King Chung (Johnny) Ho
- UCLA Bioengineering
Office: 924 Westwood Blvd, Suite 420, Room R
Johnny received his BS in Bioengineering (UC Berkeley) in 2011 and MS in Bioengineering (UCLA) in 2013, after which he joined the UCLA medical imaging informatics (MIIs) group for continuing graduate study. Johnny research focuses on machine learning application in clinical data and medical images; he is passionate about the cutting-edge deep learning techniques (CNN, RNN, Autoencoders, etc) and is actively seeking opportunities to apply these techniques in various medical problems. He is working on applying deep learning techniques to identify correlated features across multiple data sources (e.g., demographics, labs, imaging, etc.), and use these features in multimodal framework to better predict stroke patients' outcome, with the hope of detecting cases that need to be treated more or less aggressively.
Before joining the MIIs group, he had been software engineer for several summer internships, working in companies/teams including Amazon.com and BuildUCLA (previous Simul8). In UCLA, Johnny has been actively involving in the committee board of Engineering Graduate Student Association (eGSA) and Chinese Christian Fellowship (CCF).
- Ho K, Speier W, El-Saden S, Arnold C. Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features. AMIA Annu Symp Proc. 2017.
- Ho KC, Scalzo F, Sarma KV, El-Saden S, Arnold CW. A Temporal Deep Learning Approach for MR Perfusion Parameter Estimation in Stroke. 23rd International Conference on Pattern Recognition. Cancun; 2016.
- Sarma KV, Zhong X, Ho KC, Margolis DJA, Raman S, Scalzo F, Sung KH, Tan N, Arnold C. An Investigational Patch-based Convolutional Neural Network Model for the Detection of Clinically Significant Prostate Cancer using Multiparametric MRI. 2016 Annual Meeting of the Radiological Society of North America. Chicago, IL; 2016.
- Ho KC, Scalzo F, Sarma KV, El-Saden S, Bui AAT, Arnold CW. A Novel Bi-Input Convolutional Neural Network for Deconvolution-Free Estimation of Stroke MR Perfusion Parameters. 2016 Annual Meeting of the Radiological Society of North America. Chicago, IL; 2016.
- Sarma KV, Zhong X, Ho KC, Margolis DJA, Raman S, Scalzo F, Sung KH, Tan N, Arnold CW. Development of a Deep Learning Model for the Detection of Prostate Cancer using MRI. Gordon Research Conference in Advanced Health Informatics. Hong Kong; 2016.
- Piedra EAR, Ho KC, Taira RK, El-Saden S, Ellingson B, Bui AAT, Hsu W. Glioblastoma Multiforme Segmentation by Variability Characterization of Tumor Boundaries. Medical Image Computing and Computer Assisted Intervention Society (MICCAI), MICCAI-BRATS Conference Workshop. 2016.
- Ho KC, El-Saden S, Scalzo F, Bui AAT, Arnold CW. Abstract WP41: Predicting Acute Ischemic Stroke Tissue Fate Using Deep Learning on Source Perfusion MRI. Stroke. 2016;47.
- Ho KC, Speier W, El-Saden S, Liebeskind DS, Saver JL, Bui AAT, Arnold CW. Predicting discharge mortality in acute ischemic stroke patients using support vector machines and affinity propagation. MUCMD. 2014.
- Ho KC, Speier W, El-Saden S, Liebeskind DS, Saver JL, Bui AAT, Arnold CW. Predicting discharge mortality after acute ischemic stroke using balanced data. AMIA Annu Symp Proc. 2014. p. 1787–96. PDF