Advanced Machine Learning and Its Application for Microscopic/Pathology Image Analysis

Speaker:Dr. Lin Yang (University of Florida)

Time and Date: 14:00 - , Mar. 7, 2017

Place: Room 521, Physics Building, Handan Campus

 

 

Abstract

We are living in a revolutionary age, witnessing the next generation of biomedical images emerging in astounding volume and rich formats. This rapidly grown, efficiently delivered, densely connected and incrementally well-defined multiple dimensional multimedia data have fundamentally reshaped the ways researchers can express their thoughts, interact with their colleagues and patients, analyze their data, and lead to ultimately deeper understanding of the nature of biology and diseases. The objective of this talk is to introduce to the boarder community of the current state-of-the-art in microscopic/pathology image analysis and imaging informatics using advanced machine learning technologies such as deep learning. The ultimate goal is to inspire new collaborations among engineers and clinical scientists to tackle the challenges presented in high throughput biomedical image analysis and personalized medicine.

 

 

Biography

Lin Yang is an Associate Professor with the J. Crayton Pruitt Family Department of Biomedical Engineering (BME) in Herbert Wertheim College of Engineering at University of Florida. He is also an official affiliated Associate Professor with the Department of Electrical and Computer Engineering (ECE) and the Department of Computer and Information Science and Engineering (CISE) at University of Florida. He received his B. E. and M. S. from Xian Jiaotong University in 1999 and 2002, and his Ph. D. in Dept. of Electrical and Computer Engineering from Rutgers, the State University of New Jersey in 2009. His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics, and machine learning. He is also working on high performance computing and computed aided health care and information technology using big data. His lab's research is funded by multiple federal funding, including NIH R01. He also serves in NIH study sections and NSF review panels. He is the co-author of the award winning papers for 2008 ISBI NIH Young Investigator Best Paper and Travel Award, 2014 NANETS Young Investigator Paper and Travel Award, 2015 Young Scientist Best Paper Award Runner-up, and 2015 MICCAI Young Scientist Best Paper Award.