智能感知与计算系列讲座
Lecture Series in Intelligent Perception and Computing 

题    目（TITLE）：Big Image-Omics Data Analytics for Clinical Outcome Prediction

讲 座 人（SPEAKER）: Associate Professor. Junzhou Huang;  the University of Texas

主 持 人 (CHAIR)：Prof. Ran He

时    间 (TIME)：August 11, 2017 (Friday), 10:30AM

地    点 (VENUE)：Meeting Room (1610), 16 Floor, Intelligent Building

报告摘要（ABSTRACT）：

This talk will introduce how to develop big image-omics data analytics algorithms with GPU computing tools for clinical outcome prediction from pathological images and cell profiling data of cancer patients. Recent technological innovations are enabling scientists to capture image-omics data at increasing speed and resolution, where the image-omics refers to both image data (pathology images or radiology images) and omics data (genomics, proteomics or metabolomics) captured from the same patient. This is generating a deluge of heterogeneous data from different views. Thus, a compelling need exists to develop novel data analytics tools to foster and fuel the next generation of scientific discovery in image-omics data-related research. However, the major computational challenges are due to the unprecedented scale and complexity of heterogeneous image-omics data analytics. There is a critical need for large-scale modeling and mining strategies to bridge the gap and facilitate knowledge discovery from complex image-omics data. This talk will introduce our recent work on developing novel deep learning methods to detect cells in the terapixel histopathological images with 10,000+ speed-up and automatically discovering biomarkers for clinical outcome prediction.

报告人简介（BIOGRAPHY）：

Junzhou Huang is an associate professor in the Computer Science and Engineering department at the University of Texas at Arlington, and an adjunct professor in the department of Clinical Science at the University of Texas Southwestern Medical Center. He received the B.E. degree from Huazhong University of Science and Technology, Wuhan, China, the M.S. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, and the Ph.D. degree in Computer Science at Rutgers, The State University of New Jersey. His major research interests include machine learning, computer vision and big data analytics. He was selected as one of the 10 emerging leaders in multimedia and signal processing by the IBM T.J. Watson Research Center in 2010. His work won the MICCAI Young Scientist Award 2010, the FIMH Best Paper Award 2011, the MICCAI Young Scientist Award Finalist 2011, the STMI Best Paper Award 2012, the NIPS Best Reviewer Award 2013, the MICCAI Best Student Paper Award Finalist 2014 and the MICCAI Best Student Paper Award 2015. He received the NSF CAREER Award in 2016.