial Network
In-slico fluorescence staining using generative adversarial network (GAN).
Deep Neural Network
Identify cancer types from blood driven particles using deep neural networks (variational encoder-decoder conjugated with convolutional neural networks).
Breast cancer prediction from blood driven particles using machine learning algorihtms.
Classifying urinary track infection from the diagnostic kit photograph using convolutioanl neural network.
Finding extreamly rare cancer event from blood using generative adversarial network.
In-slico histology labeling using generative adversarial netowrk.
Recovering high definition medical image from the low resolution using generative adversarial network.
High resolution medical image recovey from a small subset of acqusition using generative adversarial netowrk.